{"id":4305,"date":"2026-01-26T15:34:30","date_gmt":"2026-01-26T14:34:30","guid":{"rendered":"https:\/\/datamobility.it\/?p=4305"},"modified":"2026-02-24T17:46:00","modified_gmt":"2026-02-24T16:46:00","slug":"the-two-sides-of-italian-logistics","status":"publish","type":"post","link":"https:\/\/datamobility.it\/en\/magazine\/the-two-sides-of-italian-logistics\/","title":{"rendered":"<strong>Decoding the rhythms of the city with GPS data<\/strong><br>"},"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\/dati-gps-ritmi-citta.jpg&#8221; alt=&#8221;big data mobility planning&#8221; title_text=&#8221;gps-data-city-rhythms&#8221; 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; global_colors_info=&#8221;{}&#8221;][\/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>Drawn from the thesis of Francesca Nadalini (trainee at 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>Decoding the rhythm of cities with GPS data<\/strong><\/h1>\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<h3><span>A study on the potential of activity-based models<\/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 &lt; 3.5 t) and <strong>heavy vehicles<\/strong> (gross vehicle weight &gt; 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&amp;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 &lt; 3.5 t) and <strong>heavy vehicles<\/strong> (gross vehicle weight &gt; 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&amp;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><strong>How well can we understand the movements of a city?<\/strong> Here are <strong>5 things<\/strong> we learned from the thesis research of our trainee Francesca Nadalini (Politecnico di Milano), a valuable study aimed at identifying <strong>new data sources<\/strong> and developing <strong>innovative methods<\/strong> to fuel <strong>activity-based models (AcBM)<\/strong> and produce useful metrics to guide mobility planning. Based on an extensive sample of GPS traces, we will discover how it is possible to transform raw and chaotic data into a <strong>detailed narrative<\/strong> of human behavior and develop <strong>solutions<\/strong> for current mobility challenges. <\/p>\n<h2><strong>From <em>how<\/em> we travel\u2026 to <em>why<\/em><\/strong><\/h2>\n<p>The thesis focuses on the potential of <strong>activity-based models<\/strong>, which shift the transport planner&#8217;s question by focusing not so much on the trip itself but on <strong>why it is made<\/strong>, that is, on the <strong>activities<\/strong> that drive people to move. Why is this paradigm shift so important? We explained it in just 7 minutes in this illustrated video \ud83d\udc47  <\/p>\n<p>[\/et_pb_text][et_pb_code _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_orientation=&#8221;center&#8221; global_colors_info=&#8221;{}&#8221;]<iframe loading=\"lazy\" src=\"https:\/\/www.linkedin.com\/embed\/feed\/update\/urn:li:ugcPost:7397305025364107264?compact=1\" height=\"399\" width=\"504\" frameborder=\"0\" allowfullscreen=\"\" title=\"Embedded post\"><\/iframe>[\/et_pb_code][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 &lt; 3.5 t) and <strong>heavy vehicles<\/strong> (gross vehicle weight &gt; 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&amp;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 &lt; 3.5 t) and <strong>heavy vehicles<\/strong> (gross vehicle weight &gt; 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&amp;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>The limitation of traditional transport models, known as <strong>trip-based<\/strong>, is that they consider each trip as an isolated event from point A to point B, without taking into account the complex <strong>chains of decisions<\/strong> that characterize people&#8217;s lives.<\/p>\n<p>However, people do not travel for the simple <strong>sake of it<\/strong> (or at least not always), but do so because they are driven by different needs and activities (work, study, shopping, leisure) distributed differently in time and space. It is the concept of <strong>derived demand<\/strong>: the trip is a consequence, not the ultimate goal. <\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/modeli-tradizionali.jpg\" width=\"1794\" height=\"866\" alt=\"\" class=\"wp-image-4324 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/modeli-tradizionali.jpg 1794w, https:\/\/datamobility.it\/wp-content\/uploads\/modeli-tradizionali-1280x618.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/modeli-tradizionali-980x473.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/modeli-tradizionali-480x232.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) 1794px, 100vw\" \/><\/p>\n<p><span data-teams=\"true\">An example: imagine a person (&#8220;Anna&#8221;) who is used to going to work by car leaving at 7:30 and stopping to shop at the supermarket on the return trip home. One day her company decides to offer its employees a <strong>strong monetary incentive<\/strong> for using <strong>public transport<\/strong>. At that point Anna decides it is more convenient to go to work by <strong>train<\/strong>. She will therefore reorganize her routine: she will decide to reach the station on foot to get some daily exercise, and since the old supermarket is no longer so convenient, she will choose a <strong>new reference<\/strong> for shopping, namely the market near home, which she will pass on the way back from the station. Here: a traditional model <strong>would not be able to predict<\/strong> such a complex reorganization, while an activity-based approach would be able to do so because it considers the individual&#8217;s <strong>needs<\/strong>.    <\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/effetto-introduzione-incentivo-tpl-aziendale.png\" width=\"1500\" height=\"824\" alt=\"\" class=\"wp-image-4329 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/effetto-introduzione-incentivo-tpl-aziendale.png 1500w, https:\/\/datamobility.it\/wp-content\/uploads\/effetto-introduzione-incentivo-tpl-aziendale-1280x703.png 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/effetto-introduzione-incentivo-tpl-aziendale-980x538.png 980w, https:\/\/datamobility.it\/wp-content\/uploads\/effetto-introduzione-incentivo-tpl-aziendale-480x264.png 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) 1500px, 100vw\" \/><\/p>\n<p style=\"text-align: center;\"><span style=\"font-size: small;\"><small>Image inspired by the example reported in the study by Ben-Akiva and Bowman in &#8220;Activity Based Travel Demand Model Systems<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a>]<\/small><\/span><\/p>\n<p>Activity-based models, in fact, represent a paradigm shift: they place <strong>the person, not the trip,<\/strong> at the center of the analysis, considering their entire daily activity schedule. These models do not merely study <strong>how we move<\/strong>, but seek to simulate <strong>why, when, and how<\/strong> we organize our day. <\/p>\n<p>This approach offers a much more <strong>realistic<\/strong> vision and allows testing with greater accuracy the introduction of any <strong>changes to the routine<\/strong>, whether monetary incentives as in Bob&#8217;s example up to new regulations on smart working or urban tolls.<\/p>\n<blockquote>\n<p>&#8220;<em>Stated simply, the motivation for <strong>activity-based travel demand<\/strong> modelling is that travel <strong>decisions are activity based<\/strong><\/em>.&#8221;<\/p>\n<\/blockquote>\n<h2><strong>From sample surveys to GPS data<\/strong><\/h2>\n<p>For decades, the main source of data for transport planning has been travel surveys conducted in the form of <strong>sample questionnaires<\/strong>. This method, while valuable, has significant limitations: it is extremely costly, time-consuming, and subject to memory errors or inaccuracies by respondents (a phenomenon known as &#8220;recall bias&#8221;). <\/p>\n<p>The breakthrough in the transport field is represented by the advent of <strong>GPS data<\/strong>, provided by providers that collect data from apps, appropriately <strong>anonymized<\/strong> to ensure user privacy. These data offer <strong>great advantages<\/strong> for those who study and plan mobility: precision, objectivity, efficiency, and, above all, <strong>unprecedented scale<\/strong>. In the case of the research in question, which used the city of Milan as its focus, it is a database of <strong>100 million<\/strong> GPS &#8220;pings&#8221; and almost <strong>one million<\/strong> users, considering an observation period of one month (mid-November to mid-December 2024).  <\/p>\n<p>However, these are not <strong>ready-to-use<\/strong> data. Like many big data that are not specifically created for mobility analyses (&#8220;<strong>first generation big data<\/strong>&#8220;, <a href=\"https:\/\/datamobility.it\/en\/magazine\/make-tpl-great-again-2\/\">we discussed this here<\/a>), they are presented in raw form and require an accurate <strong>cleaning process<\/strong> to ensure quality and completeness. First, all profiles that had an <strong>insufficient<\/strong> and <strong>very sparse<\/strong> number of pings during the observation month were removed, losing 82% of the sample. On the remaining 164,000 profiles, further cleaning steps were carried out to obtain <strong>mobility diaries<\/strong> that were as <strong>reliable and reconstructable<\/strong> as possible, for example discarding those who had data available for less than <strong>three days<\/strong> and arriving at a final core of <strong>17,000 mobility diaries<\/strong>.   <\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/dati-grezzi-distribuzione-dei-ping-Milano.jpg\" width=\"462\" height=\"542\" alt=\"\" class=\"wp-image-4322 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/dati-grezzi-distribuzione-dei-ping-Milano.jpg 462w, https:\/\/datamobility.it\/wp-content\/uploads\/dati-grezzi-distribuzione-dei-ping-Milano-256x300.jpg 256w\" sizes=\"(max-width: 462px) 100vw, 462px\" \/><\/p>\n<p style=\"text-align: center;\"><span style=\"font-size: small;\"><em>Raw data: distribution of pings (Milan case study)<\/em><\/span><\/p>\n<h2><strong>Reconstructing behaviors<\/strong><\/h2>\n<p>The second methodological step was to recognize, from a chaotic sequence of GPS points, which of these were the trajectory of a movement (<strong>trip<\/strong>) and which instead represented stationing in a place for carrying out an activity (<strong>stop<\/strong>).<\/p>\n<p>Traditional methods, based on <strong>fixed thresholds<\/strong> of time or speed, are not suitable for this type of data: the frequency of GPS pings is too variable and inconsistent. The study therefore adopted <strong>DBSCAN<\/strong> (Density-Based Spatial Clustering of Applications with Noise), an unsupervised <strong>clustering algorithm<\/strong> (we discussed <a href=\"https:\/\/datamobility.it\/en\/magazine\/transforming-big-data-into-mobility-services\/\">supervised models here<\/a>), applying it in an innovative way. Instead of simply grouping geographic points, the methodology applied the algorithm to <strong>coordinates weighted by speed<\/strong> between one point and the next.  <\/p>\n<p>Thanks to this adjustment, points belonging to a <strong>trip<\/strong> (high speed, scattered points) were grouped into clusters, while points of a <strong>stop<\/strong> (almost zero speed, densely accumulated points) were classified by the algorithm as anomalous (&#8220;noise&#8221;). The innovation consisted in understanding that, in this context, the anomaly was not an error to be discarded, but exactly the signal being sought: <strong>the activity<\/strong>. To correct small inaccuracies and refine the distinction between stops and trips, other minor post-processing steps were carried out, including techniques such as <strong>K-Nearest Neighbors (KNN)<\/strong>, which added an additional level of methodological robustness.  <\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/Individuazione-di-punti-di-sosta-spostamento-.jpg\" width=\"856\" height=\"416\" alt=\"\" class=\"wp-image-4323 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/Individuazione-di-punti-di-sosta-spostamento-.jpg 856w, https:\/\/datamobility.it\/wp-content\/uploads\/Individuazione-di-punti-di-sosta-spostamento--480x233.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 856px, 100vw\" \/><\/p>\n<p style=\"text-align: center;\"><span style=\"font-size: small;\"><em>Identification of stop points (green) and movement points (blue) and correction with elimination of isolated stop points (on the right)<\/em><\/span><\/p>\n<h2><strong>Identifying trip purposes<\/strong><\/h2>\n<p>To take the final step toward the core of activity-based models, namely <strong>knowing the purpose of the trip<\/strong>, it is necessary to know the nature of the destination: is it an office, a shop, a school?<\/p>\n<p>The classification of destinations consists of <strong>two levels<\/strong>:<\/p>\n<ul>\n<li>a <strong>first &#8220;macro&#8221; level<\/strong> in which activities are distinguished into three main categories: Home, Main Activity, and Secondary Activity, according to precise time and frequency parameters.<\/li>\n<li>a <strong>second level<\/strong> in which, thanks to the integration of land use data, detail is entered to give a more precise classification to these macro-categories (for example, whether the main activity is study or work, or what type of secondary activity it is) and to correct any anomalies.<\/li>\n<\/ul>\n<p>To do this, activity data were overlaid with <strong>land use<\/strong> information from <strong>OpenStreetMap (OSM)<\/strong>. A catchment area was created that covered <strong>98%<\/strong> of identified activities, processing approximately <strong>1.2 million buildings<\/strong> and <strong>224,000 Points of Interest (POI)<\/strong>. By overlaying the position of each stop with these maps, it was possible to deduce the purpose: for example, if a long-duration stop fell on an area mapped as &#8220;university,&#8221; the activity was classified as &#8220;<strong>Study<\/strong>&#8220;; if it fell on an area with offices, it became &#8220;<strong>Work<\/strong>&#8220;.  <\/p>\n<p>However, it is important to emphasize the <strong>importance of the first step<\/strong>, that of recognizing activities into macro-categories: land use data, in fact, are not always precise and have gaps. In other cases, it is difficult to understand whether the ping refers to one shop or the one next to it. Without <strong>accurate categorization<\/strong> of usual residence and main activity, for example, it would not be possible to understand whether a person going to a restaurant is doing so to have lunch (secondary activity) or for work (main activity).  <\/p>\n<p style=\"text-align: center;\"><em><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/punti-di-interesse-e-edificia-milano.jpg\" width=\"792\" height=\"884\" alt=\"\" class=\"wp-image-4325 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/punti-di-interesse-e-edificia-milano.jpg 792w, https:\/\/datamobility.it\/wp-content\/uploads\/punti-di-interesse-e-edificia-milano-480x536.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 792px, 100vw\" \/><br \/><span style=\"font-size: small;\">Image from the thesis<\/span><\/em><\/p>\n<p><em> <\/em>Not only that: the methodology included a two-way <strong>adjustment<\/strong> process. For example, an activity initially classified as &#8220;Occasional&#8221; (because infrequent), but lasting many hours in an area mapped as &#8220;Workplace,&#8221; was corrected to &#8220;Work.&#8221; Conversely, an activity classified as &#8220;Regular&#8221; (because daily), but very brief, such as stopping at the bar for morning coffee, was reclassified as &#8220;Occasional.&#8221; This procedure made it possible to translate a set of <strong>scattered coordinates<\/strong> into a series of activities with a purpose, getting closer and closer to the research goal: <strong>decoding human behavior<\/strong>.   <\/p>\n<h2><strong>No more &#8220;average travelers&#8221;: seeing and recognizing different user profiles<\/strong><\/h2>\n<p>Contemporary mobility behaviors follow increasingly <strong>multifaceted and complex<\/strong> logics, often linked to people&#8217;s different life trajectories and contexts. Consequently, to create effective mobility models, the population cannot be treated as a <strong>homogeneous block<\/strong>. It is necessary to identify <strong>segments with similar behaviors<\/strong>, the so-called &#8220;person types.&#8221; How was this possible?   <\/p>\n<p>The different <strong>mobility profiles<\/strong> were extrapolated by analyzing <strong>recurring activity patterns<\/strong>. For example: <\/p>\n<ul>\n<li>A user with long and recurring stops in places labeled as &#8220;Workplace&#8221; was classified as &#8220;<strong>Full-time worker<\/strong>&#8221; (or &#8220;part-time&#8221; if the average duration was below a certain threshold).<\/li>\n<li>A user whose main stops occurred in &#8220;Education&#8221; places was identified as &#8220;<strong>University student<\/strong>&#8221; or &#8220;Secondary school student,&#8221; depending on the type of institution.<\/li>\n<li>Users who did not show regular patterns of &#8220;mandatory&#8221; activities were classified in the &#8220;<strong>Non-worker\/retiree<\/strong>&#8221; group.<\/li>\n<\/ul>\n<p>However, as the thesis points out, this method cannot distinguish with certainty a &#8220;non-worker&#8221; from a &#8220;retiree,&#8221; given the <strong>absence of demographic data<\/strong>. Similarly, it struggles to identify students&#8217; part-time jobs, since the study activity is dominant. These are limitations that could be compensated by <strong>integrating other types of data<\/strong> more traditional (data fusion), for example census data or data from questionnaires.  <\/p>\n<p>Recognizing these limitations demonstrates, however, how, even from anonymous data, it is still worth exploiting the enormous potential of GPS data for the <strong>volume of movements<\/strong> identifiable simultaneously at an unprecedented level of detail and the absence of <strong>recall bias<\/strong>, reconstructing population segments with distinct <strong>behaviors<\/strong> and providing important elements for understanding and improving the mobility of our cities.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datamobility.it\/wp-content\/uploads\/analisi-dati-milano.jpg\" width=\"1612\" height=\"602\" alt=\"\" class=\"wp-image-4321 aligncenter size-full\" srcset=\"https:\/\/datamobility.it\/wp-content\/uploads\/analisi-dati-milano.jpg 1612w, https:\/\/datamobility.it\/wp-content\/uploads\/analisi-dati-milano-1280x478.jpg 1280w, https:\/\/datamobility.it\/wp-content\/uploads\/analisi-dati-milano-980x366.jpg 980w, https:\/\/datamobility.it\/wp-content\/uploads\/analisi-dati-milano-480x179.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) 1612px, 100vw\" \/><\/p>\n<h2><strong>Beyond a Mere Academic Exercise<\/strong><\/h2>\n<p>The research demonstrates that combining large-scale GPS data with advanced analytical methodologies represents an extremely powerful tool for understanding urban mobility, with an unprecedented level of <strong>detail and accuracy<\/strong>.<\/p>\n<p>However, to ensure this does not remain a <strong>mere academic exercise<\/strong>, it is important that these tools and methodologies increasingly permeate the toolkits of urban planning and territorial governance. This rich dataset and the proposed methodology, for example, can be used to power simulation platforms capable of creating a <strong>digital twin<\/strong> of the population under study, and thus of the city&#8217;s daily life (<a href=\"https:\/\/datamobility.it\/en\/magazine\/make-tpl-great-again-2\/\">see our in-depth analysis here<\/a>). The methodology is designed to be replicable in any city and territory, although transferability is partially linked to the <strong>quantity and quality of available data<\/strong>.  <\/p>\n<p>As mentioned, thanks to these tools we are able to test more realistically <strong>how the population would react<\/strong> to the introduction of new services, policies, or incentives, such as adding a new metro line, or to <strong>optimize<\/strong> public service schedules or plan cycling infrastructure in a targeted manner.<\/p>\n<p>Improving the <strong>ability<\/strong> to analyze this data is essential for designing mobility services that are increasingly <strong>efficient and tailored<\/strong> to the inhabitants of our cities and their diverse needs.<\/p>\n<p>&nbsp;<\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\"><span>[1]<\/span><\/a> Ben-Akiva, M.E., Bowman, J.L. (1998). Activity Based Travel Demand Model Systems. In: Marcotte, P., Nguyen, S. (eds) Equilibrium and Advanced Transportation Modelling. Centre for Research on Transportation. Springer, Boston, MA.     <a href=\"https:\/\/doi.org\/10.1007\/978-1-4615-5757-9_2\">https:\/\/doi.org\/10.1007\/978-1-4615-5757-9_2<\/a><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/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 _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;94px&#8221; 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