{"id":4694,"date":"2026-05-15T15:02:53","date_gmt":"2026-05-15T13:02:53","guid":{"rendered":"https:\/\/datamobility.it\/magazine\/bologna-citta-30-top-or-flop\/"},"modified":"2026-05-15T16:06:46","modified_gmt":"2026-05-15T14:06:46","slug":"bologna-citta-30-top-or-flop","status":"publish","type":"post","link":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/","title":{"rendered":"<strong>Bologna Citt\u00e0 30: top or flop?<\/strong>"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.27.4&#8243; custom_margin=&#8221;0px||||false|false&#8221; custom_padding=&#8221;0px||||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding=&#8221;0px|0px|0px|0px|false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg&#8221; alt=&#8221;perche-compriamo-auto&#8221; title_text=&#8221;bologna-citta-30&#8243; force_fullwidth=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||10px||false|false&#8221; custom_padding=&#8221;||0px||false|false&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; header_3_font_size=&#8221;14px&#8221; custom_margin=&#8221;0px|0px|30px|0px|false|false&#8221; custom_padding=&#8221;0px|0px|0px|0px|false|false&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>GO-Mobility<\/h3>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding=&#8221;0px|0px|0px|0px|false|false&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h1><strong>Bologna Citt\u00e0 30: top or flop?<\/strong><\/h1>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Testo&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding=&#8221;0px|0px|0px|0px|false|false&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3><span>A data-driven analysis<\/span><\/h3>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;row&#8221; _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text content_tablet=&#8221;<\/p>\n<h2 style=%22text-align: justify;%22><strong>Introduction<\/strong><\/h2>\n<p style=%22text-align: justify;%22><strong>Road freight transport<\/strong> constitutes the operational infrastructure on which most domestic trade is based, a complex system on which the <strong>competitiveness<\/strong> of businesses, the efficiency, and the economic attractiveness of the entire country depend. Understanding its dynamics in depth is therefore not a mere academic exercise, but a <strong>strategic necessity<\/strong> to better address the rapid transformations in the world of logistics and freight. Through the analysis of a vast sample of <strong>big data<\/strong> from black boxes installed on board a sample of commercial vehicles, we wanted to shed light on this phenomenon with dedicated research. The study explores the <strong>clear differences<\/strong> both between different load capacities (light commercial and heavy commercial vehicles) and between the specific <strong>regional<\/strong> and urban <strong>dynamics<\/strong>, bringing to light the <strong>structural criticalities<\/strong> of the system and illustrating the new planning <strong>perspectives<\/strong> that are emerging to govern freight mobility in the future. How? Always with a data-driven approach, of course.     <\/p>\n<h2 style=%22text-align: justify;%22><strong>Where we started<\/strong><\/h2>\n<p style=%22text-align: justify;%22>Our study was born to provide some insights on the mobility behaviors of <strong>commercial vehicles<\/strong> and their movement dynamics along the national road network. These analyses aim to reconstruct the relationships between the %22intermediate%22 stages of the logistics chain and to read the phenomenon with <strong>greater detail<\/strong>. The objective is therefore to assess <strong>impacts and externalities<\/strong> on the mobility system and provide technicians and stakeholders with methods and interpretive keys useful for planning and making <strong>informed decisions<\/strong>.  <\/p>\n<p style=%22text-align: justify;%22>The analysis is based on data from October 2024, provided by the provider <a href=%22https:\/\/targatelematics.com\/it-it\/%22>Targa Telematics-Viasat<\/a>: a set of %22first generation%22 <strong>big data<\/strong>, that is, information collected for operational purposes other than mobility analysis, which, however, if properly processed, can yield evidence of considerable interest, as we intend to demonstrate in this article.<\/p>\n<p style=%22text-align: justify;%22>The data used for the analysis has some important peculiarities: first, it considers only <strong>one segment<\/strong> of the freight chain (which goes from a production point to a distribution or consumption point, as observable in the diagram below) describing exclusively <strong>road transport<\/strong> related to <strong>national carriers<\/strong>, thus excluding, for example, vehicles crossing the Brenner Pass or coming from Eastern Europe and cabotage (domestic transport within national borders managed by foreign carriers). Furthermore, it does not provide %22load%22 information on circulation: we do not know whether vehicles are traveling <strong>full or empty<\/strong>, nor whether they are in the loading, unloading, or both phases. <\/p>\n<h6 style=%22text-align: center;%22><a href=%22https:\/\/datamobility.it\/wp-content\/uploads\/segmenti-analizzati-veicoli-commerciali.svg%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/segmenti-analizzati-veicoli-commerciali.svg%22 width=%22922%22 height=%22475%22 alt=%22%22 class=%22wp-image-4172 aligncenter size-full%22><\/a>Representation of the logistics chain highlighting the segments analyzed in this study<\/h6>\n<\/p>\n<p>On the methodological level, the approach is based on <strong>established metrics<\/strong> in dedicated studies, such as the <a href=%22https:\/\/www.go-mobility.it\/pianificazione-trasporti-mobilita\/studio-reif-analisi-intermodalita-merci-in-emilia%e2%80%91romagna\/%22>assessment of the freight model of the Emilia-Romagna Region<\/a>, which made it possible to identify the intermediate points of the logistics chain based on three parameters:<\/p>\n<ul style=%22text-align: justify;%22>\n<li><strong>stop<\/strong> duration<\/li>\n<li><strong>land use<\/strong> characteristics<\/li>\n<li>vehicle <strong>capacity<\/strong>.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22>Data on <strong>service areas<\/strong> were retrieved and updated through official mappings, appropriately integrated where necessary. In this case, trips are automatically aggregated into a single movement, defining origins and destinations between these intermediate points through a <strong>data-driven procedure<\/strong> based on temporal and spatial parameters. The monitored sample was expanded to the universe based on penetration rates calculated at the metropolitan city level for light commercial vehicles, and at the regional level for heavy vehicles.  <\/p>\n<h2 style=%22text-align: justify;%22><strong>Two %22species%22 compared: light and heavy vehicles<\/strong><\/h2>\n<p style=%22text-align: justify;%22>One of the first points of the research was to understand whether different behaviors emerged between <strong>light vehicles<\/strong> (gross vehicle weight < 3.5 t) and <strong>heavy vehicles<\/strong> (gross vehicle weight > 3.5 t)<\/p>\n<p style=%22text-align: justify;%22>In the logistics field, light and heavy vehicles represent, or should represent, two <strong>distinct operational worlds<\/strong>, with profoundly different logistics functions, areas of influence, and territorial impacts. This differentiation is the first step to correctly decode the data and interpret the movement patterns that draw the country&#8217;s economic map. <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/veicoli-leggeri-pesanti.jpg%22 width=%221824%22 height=%22884%22 alt=%22%22 class=%22wp-image-4180 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>The analysis of trajectories at the national level shows how <strong>light vehicles<\/strong> have a clear tendency to orbit around the <strong>major metropolitan areas<\/strong>. As visible in the map below, in fact, <strong>42.6%<\/strong> of the total mileage of this category is concentrated within <strong>metropolitan cities<\/strong>, which represent only 17% of the national surface. Their activity is intrinsically linked to <strong>capillary distribution<\/strong> and last-mile logistics, essential functions for serving the dense and fragmented fabric of urban centers and their immediate suburbs.  <\/p>\n<p style=%22text-align: justify;%22>On the contrary, <strong>heavy vehicles<\/strong> concentrate their movements in the economic heartland of the country, namely the %22new industrial triangle%22 of the Po Valley that unites <strong>Lombardy, Veneto, and Emilia-Romagna<\/strong>. These vehicles are the protagonists of <strong>medium and long-distance<\/strong> flows that connect the major production hubs, distribution centers, and intermodal hubs, tracing the main routes of national trade. <\/p>\n<p style=%22text-align: justify;%22>A peculiarity of the <strong>islands<\/strong>, however, is that this behavioral difference between light and heavy vehicles is not so evident, evidence suggesting less specialization of functions.<\/p>\n<p style=%22text-align: justify;%22>This strong differentiation led to dividing the analysis into <strong>two distinct territorializations<\/strong>:<\/p>\n<ul style=%22text-align: justify;%22>\n<li><strong>metropolitan cities<\/strong>, which absorb the most light vehicle traffic;<\/li>\n<li><strong>regions<\/strong> and the system of major hubs, namely <strong>interports<\/strong>.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/differenti-elementi-territoriali-analisi.svg%22 width=%22925%22 height=%22519%22 alt=%22%22 class=%22wp-image-4179 aligncenter size-full%22><\/p>\n<h6 style=%22text-align: center;%22>Map representation of the behaviors of the two vehicle categories: on the left light vehicles <br \/>(concentration in metropolitan cities) and on the right heavy vehicles (concentration in the Po Valley hub)<\/h6>\n<h2 style=%22text-align: justify;%22><strong><\/strong><\/h2>\n<h2 style=%22text-align: justify;%22><strong>The dominance of light vehicles in urban areas<\/strong><\/h2>\n<p style=%22text-align: justify;%22>Light vehicles represent the key to <strong>last-mile logistics<\/strong>, a link in the distribution chain as essential as it is critical for the <strong>livability<\/strong> of our cities. Analyzing their behavior within metropolitan areas is fundamental for planning mobility, reconciling economic needs with sustainability and the fluidity of vehicular traffic in urban centers. <\/p>\n<p style=%22text-align: justify;%22>The most interesting numbers come precisely from this category:<\/p>\n<ul style=%22text-align: justify;%22>\n<li>Almost <strong>40%<\/strong> of kilometers traveled in metropolitan cities occur <strong>within the boundaries of urban centers<\/strong>, data that highlights strong interaction with local traffic;<\/li>\n<li><strong>Loading\/unloading<\/strong> operations occupy <strong>over a third<\/strong> of the total delivery round time, a factor that significantly impacts the efficiency of the logistics chain and urban congestion, considering the widespread undersizing of specific facilities dedicated to loading\/unloading activities.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/macronumeri-veicoli-commerciali-leggeri.svg%22 width=%22944%22 height=%22529%22 alt=%22%22 class=%22wp-image-4178 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>Going into the detail of territorial specificities, heterogeneous operational models emerge. For example, the contrast between <strong>Venice<\/strong>, the metropolitan city that makes the fewest stops for loading\/unloading, and <strong>Milan<\/strong> which, for the same distance traveled, is instead dominated by short delivery rounds and many intermediate stops. Other dynamics emerge elsewhere: <strong>Bari, Palermo, and Rome<\/strong> record greater average trip lengths, while <strong>Florence<\/strong> stands out for the longer duration of each individual trip.  <\/p>\n<p style=%22text-align: justify;%22>In terms of temporal trends, in some cities, for example in Milan and Rome, there is a high incidence of movements during the <strong>morning rush hour<\/strong>, while in other cities demand is more evenly distributed throughout the day, with the usual three peaks also comparable to private mobility demand<\/p>\n<h2 style=%22text-align: justify;%22><strong>Heavy vehicles and interports: a system with untapped potential<\/strong><\/h2>\n<p style=%22text-align: justify;%22>If we shift our gaze to the regional and national scale, the protagonists become <strong>heavy vehicles<\/strong>. The analysis of these vehicles provides a valuable indicator of the health, structure, and sustainability of medium and long-distance logistics flows. For this reason, it is particularly important to assess the functionality of <strong>interports<\/strong>, conceived as strategic nodes for modal integration and network efficiency.  <\/p>\n<p style=%22text-align: justify;%22>The data show a system still strongly short-distance: almost <strong>three trips out of four<\/strong> remain within <strong>regional borders<\/strong>. In this picture, the weight of the northern regions is preponderant: <strong>Lombardy, Veneto, and Emilia-Romagna<\/strong> generate or attract <strong>almost half<\/strong> of total movements at the national level, although a very strong exchange quota also persists between <strong>Lombardy and Piedmont<\/strong>. Lombardy alone is the origin or destination of almost <strong>one-fifth<\/strong> of all monitored movements, as well as the main reference for <strong>international exchanges<\/strong> (22%), confirming itself as the country&#8217;s logistics center.  <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/pesanti-veicoli-commerciali-leggeri.svg%22 width=%22926%22 height=%22519%22 alt=%22%22 class=%22wp-image-4177 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>The study therefore focused on the interaction between these flows and strategic infrastructures such as <strong>interports<\/strong>, observing how many of the analyzed vehicles intercept these structures in their trips during the observation month.<\/p>\n<p style=%22text-align: justify;%22>What emerges?<\/p>\n<ul style=%22text-align: justify;%22>\n<li>Only <strong>10%<\/strong> of the heavy vehicle sample intercepted at least one interport during the observation month.<\/li>\n<li>The share drops to <strong>3%<\/strong> if only trips that actually start or end within an interport area are considered.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22>What stands out when observing the data is that the <strong>underutilization<\/strong> of interports does not derive from infrastructural inefficiency. The analysis shows, in fact, that the <strong>average travel times<\/strong> for those who use them are the same as those who do not frequent them, even though they cover longer distances, precisely thanks to the better network access that these structures guarantee. <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/commerciali-pesanti-interporti.svg%22 width=%22918%22 height=%22514%22 alt=%22%22 class=%22wp-image-4176 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>The fact that a single hub like <strong>Bologna<\/strong> handles almost 40% of all interregional road traffic through interports, while that of <strong>Mortara<\/strong> stands out for having the most extensive catchment area (350 km), only underscores the heterogeneity and imbalance of the system and the fact that these infrastructures fail to act systemically, but as separate and specialized entities.<\/p>\n<p style=%22text-align: justify;%22><strong>Verona<\/strong> instead has the smallest catchment area, probably due to high intermodality, as well as the high density of production activities located adjacent to the interport.<\/p>\n<p style=%22text-align: justify;%22>Focusing on flows originating from or destined to an interport, the shares of <strong>impact on urban centers<\/strong> are highly variable: ranging from 12% for the <strong>Pescara<\/strong> interport to 36% for the <strong>Vado<\/strong> interport, which has many exchanges with the port of Genoa in a seamless urban context. Road exchange occurs mainly between the northeastern interports, primarily between <strong>Bologna and Padua.<\/strong> <\/p>\n<p style=%22text-align: justify;%22><strong><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/origine-destinazione-veicoli-commerciali-pesanti.svg%22 width=%22922%22 height=%22517%22 alt=%22%22 class=%22wp-image-4175 aligncenter size-full%22><\/strong><\/p>\n<h2 style=%22text-align: justify;%22><strong>Diagnosis of a system: fragmentation and lack of planning<\/strong><\/h2>\n<p style=%22text-align: justify;%22>The numbers emerging from the analysis paint the picture of a <strong>fragmented logistics system<\/strong> afflicted by evident <strong>structural dysfunctions,<\/strong> which makes a specialization of logistics functions across the territory desirable. The absence of structured planning, in fact, has meant that strategic infrastructures, such as interports, have been built but fail to intercept real demand, causing <strong>logistics dispersion<\/strong> across the territory. This void has favored the disorganized proliferation of private networks, located based on proprietary logic rather than a systemic vision of the territory guided by well-defined governance. As a result, the indiscriminate growth of logistics settlements, at the expense of structured planning, has caused <strong>anomalous and dysfunctional development<\/strong> of infrastructures.   <\/p>\n<p style=%22text-align: justify;%22><em>%22Logistics is the most demand-driven sector that exists: there is no freight that does not move out of necessity, unlike passengers%22:<\/em> the system must be able to combine the request for enormous flexibility with the low elasticity of demand, and sometimes also of infrastructures, to be shared with passenger transport, which makes it a highly <strong>constrained<\/strong> system.<\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/di-antonio-data-mobility.jpg%22 width=%22996%22 height=%22626%22 alt=%22%22 class=%22wp-image-4174 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>Faced with this diagnosis, the response can only lie in a <strong>paradigm shift<\/strong>. In some high-density areas, for example, it might be convenient to use <strong>other types of vehicles<\/strong> for deliveries, especially considering the high weight of stop time and the inconvenience they cause in urban areas to vehicles and people, also due to the absence of dedicated parking areas. <\/p>\n<h2 style=%22text-align: justify;%22><strong>Toward integrated logistics: new tools for planning<\/strong><\/h2>\n<p style=%22text-align: justify;%22>The gradual awareness of the criticalities can generate a paradigm shift: an example comes from the Lombardy Region, which approved the <strong>first law in Italy for the coordinated planning of logistics infrastructures<\/strong> (LR 15 of 8\/8\/2024 %22<a href=%22https:\/\/normelombardia.consiglio.regione.lombardia.it\/normelombardia\/accessibile\/main.aspx?view=showdoc&#038;iddoc=lr002024080800015%22>Regulation of logistics settlements of supra-municipal relevance<\/a>%22). This legislation aims to bring development governance to a supra-municipal level, ensuring that the location of new settlements is consistent with a broader territorial strategy and not dictated only by local interests. <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/logistica-integrata.jpg%22 width=%221832%22 height=%22994%22 alt=%22%22 class=%22wp-image-4181 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>Also in Lombardy, other strategic initiatives are being developed to strengthen this new approach, such as the update of the PRMT (Regional Mobility and Transport Plan), <a href=%22https:\/\/www.go-mobility.it\/pianificazione-trasporti-mobilita\/monitoraggio-prmt-prmc-lombardia\/%22>on which GO-Mobility also worked<\/a>, which for the first time will include a <strong>specific section<\/strong> dedicated to freight transport, formally recognizing its strategic role.<\/p>\n<p style=%22text-align: justify;%22>A change at the legislative and planning level represents the first important step toward the goal of an increasingly integrated mobility system. Freight and passengers often share the same infrastructure: only a holistic approach, which considers both components of mobility and is based on in-depth data analysis, can lead to effective, efficient, and sustainable solutions. <\/p>\n<p style=%22text-align: justify;%22><em>The full illustration of this study, including the description of sample representativeness and further methodological details, is available in the <strong>reserved section of the site<\/strong> dedicated to videos of all the main presentations from the Data Mobility Summit 2025: <a href=%22https:\/\/datamobility.it\/contenuti-esclusivi\/%22>to access click on this link.<\/a><\/em><\/p>\n<p style=%22text-align: justify;%22>\n<p style=%22text-align: justify;%22>\n<p>&#8221; content_phone=&#8221;<\/p>\n<h2 style=%22text-align: justify;%22><strong>Introduction<\/strong><\/h2>\n<p style=%22text-align: justify;%22><strong>Road freight transport<\/strong> constitutes the operational infrastructure on which most domestic trade is based, a complex system on which the <strong>competitiveness<\/strong> of businesses, the efficiency, and the economic attractiveness of the entire country depend. Understanding its dynamics in depth is therefore not a mere academic exercise, but a <strong>strategic necessity<\/strong> to better address the rapid transformations in the world of logistics and freight. Through the analysis of a vast sample of <strong>big data<\/strong> from black boxes installed on board a sample of commercial vehicles, we wanted to shed light on this phenomenon with dedicated research. The study explores the <strong>clear differences<\/strong> both between different load capacities (light commercial and heavy commercial vehicles) and between the specific <strong>regional<\/strong> and urban <strong>dynamics<\/strong>, bringing to light the <strong>structural criticalities<\/strong> of the system and illustrating the new planning <strong>perspectives<\/strong> that are emerging to govern freight mobility in the future. How? Always with a data-driven approach, of course.     <\/p>\n<h2 style=%22text-align: justify;%22><strong>Where we started<\/strong><\/h2>\n<p style=%22text-align: justify;%22>Our study was born to provide some insights on the mobility behaviors of <strong>commercial vehicles<\/strong> and their movement dynamics along the national road network. These analyses aim to reconstruct the relationships between the %22intermediate%22 stages of the logistics chain and to read the phenomenon with <strong>greater detail<\/strong>. The objective is therefore to assess <strong>impacts and externalities<\/strong> on the mobility system and provide technicians and stakeholders with methods and interpretive keys useful for planning and making <strong>informed decisions<\/strong>.  <\/p>\n<p style=%22text-align: justify;%22>The analysis is based on data from October 2024, provided by the provider <a href=%22https:\/\/targatelematics.com\/it-it\/%22>Targa Telematics-Viasat<\/a>: a set of %22first generation%22 <strong>big data<\/strong>, that is, information collected for operational purposes other than mobility analysis, which, however, if properly processed, can yield evidence of considerable interest, as we intend to demonstrate in this article.<\/p>\n<p style=%22text-align: justify;%22>The data used for the analysis has some important peculiarities: first, it considers only <strong>one segment<\/strong> of the freight chain (which goes from a production point to a distribution or consumption point, as observable in the diagram below) describing exclusively <strong>road transport<\/strong> related to <strong>national carriers<\/strong>, thus excluding, for example, vehicles crossing the Brenner Pass or coming from Eastern Europe and cabotage (domestic transport within national borders managed by foreign carriers). Furthermore, it does not provide %22load%22 information on circulation: we do not know whether vehicles are traveling <strong>full or empty<\/strong>, nor whether they are in the loading, unloading, or both phases. <\/p>\n<h6 style=%22text-align: center;%22><a href=%22https:\/\/datamobility.it\/wp-content\/uploads\/segmenti-analizzati-veicoli-commerciali.svg%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/segmenti-analizzati-veicoli-commerciali.svg%22 width=%22922%22 height=%22475%22 alt=%22%22 class=%22wp-image-4172 aligncenter size-full%22><\/a>Representation of the logistics chain highlighting the segments analyzed in this study<\/h6>\n<\/p>\n<p>On the methodological level, the approach is based on <strong>established metrics<\/strong> in dedicated studies, such as the <a href=%22https:\/\/www.go-mobility.it\/pianificazione-trasporti-mobilita\/studio-reif-analisi-intermodalita-merci-in-emilia%e2%80%91romagna\/%22>assessment of the freight model of the Emilia-Romagna Region<\/a>, which made it possible to identify the intermediate points of the logistics chain based on three parameters:<\/p>\n<ul style=%22text-align: justify;%22>\n<li><strong>stop<\/strong> duration<\/li>\n<li><strong>land use<\/strong> characteristics<\/li>\n<li>vehicle <strong>capacity<\/strong>.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22>Data on <strong>service areas<\/strong> were retrieved and updated through official mappings, appropriately integrated where necessary. In this case, trips are automatically aggregated into a single movement, defining origins and destinations between these intermediate points through a <strong>data-driven procedure<\/strong> based on temporal and spatial parameters. The monitored sample was expanded to the universe based on penetration rates calculated at the metropolitan city level for light commercial vehicles, and at the regional level for heavy vehicles.  <\/p>\n<h2 style=%22text-align: justify;%22><strong>Two %22species%22 compared: light and heavy vehicles<\/strong><\/h2>\n<p style=%22text-align: justify;%22>One of the first points of the research was to understand whether different behaviors emerged between <strong>light vehicles<\/strong> (gross vehicle weight < 3.5 t) and <strong>heavy vehicles<\/strong> (gross vehicle weight > 3.5 t)<\/p>\n<p style=%22text-align: justify;%22>In the logistics field, light and heavy vehicles represent, or should represent, two <strong>distinct operational worlds<\/strong>, with profoundly different logistics functions, areas of influence, and territorial impacts. This differentiation is the first step to correctly decode the data and interpret the movement patterns that draw the country&#8217;s economic map. <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/veicoli-leggeri-pesanti.jpg%22 width=%221824%22 height=%22884%22 alt=%22%22 class=%22wp-image-4180 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>The analysis of trajectories at the national level shows how <strong>light vehicles<\/strong> have a clear tendency to orbit around the <strong>major metropolitan areas<\/strong>. As visible in the map below, in fact, <strong>42.6%<\/strong> of the total mileage of this category is concentrated within <strong>metropolitan cities<\/strong>, which represent only 17% of the national surface. Their activity is intrinsically linked to <strong>capillary distribution<\/strong> and last-mile logistics, essential functions for serving the dense and fragmented fabric of urban centers and their immediate suburbs.  <\/p>\n<p style=%22text-align: justify;%22>On the contrary, <strong>heavy vehicles<\/strong> concentrate their movements in the economic heartland of the country, namely the %22new industrial triangle%22 of the Po Valley that unites <strong>Lombardy, Veneto, and Emilia-Romagna<\/strong>. These vehicles are the protagonists of <strong>medium and long-distance<\/strong> flows that connect the major production hubs, distribution centers, and intermodal hubs, tracing the main routes of national trade. <\/p>\n<p style=%22text-align: justify;%22>A peculiarity of the <strong>islands<\/strong>, however, is that this behavioral difference between light and heavy vehicles is not so evident, evidence suggesting less specialization of functions.<\/p>\n<p style=%22text-align: justify;%22>This strong differentiation led to dividing the analysis into <strong>two distinct territorializations<\/strong>:<\/p>\n<ul style=%22text-align: justify;%22>\n<li><strong>metropolitan cities<\/strong>, which absorb the most light vehicle traffic;<\/li>\n<li><strong>regions<\/strong> and the system of major hubs, namely <strong>interports<\/strong>.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/differenti-elementi-territoriali-analisi.svg%22 width=%22925%22 height=%22519%22 alt=%22%22 class=%22wp-image-4179 aligncenter size-full%22><\/p>\n<h6 style=%22text-align: center;%22>Map representation of the behaviors of the two vehicle categories: on the left light vehicles <br \/>(concentration in metropolitan cities) and on the right heavy vehicles (concentration in the Po Valley hub)<\/h6>\n<h2 style=%22text-align: justify;%22><strong><\/strong><\/h2>\n<h2 style=%22text-align: justify;%22><strong>The dominance of light vehicles in urban areas<\/strong><\/h2>\n<p style=%22text-align: justify;%22>Light vehicles represent the key to <strong>last-mile logistics<\/strong>, a link in the distribution chain as essential as it is critical for the <strong>livability<\/strong> of our cities. Analyzing their behavior within metropolitan areas is fundamental for planning mobility, reconciling economic needs with sustainability and the fluidity of vehicular traffic in urban centers. <\/p>\n<p style=%22text-align: justify;%22>The most interesting numbers come precisely from this category:<\/p>\n<ul style=%22text-align: justify;%22>\n<li>Almost <strong>40%<\/strong> of kilometers traveled in metropolitan cities occur <strong>within the boundaries of urban centers<\/strong>, data that highlights strong interaction with local traffic;<\/li>\n<li><strong>Loading\/unloading<\/strong> operations occupy <strong>over a third<\/strong> of the total delivery round time, a factor that significantly impacts the efficiency of the logistics chain and urban congestion, considering the widespread undersizing of specific facilities dedicated to loading\/unloading activities.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/macronumeri-veicoli-commerciali-leggeri.svg%22 width=%22944%22 height=%22529%22 alt=%22%22 class=%22wp-image-4178 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>Going into the detail of territorial specificities, heterogeneous operational models emerge. For example, the contrast between <strong>Venice<\/strong>, the metropolitan city that makes the fewest stops for loading\/unloading, and <strong>Milan<\/strong> which, for the same distance traveled, is instead dominated by short delivery rounds and many intermediate stops. Other dynamics emerge elsewhere: <strong>Bari, Palermo, and Rome<\/strong> record greater average trip lengths, while <strong>Florence<\/strong> stands out for the longer duration of each individual trip.  <\/p>\n<p style=%22text-align: justify;%22>In terms of temporal trends, in some cities, for example in Milan and Rome, there is a high incidence of movements during the <strong>morning rush hour<\/strong>, while in other cities demand is more evenly distributed throughout the day, with the usual three peaks also comparable to private mobility demand<\/p>\n<h2 style=%22text-align: justify;%22><strong>Heavy vehicles and interports: a system with untapped potential<\/strong><\/h2>\n<p style=%22text-align: justify;%22>If we shift our gaze to the regional and national scale, the protagonists become <strong>heavy vehicles<\/strong>. The analysis of these vehicles provides a valuable indicator of the health, structure, and sustainability of medium and long-distance logistics flows. For this reason, it is particularly important to assess the functionality of <strong>interports<\/strong>, conceived as strategic nodes for modal integration and network efficiency.  <\/p>\n<p style=%22text-align: justify;%22>The data show a system still strongly short-distance: almost <strong>three trips out of four<\/strong> remain within <strong>regional borders<\/strong>. In this picture, the weight of the northern regions is preponderant: <strong>Lombardy, Veneto, and Emilia-Romagna<\/strong> generate or attract <strong>almost half<\/strong> of total movements at the national level, although a very strong exchange quota also persists between <strong>Lombardy and Piedmont<\/strong>. Lombardy alone is the origin or destination of almost <strong>one-fifth<\/strong> of all monitored movements, as well as the main reference for <strong>international exchanges<\/strong> (22%), confirming itself as the country&#8217;s logistics center.  <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/pesanti-veicoli-commerciali-leggeri.svg%22 width=%22926%22 height=%22519%22 alt=%22%22 class=%22wp-image-4177 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>The study therefore focused on the interaction between these flows and strategic infrastructures such as <strong>interports<\/strong>, observing how many of the analyzed vehicles intercept these structures in their trips during the observation month.<\/p>\n<p style=%22text-align: justify;%22>What emerges?<\/p>\n<ul style=%22text-align: justify;%22>\n<li>Only <strong>10%<\/strong> of the heavy vehicle sample intercepted at least one interport during the observation month.<\/li>\n<li>The share drops to <strong>3%<\/strong> if only trips that actually start or end within an interport area are considered.<\/li>\n<\/ul>\n<p style=%22text-align: justify;%22>What stands out when observing the data is that the <strong>underutilization<\/strong> of interports does not derive from infrastructural inefficiency. The analysis shows, in fact, that the <strong>average travel times<\/strong> for those who use them are the same as those who do not frequent them, even though they cover longer distances, precisely thanks to the better network access that these structures guarantee. <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/commerciali-pesanti-interporti.svg%22 width=%22918%22 height=%22514%22 alt=%22%22 class=%22wp-image-4176 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>The fact that a single hub like <strong>Bologna<\/strong> handles almost 40% of all interregional road traffic through interports, while that of <strong>Mortara<\/strong> stands out for having the most extensive catchment area (350 km), only underscores the heterogeneity and imbalance of the system and the fact that these infrastructures fail to act systemically, but as separate and specialized entities.<\/p>\n<p style=%22text-align: justify;%22><strong>Verona<\/strong> instead has the smallest catchment area, probably due to high intermodality, as well as the high density of production activities located adjacent to the interport.<\/p>\n<p style=%22text-align: justify;%22>Focusing on flows originating from or destined to an interport, the shares of <strong>impact on urban centers<\/strong> are highly variable: ranging from 12% for the <strong>Pescara<\/strong> interport to 36% for the <strong>Vado<\/strong> interport, which has many exchanges with the port of Genoa in a seamless urban context. Road exchange occurs mainly between the northeastern interports, primarily between <strong>Bologna and Padua.<\/strong> <\/p>\n<p style=%22text-align: justify;%22><strong><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/origine-destinazione-veicoli-commerciali-pesanti.svg%22 width=%22922%22 height=%22517%22 alt=%22%22 class=%22wp-image-4175 aligncenter size-full%22><\/strong><\/p>\n<h2 style=%22text-align: justify;%22><strong>Diagnosis of a system: fragmentation and lack of planning<\/strong><\/h2>\n<p style=%22text-align: justify;%22>The numbers emerging from the analysis paint the picture of a <strong>fragmented logistics system<\/strong> afflicted by evident <strong>structural dysfunctions,<\/strong> which makes a specialization of logistics functions across the territory desirable. The absence of structured planning, in fact, has meant that strategic infrastructures, such as interports, have been built but fail to intercept real demand, causing <strong>logistics dispersion<\/strong> across the territory. This void has favored the disorganized proliferation of private networks, located based on proprietary logic rather than a systemic vision of the territory guided by well-defined governance. As a result, the indiscriminate growth of logistics settlements, at the expense of structured planning, has caused <strong>anomalous and dysfunctional development<\/strong> of infrastructures.   <\/p>\n<p style=%22text-align: justify;%22><em>%22Logistics is the most demand-driven sector that exists: there is no freight that does not move out of necessity, unlike passengers%22:<\/em> the system must be able to combine the request for enormous flexibility with the low elasticity of demand, and sometimes also of infrastructures, to be shared with passenger transport, which makes it a highly <strong>constrained<\/strong> system.<\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/di-antonio-data-mobility.jpg%22 width=%22996%22 height=%22626%22 alt=%22%22 class=%22wp-image-4174 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>Faced with this diagnosis, the response can only lie in a <strong>paradigm shift<\/strong>. In some high-density areas, for example, it might be convenient to use <strong>other types of vehicles<\/strong> for deliveries, especially considering the high weight of stop time and the inconvenience they cause in urban areas to vehicles and people, also due to the absence of dedicated parking areas. <\/p>\n<h2 style=%22text-align: justify;%22><strong>Toward integrated logistics: new tools for planning<\/strong><\/h2>\n<p style=%22text-align: justify;%22>The gradual awareness of the criticalities can generate a paradigm shift: an example comes from the Lombardy Region, which approved the <strong>first law in Italy for the coordinated planning of logistics infrastructures<\/strong> (LR 15 of 8\/8\/2024 %22<a href=%22https:\/\/normelombardia.consiglio.regione.lombardia.it\/normelombardia\/accessibile\/main.aspx?view=showdoc&#038;iddoc=lr002024080800015%22>Regulation of logistics settlements of supra-municipal relevance<\/a>%22). This legislation aims to bring development governance to a supra-municipal level, ensuring that the location of new settlements is consistent with a broader territorial strategy and not dictated only by local interests. <\/p>\n<p style=%22text-align: justify;%22><img src=%22https:\/\/datamobility.it\/wp-content\/uploads\/logistica-integrata.jpg%22 width=%221832%22 height=%22994%22 alt=%22%22 class=%22wp-image-4181 aligncenter size-full%22><\/p>\n<p style=%22text-align: justify;%22>Also in Lombardy, other strategic initiatives are being developed to strengthen this new approach, such as the update of the PRMT (Regional Mobility and Transport Plan), <a href=%22https:\/\/www.go-mobility.it\/pianificazione-trasporti-mobilita\/monitoraggio-prmt-prmc-lombardia\/%22>on which GO-Mobility also worked<\/a>, which for the first time will include a <strong>specific section<\/strong> dedicated to freight transport, formally recognizing its strategic role.<\/p>\n<p style=%22text-align: justify;%22>A change at the legislative and planning level represents the first important step toward the goal of an increasingly integrated mobility system. Freight and passengers often share the same infrastructure: only a holistic approach, which considers both components of mobility and is based on in-depth data analysis, can lead to effective, efficient, and sustainable solutions. <\/p>\n<p style=%22text-align: justify;%22><em>The full illustration of this study, including the description of sample representativeness and further methodological details, is available in the <strong>reserved section of the site<\/strong> dedicated to videos of all the main presentations from the Data Mobility Summit 2025: <a href=%22https:\/\/datamobility.it\/contenuti-esclusivi\/%22>to access click on this link.<\/a><\/em><\/p>\n<p style=%22text-align: justify;%22>\n<p style=%22text-align: justify;%22>\n<p>&#8221; content_last_edited=&#8221;on|desktop&#8221; admin_label=&#8221;Text&#8221; _builder_version=&#8221;4.27.4&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p style=\"text-align: justify;\">It was July 2023 when the City of Bologna announced the introduction of \u2018Citt\u00e0 30\u2019, extending the 30 km\/h speed limit to around 70% of urban roads and becoming the first major Italian city to adopt a measure of this scale. The debate over the measure\u2019s effectiveness soon spread from the porticoes of the Emilian capital to the rest of the country, based mainly on first impressions and the limited data available. <\/p>\n<p style=\"text-align: justify;\">But what do the figures say about the impact of \u2018Citt\u00e0 30\u2019 on Bologna? Two and a half years after the project was launched, and following the conclusion of the <a href=\"https:\/\/www.ilpost.it\/2026\/03\/23\/bologna-nuovo-piano-citta-30\/\">administrative proceedings<\/a> surrounding the scheme, we are now able to provide more definitive answers to what have been (and still are) the most frequently asked questions: how much extra time is lost in traffic? Is Bologna a more polluted city? Are its roads really safer? In collaboration with the Municipality of Bologna and the <a href=\"https:\/\/fondazioneiu.it\/\">IU Rusconi Ghigi Foundation<\/a>, and thanks to big data from car black boxes, we have analysed over 4 million journeys within the urban area of Bologna and have compiled the answers to these various questions.    <\/p>\n<h2 style=\"text-align: justify;\"><strong>The different phases of Bologna Citt\u00e0 30<\/strong><\/h2>\n<p style=\"text-align: justify;\">The history of the measure is not straightforward; on the contrary, since its inception it has undergone various stages and changes, resulting in it being implemented in several phases.<\/p>\n<ul style=\"text-align: justify;\">\n<li>In July 2023, the 30 km\/h speed limit will be extended to the relevant roads through the installation of road signs alone. The measure has therefore been announced, but there are not yet any systematic checks in place. <\/li>\n<li>From January 2024, the traffic regulations will come into force and enforcement checks will begin. To coincide with the launch of the measure, <a href=\"https:\/\/www.comune.bologna.it\/novita\/notizie\/citta30-dati-primo-anno\">from 16 January to 31 December 2024<\/a>, the local police carried out 166 days of checks with road patrols (approximately one every two days), totalling 322 shifts, stopping and checking 193 vehicles and issuing 2,046 fines, of which 306 were for exceeding the speed limit. <a href=\"https:\/\/bolognacitta30.it\/faq\/che-tipo-di-controlli-vengono-fatti-sulle-strade-a-30-km-h-e-con-quali-strumenti\/\">Checks are carried out<\/a> by patrols (around 6 per day) and laser speed guns in all districts of the city, particularly on roads affected by the change from 50 to 30 km\/h<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a>.  <\/li>\n<li>On 20 January 2026, the Regional Administrative Court (TAR) of Emilia-Romagna<a href=\"https:\/\/www.ansa.it\/canale_motori\/notizie\/istituzioni\/2026\/01\/20\/il-tar-annulla-il-provvedimento-di-bologna-citta-30_6841f06a-3d34-4f7e-86a7-9ca8fff19f43.html\"> upheld the appeal <\/a>lodged by two taxi drivers and quashed the decision.<\/li>\n<li>On 20 April 2026, following the approval by the Bologna City Council of the new \u201c<a href=\"https:\/\/www.comune.bologna.it\/novita\/notizie\/citta-30-nuovo-piano-particolareggiato\">Detailed Plan for Speed Management on Bologna\u2019s Urban Roads<\/a>\u201d, the 30 km\/h speed limit is reintroduced on 258 km of roads (the same roads covered by the previous measure) through the implementation of <a href=\"https:\/\/www.comune.bologna.it\/novita\/comunicati-stampa\/bologna-citta-30-da-lunedi-20-aprile-vigore-le-ordinanze-ecco-come-sara-la-fase-due-oltre-100-0\">22 by-laws<\/a> which define, road by road \u2013 as required by the Ministry of Infrastructure and Transport and in compliance with the Regional Administrative Court\u2019s ruling <a href=\"#_ftn2\" name=\"_ftnref2\">[2]<\/a> \u2013 the individual 30 km\/h zones spread across all neighbourhoods.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Limiti-di-velocita-sulla-rete-stradale-di-Bologna-.jpg\" width=\"1816\" height=\"624\" alt=\"\" class=\"wp-image-4572 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Limiti-di-velocita-sulla-rete-stradale-di-Bologna-.jpg 1816w, https:\/\/datamobility.it\/wp-content\/uploads\/Limiti-di-velocita-sulla-rete-stradale-di-Bologna--1280x440.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Limiti-di-velocita-sulla-rete-stradale-di-Bologna--980x337.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Limiti-di-velocita-sulla-rete-stradale-di-Bologna--480x165.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 1816px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"color: #757575;\"><a name=\"_Toc215586005\" style=\"color: #757575;\"><\/a><span style=\"font-size: small;\">Figure 1 \u2013 Speed limits on Bologna\u2019s road network before (left) and after (right) the implementation of the traffic regulations that came into force on 15 January 2024<\/span><\/span><\/h6>\n<p><span style=\"font-size: small;\"><\/span><\/p>\n<p style=\"text-align: justify;\">The practical implementation of the measure, in addition to the enforcement of checks, involves a <a href=\"https:\/\/www.comune.bologna.it\/novita\/notizie\/bologna-citta-30-report-2025\">multi-year investment programme<\/a> to give tangible form to the \u2018Citt\u00e0 30\u2019 scheme through physical improvements; the budget for this has risen from an initial \u20ac24 million to over \u20ac35 million, to improve the safety of roads, junctions and crossings, the creation of new pedestrian and school squares, and the expansion of the network of footpaths, cycle paths and cycle lanes, as well as cycle stations. From 2022, when the guidelines for Bologna Citt\u00e0 30 were approved, to the start of 2026, approximately \u20ac18.9 million worth of works have already been completed, \u20ac6.5 million are currently underway, \u20ac7.7 million are in the design phase and \u20ac2.1 million are yet to be planned. <\/p>\n<p style=\"text-align: justify;\">In order to \u2018isolate\u2019 as far as possible the effects of the introduction of the Citt\u00e0 30 scheme on the city, various pre- and post-ordinance periods were compared, taking into account both the first six months of 2023 (when the installation of signage announcing the implementation of the measure had not yet begun), the phase of signage installation (October 2023) and, finally, the actual implementation of the by-law in the subsequent periods (the first six months of 2024 and 2025), accompanied by enforcement checks and the commencement of infrastructure works.<\/p>\n<h2 style=\"text-align: justify;\"><strong>What we measured and how<\/strong><\/h2>\n<p style=\"text-align: justify;\">The analysis covers the main, primary, secondary and local roads in Bologna and is based on two separate FCD databases, both derived from black boxes installed in vehicles for insurance purposes, which have been integrated to enhance the study as it progresses:<\/p>\n<ul style=\"text-align: justify;\">\n<li>Aggregated FCD data (2023\u20132025): over 4 million journeys analysed, spread across twelve sample weeks (Q1 and Q2) for each year. This data covers speed, journey times and critical events, namely sudden changes in acceleration (so-called \u2018harsh\u2019 events) and collisions (\u2018crashes\u2019). <\/li>\n<li>High-frequency FCD data from October 2023 and October 2024, corresponding respectively to the periods closely linked to the phase involving the introduction of signage alone and the phase characterised by enforcement checks. A smaller but very high-resolution sample, with GPS tracking at one-second intervals and high spatial accuracy, from which it is possible to obtain a detailed description of accelerations and decelerations, and thus of actual driving dynamics, with over 135,000 total journeys analysed and more than 90 million sampling points. This database was used to calculate, as accurately as possible, the emission levels generated by vehicles and for an in-depth analysis of sudden braking.  <\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Traiettoria-esempio-derivante-da-dati-FCD-ad-alta-frequenza.jpg\" width=\"2256\" height=\"1088\" alt=\"\" class=\"wp-image-4594 size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Traiettoria-esempio-derivante-da-dati-FCD-ad-alta-frequenza.jpg 2256w, https:\/\/datamobility.it\/wp-content\/uploads\/Traiettoria-esempio-derivante-da-dati-FCD-ad-alta-frequenza-1280x617.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Traiettoria-esempio-derivante-da-dati-FCD-ad-alta-frequenza-980x473.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Traiettoria-esempio-derivante-da-dati-FCD-ad-alta-frequenza-480x231.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2256px, 100vw\" \/> <span style=\"font-size: small; color: #757575;\">Figure 2 \u2013 Example trajectory derived from high-frequency FCD data: the top graph illustrates changes in speed (Y-axis) along the route (X-axis), whilst the map below pinpoints critical points, highlighting slowdowns using warm colours (green = higher speeds; red = lower speeds)  <\/span><\/p>\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">FCD data is processed in a fully anonymised and aggregated form, in compliance with European data protection regulations. The stratification of the high-frequency FCD sample<a href=\"#_ftn3\" name=\"_ftnref3\">[3] <\/a>was validated by comparing the characteristics of the monitored vehicles with those of the regional vehicle fleet (source: ACI). As expected, the age distribution of the car fleet is slightly younger than that of the registered fleet (which includes all vehicles that are rarely used \u2013 <a href=\"https:\/\/www.linkedin.com\/posts\/go-mobility-s-r-l-_weekly-data-flotte-dormienti-activity-7425110876740980736-BeCK?utm_source=share&#038;utm_medium=member_desktop&#038;rcm=ACoAAA9qjikBI28zUZKZJvB1N3-m1EUAit3-Vwg\">as discussed here <\/a>and as <a href=\"https:\/\/www.istat.it\/wp-content\/uploads\/2025\/06\/Le-percorrenze-dei-veicoli-stradali-circolanti.pdf\">Istat also explains here<\/a>), whilst the fuel type and brand are in line (see figure below).  <\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-campione-dati-FCD-ad-alta-frequenza-e-parco-auto-ACI.jpg\" width=\"1772\" height=\"880\" alt=\"\" class=\"wp-image-4569 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-campione-dati-FCD-ad-alta-frequenza-e-parco-auto-ACI.jpg 1772w, https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-campione-dati-FCD-ad-alta-frequenza-e-parco-auto-ACI-1280x636.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-campione-dati-FCD-ad-alta-frequenza-e-parco-auto-ACI-980x487.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-campione-dati-FCD-ad-alta-frequenza-e-parco-auto-ACI-480x238.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 1772px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"color: #757575;\">Figure 3 \u2013 Comparison of high-frequency FCD data and the ACI vehicle fleet<\/span><\/h6>\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">To estimate emissions in the urban area of Bologna, a model developed by GO-Mobility based on COPERT Tier 3 (<a href=\"https:\/\/copert.emisia.com\/#:~:text=COPERT%20is%20the%20EU%20standard,a%20specific%20country%20or%20region.\">Computer Programme to Calculate Emissions from Road Transport<\/a>, version 5.8.1[<a href=\"#_ftn4\" name=\"_ftnref4\">4]<\/a>) was used. COPERT is one of the main models for estimating emissions and is developed under the coordination of the European Environment Agency for use by Member States in compiling national emissions inventories. The model provides up-to-date emission factors by vehicle type, fuel type and Euro emission class and was fed with information from high-frequency FCDs, in accordance with the methodological approach set out in the Air Pollutant Emission Inventory Guidebook<a href=\"#_ftn5\" name=\"_ftnref5\">[5<\/a>]. The emissions model developed by GO-Mobility exploits the high resolution of FCD data to reproduce the driving dynamics of each vehicle, including real-world acceleration and deceleration: a level of precision that is difficult to achieve with traditional methodologies.   <\/p>\n<h2 style=\"text-align: justify;\"><strong>How has compliance with the limits changed?<\/strong><\/h2>\n<p style=\"text-align: justify;\">A comparison of the periods corresponding to the start of the measure (October 2023, with signage only) and the enforcement of checks (October 2024) reveals a slight but noticeable improvement in compliance with the speed limit. Whilst in October 2023 \u2013 i.e. with only road signs in place \u2013 53% of journeys were within the speed limit, the following year this figure rose to 58%, representing an improvement (almost 6 in 10 drivers now comply with the limit) even without the application of \u2018draconian\u2019 measures. This figure is comparable to other cities that have recently introduced the \u2018Citt\u00e0 30\u2019 scheme, such as Amsterdam, where <a href=\"https:\/\/ambientenonsolo.com\/amsterdam-30-km-h-i-dati-del-primo-anno\/\">63% of drivers comply with the new limit<\/a>.  <\/p>\n<h2 style=\"text-align: justify;\"><strong>How much extra time is lost?<\/strong><\/h2>\n<p style=\"text-align: justify;\">This is one of the most pressing concerns for those who drive every day and feel \u2018targeted\u2019 by the measure. But how much extra time is actually lost in Bologna as a result of the measure? <\/p>\n<p style=\"text-align: justify;\">Looking at the comparison between pre- and post-measurement figures, again based on aggregated FCD data, it is clear that the median journey time across the entire urban network rose from 3 minutes 32 seconds per kilometre in 2023 to 3 minutes 53 seconds (+9.4%) in 2024 and 3 minutes 58 seconds in 2025 (+2.6%). For typical journeys made by an average user, this represents an increase of just over a minute per journey from 2023 to 2025 (+14 seconds per km on journeys of an average length of 5.2 km). <\/p>\n<p style=\"text-align: justify;\">But how can we determine whether, and to what extent, this impact is attributable to the measure? To do this, we analysed the impact on journey times separately for the roads affected by the measure (where the speed limit was reduced from 50 to 30 km\/h) and those that remained at 50 km\/h, with a further focus on 10 sample routes typically travelled by an average user in the city (mixed roads subject to both 30 and 50 km\/h limits, see Figure 5). <\/p>\n<p style=\"text-align: justify;\">I dati mostrano che le variazioni sono molto simili in tutte le categorie: l\u2019aumento \u00e8 pi\u00f9 marcato il <strong>primo anno<\/strong> (21 secondi per km, +9,4% nell\u2019intera rete urbana nel 2024), per poi <strong>stabilizzarsi il secondo<\/strong> (+6 secondi per km, +2,6% nel 2025). Ma se si guarda alle sole <strong>strade interessate dal provvedimento di riduzione del limite a 30 km\/h<\/strong>, si nota che i tempi di viaggio dal 2023 al 2025 in realt\u00e0 aumentano <strong>in misura minore<\/strong> (di 24 secondi per km, +10,9%) rispetto a quelli delle <strong>strade rimaste a 50 km\/h<\/strong>, che vedono l\u2019<strong>impatto maggiore <\/strong>(+28 secondi per km, +14,8%), specialmente il <strong>secondo anno<\/strong> (+7 secondi\/km), dove invece i tempi delle strade della Citt\u00e0 30 subiscono una <strong>variazione minima<\/strong> (+1 secondi\/km) e gli itinerari-tipo vedono addirittura un <strong>lieve miglioramento<\/strong> dei tempi di viaggio (-1 secondi\/km). <\/p>\n<p style=\"text-align: justify;\">This shows that the increase in journey times across the entire urban network is largely attributable to external factors (including construction sites for new tram lines, Figure 6), whilst the roads affected by the measure experienced less significant delays, as did the typical routes taken by an average user, which were least affected (+14 seconds per km, i.e. from 3\u201904\u2019\u2019 to 3\u201918\u2019\u2019: +7.6%).<\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Rappresentazione-delle-variazioni-nei-tempi-di-percorrenza-.jpg\" width=\"1937\" height=\"1155\" alt=\"\" class=\"wp-image-4573 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Rappresentazione-delle-variazioni-nei-tempi-di-percorrenza-.jpg 1937w, https:\/\/datamobility.it\/wp-content\/uploads\/Rappresentazione-delle-variazioni-nei-tempi-di-percorrenza--1280x763.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Rappresentazione-delle-variazioni-nei-tempi-di-percorrenza--980x584.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Rappresentazione-delle-variazioni-nei-tempi-di-percorrenza--480x286.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 1937px, 100vw\" \/><span style=\"color: #757575; font-size: small;\">Figure 4 \u2013 Illustration of changes in journey times on different types of roads within Bologna\u2019s urban network between 2023 and 2025, based on aggregated FCD data<\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Il-set-di-itinerari-campione.jpg\" width=\"975\" height=\"630\" alt=\"\" class=\"wp-image-4571 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Il-set-di-itinerari-campione.jpg 975w, https:\/\/datamobility.it\/wp-content\/uploads\/Il-set-di-itinerari-campione-480x310.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 975px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"font-size: small; color: #757575;\">Figure 5 \u2013 The set of sample routes used to calculate the change in journey times on a number of typical routes taken by the average user, comprising both roads affected by the \u2018Citt\u00e0 30\u2019 project and others where speed limits remain unchanged<\/span><\/h6>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/tracciati-dei-cantieri-per-le-linee-tranviarie-di-Bologna.jpg\" width=\"2019\" height=\"1136\" alt=\"\" class=\"wp-image-4577 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/tracciati-dei-cantieri-per-le-linee-tranviarie-di-Bologna.jpg 2019w, https:\/\/datamobility.it\/wp-content\/uploads\/tracciati-dei-cantieri-per-le-linee-tranviarie-di-Bologna-1280x720.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/tracciati-dei-cantieri-per-le-linee-tranviarie-di-Bologna-980x551.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/tracciati-dei-cantieri-per-le-linee-tranviarie-di-Bologna-480x270.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2019px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"font-size: small; color: #757575;\">Figure 6 \u2013 Maps showing the construction sites for Bologna\u2019s tram lines, available on the <a href=\"https:\/\/www.trambologna.it\/cantieri\/lavori-in-corso\/\" style=\"color: #757575;\">dedicated website<\/a><\/span><\/h6>\n<h2 style=\"text-align: justify;\"><\/h2>\n<p>&nbsp;<\/p>\n<h2 style=\"text-align: justify;\"><strong>What if we were to isolate the \u201830 km\/h zone\u2019 effect?<\/strong><\/h2>\n<p style=\"text-align: justify;\">To do this, we carried out a theoretical simulation: let\u2019s imagine that all the vehicles in the sample are fitted with an on-board electronic speed limiter that \u2018forces\u2019 them to adhere to the speed limit. How long would it have taken them to complete the same journeys? By reducing the speed of the monitored journeys \u2013 that is, by assuming they maintained a speed of 30 km\/h even when that limit had actually been exceeded on the real journey \u2013 we arrive at an interesting result. The increase in journey times, calculated based on journeys actually made in 2023, would be, in the worst-case scenario, around 5% longer than the actual recorded journey time: in absolute terms, for an average journey of less than 10 minutes, this amounts to just 24 seconds.   <\/p>\n<h3 style=\"text-align: justify;\">Calculation of the theoretical increase in journey times<\/h3>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Simulazione-teorica-con-dati-FCD-ad-alta-frequenza-bologna.jpg\" width=\"1752\" height=\"795\" alt=\"\" class=\"wp-image-4595 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Simulazione-teorica-con-dati-FCD-ad-alta-frequenza-bologna.jpg 1752w, https:\/\/datamobility.it\/wp-content\/uploads\/Simulazione-teorica-con-dati-FCD-ad-alta-frequenza-bologna-1280x581.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Simulazione-teorica-con-dati-FCD-ad-alta-frequenza-bologna-980x445.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Simulazione-teorica-con-dati-FCD-ad-alta-frequenza-bologna-480x218.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 1752px, 100vw\" \/><\/p>\n<h5 style=\"text-align: center;\"><span style=\"font-size: small; color: #757575;\">Figure 7 \u2013 Theoretical simulation using high-frequency FCD data<\/span><\/h5>\n<p><span style=\"font-size: small;\"><\/span><\/p>\n<p style=\"text-align: justify;\">As expected, the increase in journey times would have been greater at night (up to around 10%), when vehicles tend to travel faster and the speed limit has a greater impact than the natural daytime slowdown caused by traffic. During rush hour, in fact, the increase falls below 4%: congestion already reduces speeds, acting as a \u2018natural speed limiter\u2019. <\/p>\n<h2 style=\"text-align: justify;\"><strong><\/strong><\/h2>\n<h2 style=\"text-align: justify;\"><strong>Is Bologna a more polluted city?<\/strong><\/h2>\n<p style=\"text-align: justify;\">This is one of the questions to which the current study provides one of the most original and detailed answers offered to date.<\/p>\n<p style=\"text-align: justify;\">According to estimates calculated using the GO-Mobility emissions model, emissions fall both in terms of climate-changing gases (CO\u2082, -14.6%) and in terms of air pollutants (CO, NOx and PM: -17.8%, -23.7% and -21.7% respectively). This is an apparently paradoxical result: the internal combustion engine is notoriously more efficient at 50 km\/h than at 30 km\/h in terms of fuel consumption per kilometre. How, then, can these reductions be explained?  <\/p>\n<p style=\"text-align: justify;\">The key lies in driving dynamics. The COPERT Tier 3 model, powered by high-frequency FCD data, allows emissions to be calculated not on the basis of average speed, but on the actual sequence of accelerations and decelerations. This is where the key lies: driving at 30 km\/h in a more regular and consistent manner results in significantly fewer instances of abrupt stop-and-go, which are the most energy-intensive and highest-emission phases of the cycle.  <\/p>\n<p style=\"text-align: justify;\">Abrupt acceleration and sudden braking \u2013 which occur more frequently when driving at higher speeds in urban areas \u2013 cause spikes in fuel consumption that result in higher emissions. A significant reduction in these, as evidenced by real-world data, leads to an overall decrease in emissions: estimates based on the October sample show significant reductions in all pollutants between 2023 and 2024. <\/p>\n<p style=\"text-align: justify;\">This remains true even if we assume that the vehicle fleet has not changed in terms of engine type between 2023 and 2024. If, on the other hand, we also take into account the renewal of the vehicle fleet, we see a further reduction: taking October 2023 as a baseline (\u2018index 100\u2019), we estimate a reduction in CO\u2082 to 95.3% and in CO to 91.3%, whilst the decreases in NOx (82.6%) and PM (84%) are even more significant. This comparison highlights how, in terms of the quantitative reduction of polluting and climate-changing emissions, the effect of the \u2018Citt\u00e0 30\u2019 measure is comparable to (if not greater than) that resulting from the renewal of the vehicle fleet.  <\/p>\n<h3 style=\"text-align: justify;\"><\/h3>\n<h3 style=\"text-align: justify;\">Emissions<\/h3>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Scenari-emissivi-bologna.jpg\" width=\"1746\" height=\"775\" alt=\"\" class=\"wp-image-4596 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Scenari-emissivi-bologna.jpg 1746w, https:\/\/datamobility.it\/wp-content\/uploads\/Scenari-emissivi-bologna-1280x568.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Scenari-emissivi-bologna-980x435.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Scenari-emissivi-bologna-480x213.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 1746px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"font-size: small; color: #757575;\">Figure 8 \u2013 Emissions scenarios \u2013 \u2018100\u2019 baseline, October 2023 (road signs only), projected changes in emissions using the same vehicle fleet as in 2023 and the same as in 2024. Estimates based on high-frequency FCD data <\/span><\/h6>\n<h2 style=\"text-align: justify;\"><\/h2>\n<h2 style=\"text-align: justify;\"><strong><\/strong><\/h2>\n<h2 style=\"text-align: justify;\"><strong>Are the streets of Bologna safer?<\/strong><\/h2>\n<p style=\"text-align: justify;\">Data on road accidents, <a href=\"https:\/\/bolognacitta30.it\/faq\/ma-cosa-dicono-i-dati-reali-sulla-sicurezza-stradale-da-quando-bologna-e-diventata-citta-30\/\">collected by the local police on roads within the municipal area of Bologna<\/a>, confirm an overall decline in accidents and injuries in the period following the implementation of the measure, with the number of fatalities halved and no pedestrian fatalities recorded.<\/p>\n<p style=\"text-align: justify;\">These figures also translate into savings in social costs arising from road accidents amounting to <a href=\"https:\/\/www.comune.bologna.it\/novita\/comunicati-stampa\/bologna-citta-30-il-report-del-2025\">nearly \u20ac66 million for the Municipality of Bologna<\/a>, according to a conservative estimate (i.e. one that assumes only moderate injuries) based on parameters from the Ministry of Transport and adjusted to the year 2025.<\/p>\n<p style=\"text-align: justify;\">But even in this case, big data (aggregated FCD data) enables us to provide unprecedented insights derived from the analysis of Harsh and Crash events. Harsh events are instances of sudden changes in acceleration (braking, swerving, sudden acceleration) that can be precursors to accidents. Crash events are incidents that have actually resulted in a collision between vehicles, or between a vehicle and a generic obstacle (pole, tree, wall, etc.). The relationship between the two \u2013 that is, the probability that a Harsh event will lead to a Crash \u2013 is our risk indicator, i.e. the probability that a sudden change in acceleration (sharp steering or braking) will actually result in a road accident. What are the findings in this regard?    <\/p>\n<p style=\"text-align: justify;\">The probability that a sudden manoeuvre will result in a crash has fallen by 22.7% in 2024 and by 17.2% in 2025 compared with 2023. In fact, the proportion of sudden manoeuvres resulting in accidents has fallen from 11.56% in 2023 to 9.57% in 2025. <\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-eventi-Harsh-e-Crash.jpg\" width=\"2328\" height=\"928\" alt=\"\" class=\"wp-image-4570 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-eventi-Harsh-e-Crash.jpg 2328w, https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-eventi-Harsh-e-Crash-1280x510.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-eventi-Harsh-e-Crash-980x391.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Confronto-eventi-Harsh-e-Crash-480x191.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2328px, 100vw\" \/><\/p>\n<h5 style=\"text-align: center;\"><a name=\"_Toc215586032\"><\/a><span style=\"font-size: small; color: #757575;\">Figure 9 \u2013 Comparison of \u2018Harsh\u2019 and \u2018Crash\u2019 incidents recorded on the road network in Bologna during the first two quarters of 2024 and their respective percentage shares, based on aggregated FCD data.<\/span><\/h5>\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">Using the high-frequency FCD dataset (October 2023 vs October 2024), we carried out a similar analysis, paying particular attention to instances of sudden braking. The result is clear: the proportion of the most intense braking events (first percentile) decreases in intensity, falling from a deceleration of over \u22124.9 m\/s\u00b2 in 2023 to \u22122.8 m\/s\u00b2 in 2024: almost half the previous value. On 30 km\/h roads, extreme braking events are halved (Figure 10). This demonstrates how enforcement has led to smoother driving behaviour and, consequently, to improved road safety in the city.  <\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-degli-eventi-Harsh.jpg\" width=\"2099\" height=\"1136\" alt=\"\" class=\"wp-image-4579 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-degli-eventi-Harsh.jpg 2099w, https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-degli-eventi-Harsh-1280x693.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-degli-eventi-Harsh-980x530.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-degli-eventi-Harsh-480x260.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2099px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"font-size: small;\"><a name=\"_Toc215586019\"><\/a><span style=\"color: #757575;\">Figure 10 \u2013 Change in harsh events and sudden braking events between October 2023 and October 2024, based on high-frequency FCD data<\/span><\/span><\/h6>\n<h2 style=\"text-align: justify;\"><\/h2>\n<h2 style=\"text-align: justify;\"><strong><\/strong><\/h2>\n<h2 style=\"text-align: justify;\"><strong>The side effect<\/strong><\/h2>\n<p style=\"text-align: justify;\">A closer look at the Harsh &#038; Crash incidents raises further points for consideration. Whilst sudden braking decreases sharply on 30 km\/h roads, it increases on 50 km\/h roads (as shown in the previous graph, Figure 10). In October 2024, on roads outside the 30 km\/h zone, there was a 30.5% increase in sudden braking compared to October 2023. This phenomenon can be interpreted as \u2018road rage\u2019: the frustration built up by driving at reduced speeds in the 30 km\/h zones is released, once outside, in a more aggressive driving style.   <\/p>\n<h3 style=\"text-align: justify;\">Sudden braking and acceleration<\/h3>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Analisi-provenienti-dagli-FCD-ad-alta-frequenza-bologna.jpg\" width=\"2554\" height=\"1148\" alt=\"\" class=\"wp-image-4597 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Analisi-provenienti-dagli-FCD-ad-alta-frequenza-bologna.jpg 2554w, https:\/\/datamobility.it\/wp-content\/uploads\/Analisi-provenienti-dagli-FCD-ad-alta-frequenza-bologna-1280x575.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Analisi-provenienti-dagli-FCD-ad-alta-frequenza-bologna-980x441.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Analisi-provenienti-dagli-FCD-ad-alta-frequenza-bologna-480x216.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2554px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"font-size: small; color: #757575;\">Figure 11 \u2013 Analyses from high-frequency FCDs<\/span><\/h6>\n<p style=\"text-align: justify;\">\n<p style=\"text-align: justify;\">This is not an isolated phenomenon: similar evidence emerges from the<a href=\"https:\/\/flaggerforce.com\/blog\/the-data-behind-work-zone-traffic-delays-flagger-force\"> literature on roadworks<\/a>, where the perception of being forced to slow down increases frustration and, consequently, aggressive driving. According to certain psychological approaches (<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/074959789190020T\">Theory of Planned Behaviour<\/a>), the perception of an externally imposed control tends to generate resistance. Conversely, speed moderation achieved through physical road design (narrowings, chicanes, raised junctions) tends to<a href=\"https:\/\/www.mdpi.com\/1996-1073\/14\/13\/3726\"> produce more stable compliance<\/a> (adherence to rules and limits) and less negative psychological reactivity.  <\/p>\n<p style=\"text-align: justify;\">This observation underscores the importance of infrastructure measures to support the \u2018Citt\u00e0 30\u2019 policy. By reducing frustrating factors (enforcement, perceived surveillance, lack of understanding of the speed limit), the sense of injustice and repression is diminished, and with it the likelihood of aggressive reactions[<a href=\"#_ftn6\" name=\"_ftnref6\">6]<\/a>. <\/p>\n<h2 style=\"text-align: justify;\"><strong><\/strong><\/h2>\n<h2 style=\"text-align: justify;\"><strong>Traffic: fewer cars on the road?<\/strong><\/h2>\n<p style=\"text-align: justify;\">A large number of <strong><a href=\"https:\/\/dati.comune.bologna.it\/dati\/flusso-veicolare\">magnetic induction loops used for traffic counting<\/a><\/strong> are installed across the Bologna area throughout the year. Traffic counts for the first six months of 2024 and 2025 both show a reduction compared to 2023, although the decline is much more pronounced in 2025 (-16.3%, compared to -2.6% in 2024). This indicates that there has indeed been a modal shift in Bologna from private vehicles to other modes of transport (as confirmed by the<a href=\"https:\/\/bolognacitta30.it\/faq\/ma-cosa-dicono-i-dati-reali-sulla-sicurezza-stradale-da-quando-bologna-e-diventata-citta-30\/\"> increase in urban travel by train, bike and car sharing, and the number of bicycles<\/a> in circulation), with an overall reduction in the number of private vehicles on the road. This trend is explained by a combination of the \u2018Citt\u00e0 30\u2019 scheme, the expansion of public transport, and the presence of construction sites linked to the tramway project, which have restricted traffic on certain routes.   <\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-dei-passaggi-veicolari-registrati.jpg\" width=\"2444\" height=\"968\" alt=\"\" class=\"wp-image-4580 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-dei-passaggi-veicolari-registrati.jpg 2444w, https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-dei-passaggi-veicolari-registrati-1280x507.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-dei-passaggi-veicolari-registrati-980x388.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/Variazione-dei-passaggi-veicolari-registrati-480x190.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2444px, 100vw\" \/><\/p>\n<h6 style=\"text-align: center;\"><span style=\"font-size: small; color: #757575;\">Figure 12 \u2013 Change in vehicle traffic recorded by the city of Bologna\u2019s magnetic loops in the first two quarters of 2024 and 2025 compared with 2023<\/span><\/h6>\n<p>&nbsp;<\/p>\n<h2 style=\"text-align: justify;\"><strong>The figures for 30 km\/h zones in Europe\u2026 do they resemble our own?<\/strong><\/h2>\n<p style=\"text-align: justify;\">Bologna, of course, is not an isolated case: the \u2018Citt\u00e0 30\u2019 model is a widespread and well-established trend across Europe, allowing us to draw initial comparisons with similar cases.<\/p>\n<p style=\"text-align: justify;\">Collisions are falling: in Amsterdam, after one year, car accidents on roads subject to the speed limit <a href=\"https:\/\/ambientenonsolo.com\/amsterdam-30-km-h-i-dati-del-primo-anno\/\">have fallen by 11%<\/a>, and in <a href=\"https:\/\/bolognacitta30.it\/citta-30-nel-mondo\/londra\/\">London by 25%<\/a>. A systematic review of <a href=\"https:\/\/ambientenonsolo.com\/unanalisi-sugli-effetti-delle-zone-30km-h-in-40-citta-europee\/\">40 European cities<\/a><a href=\"https:\/\/bolognacitta30.it\/faq\/ma-cosa-dicono-i-dati-reali-sulla-sicurezza-stradale-da-quando-bologna-e-diventata-citta-30\/\"><\/a><a href=\"#_ftn7\" name=\"_ftnref7\">[7<\/a>] shows an average reduction of 23% in accidents, 37% in fatalities and 38% in injuries. Bologna fits into this picture: in the first year, there was a 13.1% reduction in accidents, a 48.7% reduction in fatalities and zero pedestrian deaths for the first time since 1991, whilst nationally, during the same period, deaths on urban roads <a href=\"https:\/\/bolognacitta30.it\/faq\/ma-cosa-dicono-i-dati-reali-sulla-sicurezza-stradale-da-quando-bologna-e-diventata-citta-30\/\">rose by 7.9<\/a>%. Furthermore, in Amsterdam, 63% of drivers<a href=\"https:\/\/ambientenonsolo.com\/amsterdam-30-km-h-i-dati-del-primo-anno\/\"> comply with the new limit<\/a>, comparable to the 58.4% compliance rate recorded by the FCD in Bologna.  <\/p>\n<p style=\"text-align: justify;\">The increase in journey times is marginal: this is the most common concern, and the data helps to put it into perspective. In Amsterdam, buses take an average of <a href=\"https:\/\/ambientenonsolo.com\/amsterdam-30-km-h-i-dati-del-primo-anno\/\">40 seconds longe<\/a>r from terminus to terminus; in Wales, on the majority of routes monitored, journey times have increased by <a href=\"https:\/\/willhaywardwales.substack.com\/p\/turns-out-the-20mph-limit-was-great\">no more than two minutes<\/a>. In Bologna, our theoretical simulation calculates an extra 24 seconds on an average journey of less than 10 minutes.  <\/p>\n<p style=\"text-align: justify;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/strade-bologna-scaled.jpg\" width=\"2560\" height=\"802\" alt=\"\" class=\"wp-image-4576 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/strade-bologna-scaled.jpg 2560w, https:\/\/datamobility.it\/wp-content\/uploads\/strade-bologna-1280x401.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/strade-bologna-980x307.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/strade-bologna-480x150.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2560px, 100vw\" \/><\/p>\n<p style=\"text-align: justify;\">Contrary to popular belief, the heated and polarised debate against speed limit reductions is not unique to Italy: in Wales, the 20 mph limit <a href=\"https:\/\/willhaywardwales.substack.com\/p\/turns-out-the-20mph-limit-was-great\">sparked street protests<\/a>, the toppling of road signs and a petition with nearly half a million signatures. Two years later, there have been 630 fewer accidents (more than a third) and the implementation cost (around \u00a340 million) was recouped in the first year alone thanks to savings on the social costs associated with accidents (such as the \u00a366 million in savings calculated by the City of Bologna). <\/p>\n<p style=\"text-align: justify;\">Nor is the side effect on fast roads an isolated case: in Wales, on roads with speed limits of 40 mph and above, <a href=\"https:\/\/willhaywardwales.substack.com\/p\/turns-out-the-20mph-limit-was-great\">collisions have risen slightly<\/a> (+4%, compared with a fall of -26% on roads subject to the limit), comparable to the sort of \u2018road rage\u2019 that has seen a rise in more aggressive driving outside the \u2018Citt\u00e0 30\u2019 zone in Bologna.<\/p>\n<h2><strong><\/strong><\/h2>\n<h2><strong>The final verdict<\/strong><\/h2>\n<p style=\"text-align: justify;\">After two years of collecting data on actual journeys, we are now able to present an assessment of Bologna Citt\u00e0 30 that addresses the three key themes of this initiative:<\/p>\n<ul style=\"text-align: justify;\">\n<li>Journey times between 2023 and 2025 have increased slightly, but the smallest increases are seen on the typical routes taken by the average city user, amounting to an extra 14 seconds per kilometre: for an average journey of 5.2 km, this equates to an increase of just over a minute. On roads where the speed limit has been reduced to 30 km\/h, the increase is 24 seconds per km, whilst the greatest changes are seen on roads where the speed limit remains at 50 km\/h (+28 s\/km) and on the urban road network as a whole (+26 s\/km), confirming that journey times, in the actual dynamics of city traffic, are influenced far more by factors other than the maximum speed limit (roadworks, traffic lights at junctions, traffic congestion, etc.). Thanks to the theoretical simulation, it was possible to deduce that the 30 km\/h limit, on its own, would add a maximum of 24 seconds to an average 10-minute journey: a marginal impact, especially during peak hours when congestion already acts as the main limiting factor.  <\/li>\n<li>Environment: contrary to popular belief, driving at a reduced but constant speed reduces emissions of all major pollutants by between 15% and 24%. The reduction in consumption peaks resulting from smoother, more consistent driving (fewer sudden stops and starts) more than compensates for the engine\u2019s lower thermal efficiency at low speeds and produces a reduction in emissions comparable to that achieved by renewing the vehicle fleet. <\/li>\n<li>Safety: sudden braking has more than halved on 30 km\/h roads (-58.2% in 2024 compared to 2023), and overall, in the first year, the likelihood of a sudden acceleration (\u2018harsh\u2019 event) leading to a collision (\u2018crash\u2019) (stabilising at -17.2% in 2025 compared to 2023) \u2013 confirming that a lower speed limit encourages a smoother and safer driving style.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">One issue remains unresolved: the \u2018rebound effect\u2019 on roads outside the 30 km\/h zone, where aggressive driving has increased. This is a sign that regulatory measures alone are not enough, and that physical speed reduction through traffic-calming measures on the infrastructure \u2013 already initiated by the City Council with its \u20ac35 million programme \u2013 is the factor that will determine the measure\u2019s effectiveness and long-term sustainability. <\/p>\n<h3 style=\"text-align: justify;\"><strong>Notes<\/strong><\/h3>\n<p style=\"text-align: justify;\"><em>The data for this analysis are taken from the Bologna Citt\u00e0 30 Monitoring Report, drawn up in December 2025 by GO-Mobility on behalf of the Rusconi Ghigi Urban Innovation Foundation.<\/em><\/p>\n<p style=\"text-align: justify;\">[<a href=\"#_ftnref1\" name=\"_ftn1\">1<\/a>] Updates on the inspections carried out each year can be found in the relevant section of the<a href=\"https:\/\/bolognacitta30.it\/faq\/che-tipo-di-controlli-vengono-fatti-sulle-strade-a-30-km-h-e-con-quali-strumenti\/\"> Bolognacitt\u00e030<\/a> website.<\/p>\n<p style=\"text-align: justify;\"><a href=\"#_ftnref2\" name=\"_ftn2\">[2<\/a>] The roads were identified using a methodology set out in the Plan to implement the Directive issued by the Minister of Transport on 1 February 2024, and on the basis of a detailed technical analysis which is summarised in a specific technical data sheet for each individual road or section of road attached to the orders, in accordance with the Highway Code and in compliance with the Regional Administrative Court\u2019s ruling of last January.<\/p>\n<p style=\"text-align: justify;\"><a href=\"#_ftnref3\" name=\"_ftn3\"><span>[3]<\/span><\/a> In Emilia-Romagna, the sampling rate for this type of vehicle, as monitored by the data providers used, is 3%. However, the number of journeys monitored is sufficient to ensure that the observed phenomena are well represented. <\/p>\n<p style=\"text-align: justify;\"><a href=\"#_ftnref4\" name=\"_ftn4\"><span>[4]<\/span><\/a><span> Mellios, G., Ntziachristos, L., Papageorgiou, T. (2024), <em>COPERT 5.8.1 \u2013 Road Transport Emission Inventory Model<\/em>, EMISIA<\/span><\/p>\n<p style=\"text-align: justify;\"><a href=\"#_ftnref5\" name=\"_ftn5\"><span>[5]<\/span><\/a><span> EMEP\/EEA, <em>Air Pollutant Emission Inventory Guidebook 2025 \u2013 Road Transport (1.A.3.b)<\/em><\/span><\/p>\n<p style=\"text-align: justify;\"><a href=\"#_ftnref6\" name=\"_ftn6\"><span>[6]<\/span><\/a> To explore the link between frustration at the wheel and aggression: Deffenbacher, J. L., Lynch, R. S., Filetti, L. B., Dahlen, E. R., &#038; Oetting, E. R. (2003). \u201c<span><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/12600403\/\">Anger, aggression, risky behavior, and crash-related outcomes in three groups of drivers<\/a><\/span>\u201d, Behaviour Research and Therapy, 41, 333\u2013349.<\/p>\n<p style=\"text-align: justify;\"><a href=\"#_ftnref7\" name=\"_ftn7\"><span>[7]<\/span><\/a><span> Yannis G., Michelaraki E. (2024), Review of City-Wide 30 km\/h Speed Limit Benefits in Europe, Sustainability, 16(11), 4382. <a href=\"https:\/\/www.mdpi.com\/2071-1050\/16\/11\/4382\">https:\/\/www.mdpi.com\/2071-1050\/16\/11\/4382<\/a> <\/span> <\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Subscribe&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; background_enable_color=&#8221;off&#8221; background_enable_image=&#8221;off&#8221; background_size=&#8221;custom&#8221; background_image_width=&#8221;20%&#8221; background_position=&#8221;bottom_left&#8221; custom_margin=&#8221;50px||0px||false|false&#8221; custom_padding=&#8221;0px||0px||true|false&#8221; global_module=&#8221;2869&#8243; locked=&#8221;off&#8221; collapsed=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row column_structure=&#8221;1_2,1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;94px&#8221; custom_margin=&#8221;-56px||||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font_size=&#8221;19px&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p style=\"text-align: center;\"><strong>Sign up for our newsletter!<br \/><\/strong>Every month, you\u2019ll receive news, articles and content hand-picked for you by our editorial team.<strong><\/strong><\/p>\n<p>[\/et_pb_text][et_pb_code _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<script>(function() {\n\twindow.mc4wp = window.mc4wp || {\n\t\tlisteners: [],\n\t\tforms: {\n\t\t\ton: function(evt, cb) {\n\t\t\t\twindow.mc4wp.listeners.push(\n\t\t\t\t\t{\n\t\t\t\t\t\tevent   : evt,\n\t\t\t\t\t\tcallback: cb\n\t\t\t\t\t}\n\t\t\t\t);\n\t\t\t}\n\t\t}\n\t}\n})();\n<\/script><!-- Mailchimp for WordPress v4.12.5 - https:\/\/wordpress.org\/plugins\/mailchimp-for-wp\/ --><form id=\"mc4wp-form-1\" class=\"mc4wp-form mc4wp-form-2799 mc4wp-form-basic\" method=\"post\" data-id=\"2799\" data-name=\"Mailchimp\" ><div class=\"mc4wp-form-fields\"><p>\r\n\t\t<input type=\"email\" name=\"EMAIL\" placeholder=\"Il tuo indirizzo email\" required \/>\r\n<\/label>\r\n<\/p>\r\n<p>\r\n    <label>\r\n        <input type=\"checkbox\" name=\"AGREE_TO_TERMS\" value=\"1\" required=\"\">\r\n        Ho letto e accetto i <a href=\"\/privacy-policy\" target=\"_blank\">termini e le condizioni<\/a>\r\n    <\/label>\r\n<\/p>\r\n\r\n<p>\r\n\t<input type=\"submit\" value=\"Iscriviti\" \/>\r\n<\/p>\r\n<\/div><label style=\"display: none !important;\">Leave this field empty if you're human: <input type=\"text\" name=\"_mc4wp_honeypot\" value=\"\" tabindex=\"-1\" autocomplete=\"off\" \/><\/label><input type=\"hidden\" name=\"_mc4wp_timestamp\" value=\"1781730966\" \/><input type=\"hidden\" name=\"_mc4wp_form_id\" value=\"2799\" \/><input type=\"hidden\" name=\"_mc4wp_form_element_id\" value=\"mc4wp-form-1\" \/><div class=\"mc4wp-response\"><\/div><\/form><!-- \/ Mailchimp for WordPress Plugin -->[\/et_pb_code][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font_size=&#8221;19px&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p style=\"text-align: center;\"><span data-teams=\"true\">Data Mobility is a project by GO-Mobility<br \/>Find out more about us and the projects through which we turn data into mobility solutions.<br \/><\/span><\/p>\n<p>[\/et_pb_text][et_pb_button button_url=&#8221;http:\/\/www.go-mobility.it&#8221; url_new_window=&#8221;on&#8221; button_text=&#8221;VISIT THE GO-MOBILITY WEBSITE&#8221; button_alignment=&#8221;center&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_button=&#8221;on&#8221; button_text_size=&#8221;16px&#8221; button_text_color=&#8221;#FFFFFF&#8221; button_bg_color=&#8221;#4A00EB&#8221; button_border_width=&#8221;0px&#8221; button_border_color=&#8221;#4A00EB&#8221; button_border_radius=&#8221;100px&#8221; button_letter_spacing=&#8221;2px&#8221; button_font=&#8221;IBM Plex Sans Condensed||||||||&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding=&#8221;0px|0px|0px|0px|false|false&#8221; global_module=&#8221;2894&#8243; saved_tabs=&#8221;all&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row column_structure=&#8221;3_4,1_4&#8243; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;|0px|0px|0px|false|false&#8221; custom_padding=&#8221;50px|0px|0px|0px|false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;3_4&#8243; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span>\u00a92026 GO-Mobility s.r.l. | VAT Number 11257581006<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;1_4&#8243; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_social_media_follow _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_social_media_follow_network social_network=&#8221;linkedin&#8221; url=&#8221;https:\/\/www.linkedin.com\/company\/go-mobility-s-r-l-&#8221; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#000000&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221; follow_button=&#8221;off&#8221; url_new_window=&#8221;on&#8221;]linkedin[\/et_pb_social_media_follow_network][et_pb_social_media_follow_network social_network=&#8221;instagram&#8221; url=&#8221;https:\/\/www.instagram.com\/gomobility_it\/&#8221; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#000000&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221; follow_button=&#8221;off&#8221; url_new_window=&#8221;on&#8221;]instagram[\/et_pb_social_media_follow_network][et_pb_social_media_follow_network social_network=&#8221;youtube&#8221; url=&#8221;https:\/\/www.youtube.com\/@go-mobility&#8221; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#000000&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221; follow_button=&#8221;off&#8221; url_new_window=&#8221;on&#8221;]youtube[\/et_pb_social_media_follow_network][et_pb_social_media_follow_network social_network=&#8221;twitter&#8221; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#000000&#8243; global_colors_info=&#8221;{}&#8221; follow_button=&#8221;off&#8221; url_new_window=&#8221;on&#8221;]X[\/et_pb_social_media_follow_network][et_pb_social_media_follow_network social_network=&#8221;facebook&#8221; _builder_version=&#8221;4.26.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#000000&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221; follow_button=&#8221;off&#8221; url_new_window=&#8221;on&#8221;]facebook[\/et_pb_social_media_follow_network][\/et_pb_social_media_follow][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GO-MobilityBologna Citt\u00e0 30: top or flop?A data-driven analysisIt was July 2023 when the City of Bologna announced the introduction of \u2018Citt\u00e0 30\u2019, extending the 30 km\/h speed limit to around 70% of urban roads and becoming the first major Italian city to adopt a measure of this scale. The debate over the measure\u2019s effectiveness soon [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":4668,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[295,306,323,292],"tags":[433],"class_list":["post-4694","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-data","category-fcd","category-mobility","tag-sicurezza-stradale"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Bologna City 30: what big data tells us about traffic, emissions and safety<\/title>\n<meta name=\"description\" content=\"A GO-Mobility analysis of over 4 million journeys shows the impact of the \u2018Bologna Citt\u00e0 30\u2019 scheme on journey times, emissions, road safety and urban traffic.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bologna City 30: what big data tells us about traffic, emissions and safety\" \/>\n<meta property=\"og:description\" content=\"A GO-Mobility analysis of over 4 million journeys shows the impact of the \u2018Bologna Citt\u00e0 30\u2019 scheme on journey times, emissions, road safety and urban traffic.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/\" \/>\n<meta property=\"og:site_name\" content=\"Data Mobility\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-15T13:02:53+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-15T14:06:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"665\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"datamobilityadmin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"datamobilityadmin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"44 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/\"},\"author\":{\"name\":\"datamobilityadmin\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#\\\/schema\\\/person\\\/0a5bb5ee8bdcaa1b40c5379ac463d5ae\"},\"headline\":\"Bologna Citt\u00e0 30: top or flop?\",\"datePublished\":\"2026-05-15T13:02:53+00:00\",\"dateModified\":\"2026-05-15T14:06:46+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/\"},\"wordCount\":8905,\"publisher\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/datamobility.it\\\/wp-content\\\/uploads\\\/bologna-citta-30.jpg\",\"keywords\":[\"Sicurezza stradale\"],\"articleSection\":[\"big data\",\"data\",\"fcd\",\"mobility\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/\",\"url\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/\",\"name\":\"Bologna City 30: what big data tells us about traffic, emissions and safety\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/datamobility.it\\\/wp-content\\\/uploads\\\/bologna-citta-30.jpg\",\"datePublished\":\"2026-05-15T13:02:53+00:00\",\"dateModified\":\"2026-05-15T14:06:46+00:00\",\"description\":\"A GO-Mobility analysis of over 4 million journeys shows the impact of the \u2018Bologna Citt\u00e0 30\u2019 scheme on journey times, emissions, road safety and urban traffic.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#primaryimage\",\"url\":\"https:\\\/\\\/datamobility.it\\\/wp-content\\\/uploads\\\/bologna-citta-30.jpg\",\"contentUrl\":\"https:\\\/\\\/datamobility.it\\\/wp-content\\\/uploads\\\/bologna-citta-30.jpg\",\"width\":1000,\"height\":665},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/bologna-citta-30-top-or-flop\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/datamobility.it\\\/en\\\/home\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Bologna Citt\u00e0 30: top or flop?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#website\",\"url\":\"https:\\\/\\\/datamobility.it\\\/en\\\/\",\"name\":\"Data Mobility\",\"description\":\"Mobilit\u00e0 | Trasporto Pubblico | Big Data\",\"publisher\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/datamobility.it\\\/en\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#organization\",\"name\":\"Data Mobility\",\"url\":\"https:\\\/\\\/datamobility.it\\\/en\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/datamobility.it\\\/wp-content\\\/uploads\\\/Data-Mobility-Logo.svg\",\"contentUrl\":\"https:\\\/\\\/datamobility.it\\\/wp-content\\\/uploads\\\/Data-Mobility-Logo.svg\",\"width\":247,\"height\":58,\"caption\":\"Data Mobility\"},\"image\":{\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/datamobility.it\\\/en\\\/#\\\/schema\\\/person\\\/0a5bb5ee8bdcaa1b40c5379ac463d5ae\",\"name\":\"datamobilityadmin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/074319857f3ae0a3da2f3b8ab51b427986ade849ea4d2c05d91e9c59b6a74b56?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/074319857f3ae0a3da2f3b8ab51b427986ade849ea4d2c05d91e9c59b6a74b56?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/074319857f3ae0a3da2f3b8ab51b427986ade849ea4d2c05d91e9c59b6a74b56?s=96&d=mm&r=g\",\"caption\":\"datamobilityadmin\"},\"sameAs\":[\"https:\\\/\\\/datamobility.it\"],\"url\":\"https:\\\/\\\/datamobility.it\\\/en\\\/magazine\\\/author\\\/datamobilityadmin\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Bologna City 30: what big data tells us about traffic, emissions and safety","description":"A GO-Mobility analysis of over 4 million journeys shows the impact of the \u2018Bologna Citt\u00e0 30\u2019 scheme on journey times, emissions, road safety and urban traffic.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/","og_locale":"en_US","og_type":"article","og_title":"Bologna City 30: what big data tells us about traffic, emissions and safety","og_description":"A GO-Mobility analysis of over 4 million journeys shows the impact of the \u2018Bologna Citt\u00e0 30\u2019 scheme on journey times, emissions, road safety and urban traffic.","og_url":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/","og_site_name":"Data Mobility","article_published_time":"2026-05-15T13:02:53+00:00","article_modified_time":"2026-05-15T14:06:46+00:00","og_image":[{"width":1000,"height":665,"url":"https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg","type":"image\/jpeg"}],"author":"datamobilityadmin","twitter_card":"summary_large_image","twitter_misc":{"Written by":"datamobilityadmin","Est. reading time":"44 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#article","isPartOf":{"@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/"},"author":{"name":"datamobilityadmin","@id":"https:\/\/datamobility.it\/en\/#\/schema\/person\/0a5bb5ee8bdcaa1b40c5379ac463d5ae"},"headline":"Bologna Citt\u00e0 30: top or flop?","datePublished":"2026-05-15T13:02:53+00:00","dateModified":"2026-05-15T14:06:46+00:00","mainEntityOfPage":{"@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/"},"wordCount":8905,"publisher":{"@id":"https:\/\/datamobility.it\/en\/#organization"},"image":{"@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#primaryimage"},"thumbnailUrl":"https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg","keywords":["Sicurezza stradale"],"articleSection":["big data","data","fcd","mobility"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/","url":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/","name":"Bologna City 30: what big data tells us about traffic, emissions and safety","isPartOf":{"@id":"https:\/\/datamobility.it\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#primaryimage"},"image":{"@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#primaryimage"},"thumbnailUrl":"https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg","datePublished":"2026-05-15T13:02:53+00:00","dateModified":"2026-05-15T14:06:46+00:00","description":"A GO-Mobility analysis of over 4 million journeys shows the impact of the \u2018Bologna Citt\u00e0 30\u2019 scheme on journey times, emissions, road safety and urban traffic.","breadcrumb":{"@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#primaryimage","url":"https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg","contentUrl":"https:\/\/datamobility.it\/wp-content\/uploads\/bologna-citta-30.jpg","width":1000,"height":665},{"@type":"BreadcrumbList","@id":"https:\/\/datamobility.it\/en\/magazine\/bologna-citta-30-top-or-flop\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/datamobility.it\/en\/home\/"},{"@type":"ListItem","position":2,"name":"Bologna Citt\u00e0 30: top or flop?"}]},{"@type":"WebSite","@id":"https:\/\/datamobility.it\/en\/#website","url":"https:\/\/datamobility.it\/en\/","name":"Data Mobility","description":"Mobilit\u00e0 | Trasporto Pubblico | Big Data","publisher":{"@id":"https:\/\/datamobility.it\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datamobility.it\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/datamobility.it\/en\/#organization","name":"Data Mobility","url":"https:\/\/datamobility.it\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/datamobility.it\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datamobility.it\/wp-content\/uploads\/Data-Mobility-Logo.svg","contentUrl":"https:\/\/datamobility.it\/wp-content\/uploads\/Data-Mobility-Logo.svg","width":247,"height":58,"caption":"Data Mobility"},"image":{"@id":"https:\/\/datamobility.it\/en\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/datamobility.it\/en\/#\/schema\/person\/0a5bb5ee8bdcaa1b40c5379ac463d5ae","name":"datamobilityadmin","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/074319857f3ae0a3da2f3b8ab51b427986ade849ea4d2c05d91e9c59b6a74b56?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/074319857f3ae0a3da2f3b8ab51b427986ade849ea4d2c05d91e9c59b6a74b56?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/074319857f3ae0a3da2f3b8ab51b427986ade849ea4d2c05d91e9c59b6a74b56?s=96&d=mm&r=g","caption":"datamobilityadmin"},"sameAs":["https:\/\/datamobility.it"],"url":"https:\/\/datamobility.it\/en\/magazine\/author\/datamobilityadmin\/"}]}},"_links":{"self":[{"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/posts\/4694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/comments?post=4694"}],"version-history":[{"count":2,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/posts\/4694\/revisions"}],"predecessor-version":[{"id":4696,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/posts\/4694\/revisions\/4696"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/media\/4668"}],"wp:attachment":[{"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/media?parent=4694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/categories?post=4694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datamobility.it\/en\/wp-json\/wp\/v2\/tags?post=4694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}