As the logistics environment changes dramatically in cities around the world, various freight-related issues have emerged. To address these issues, responses from the public sector are becoming increasingly important, especially at the local government level. However, knowledge and experience in urban freight/city logistics have not been sufficiently accumulated at the local level, making it challenging to address local freight-related problems. In this paper, we present an urban freight typology based on data from 688 local government units across seven metropolitan areas in Japan. Unlike previous typology studies that primarily rely on fine spatial units and estimated freight demand indicators, the proposed framework adopts a municipality-level perspective and incorporates observed freight demand data alongside socio-economic, land-use, and transport system characteristics. Using factor analysis and an agglomerative clustering approach, we identify seven urban freight factors and nine clusters. The results confirm that each cluster effectively captures freight characteristics specific to local governments. Furthermore, to demonstrate the practicability of the proposed typology, we analyze the relationship between clusters and vehicle stop frequencies, and, using the case of the Kanto metropolitan area, identify clusters preferred for locating extremely large logistics facilities. Statistical validation showed that the typology explains substantial variation in vehicle stop density and the spatial distribution of large-scale logistics facilities, supporting its usefulness for freight planning and policy analysis.
In a highly integrated Europe where freedom of movement is guaranteed by law, national borders should not pose any barriers to transportation. However, evidence suggests otherwise. National borders still appear to impede passenger transport. This paper aims to estimate the magnitude of border effects and their determinants within the context of European air passenger transport. A gravity model incorporating a unique combination of determinants of both air transport and border effects is estimated. The results indicate that cross-border routes are associated with a 74.3% decrease in seat capacity and a 72.2% decrease in total frequency relative to comparable domestic routes. Border effects are reduced between countries that share a language and between countries that experience significant migration flows. Furthermore, based on a country-pair analysis, we found that borders in Western Europe act as lower barriers than those in Central-Eastern Europe.
With evolving transportation systems, the perception of travel distance has shifted from physical distance to travel time distance, making spatial maps inadequate for capturing spatiotemporal dynamics, calling for new cartographic methods. However, existing research on constructing time cartograms and assess transportation accessibility remains limited. This study proposes a framework for constructing centered time cartograms including a hybrid coordinate transformation algorithm and a multidimensional accessibility indicator system. The hybrid coordinate transformation algorithm integrates global transformation based on the moving least square method and local correction via Delaunay triangulation. With the dynamic weighting fusion to balance between global and local transformation, we develop the multidimensional accessibility indicator system incorporating service, economic and multimodal dimensions beyond travel time. The proposed framework is applied to construct time cartograms and assess accessibility across air, railway, highway, and subway networks in China. Results reveal distinctive accessibility patterns, with bidimensional regression quantifying map deformation. At the national scale, air travel yields the least deformation, while railway travel exhibits the greatest variability due to network asymmetries. At the urban scale, the empirical study is conducted in Shanghai. Highway travel time cartograms show uniform, concentric patterns, while subway time cartograms reflect east-west compression, due to efficient rail connections on this axis.
Understanding visitors' travel mode choice is critical for promoting sustainable transportation in tourism-dependent regions. However, most existing studies focus on residents' travel behavior or visitors' primary travel mode choice, with limited research examining trip-based intra-destination travel mode choices among visitors, particularly in the U.S. This study collected detailed travel diary data from visitors on Oʻahu, Hawaiʻi, to analyze the relationships between various factors and mode choice. Given the imbalanced distribution of travel modes, the high proportion of categorical variables, and the potential non-linear and interaction effects, the emerging machine-learning method, CatBoost, was employed to model these relationships. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied to quantify the impacts of various factors on mode choice. The model evaluation and comparison results indicate that CatBoost outperforms Random Forest, XGBoost, and the Multinomial Logit (MNL) model in mode classification accuracy. SHAP analysis reveals that most variables exert non-linear effects on the likelihood of predicting different travel modes. Travel distance emerged as the most influential factor in determining visitor mode choice. Other trip-specific factors, including trip start time, visiting frequency, length of stay, and accommodation type, also play significant roles. Transportation service availability, travel habits, and mobility experience are also important factors influencing visitors' travel mode choice. In contrast, only a few demographic characteristics, specifically household income and country of origin, show a notable influence on mode choice. These findings offer valuable insights for improving the accuracy of transportation planning and modeling in tourism-dependent regions.
Working from home (WFH) and e-commerce are widely encouraged as practical tools to reduce congestion and environmental impacts. However, the persistence or rebound of vehicle travel following widespread digital adoption has created practical uncertainty, making it essential to understand how remote work and different e-commerce platforms reshape mobility patterns over time. This study investigates how digital lifestyles restructure regional mobility systems by examining the effect of WFH and e-commerce on residential vehicle miles traveled (VMT) across different temporal regimes. Drawing on Substitution–Complementarity Theory and Rebound Effect Theory, we conceptualize remote work and online consumption as activity-level and transaction-level mechanisms that reorganize the spatial distribution of mobility demand. Using a county-month panel constructed from Replica data, we find temporal instability and platform heterogeneity in regional digital mobility effects. County-level WFH prevalence negatively affects residential VMT in the pre-shock regime but positively affects it in the post-shock regime, consistent with a shift from substitution to rebound dynamics. E-commerce effects are platform-specific: the effect of county-level business-to-consumer (B2C) online retail activity shifts from negative to positive across regimes, whereas online-to-offline (O2O) restaurant activity negatively affects residential VMT in the post-shock regime. Interaction analyses further indicate that greater B2C intensity weakens, whereas greater O2O restaurant intensity strengthens, the positive effect of county-level WFH prevalence on residential VMT in the post-shock regime. Our findings thus urge managers to anticipate that long-term WFH may increase regional travel demand, while platform operators and logistics providers should adopt adaptive delivery and capacity strategies aligned with evolving digital mobility patterns.
Micromobility devices, including bikes, e-bikes, and e-scooters, can play an important role in providing first- and last-mile connections to public transport. By offering an alternative to short car trips, such integration can support the core sustainability objectives of micromobility. However, broader uptake remains constrained by multiple barriers, including access distance, individual preferences, local geographic conditions, and the limited availability of high-quality micromobility infrastructure. Among these factors, further empirical evidence is needed on the role of parking provision in integrating micromobility with public transport. This study addresses this gap through a comprehensive analysis combining publicly available data on existing bike parking facilities at railway stations across Greater Sydney, demographic data from the latest Australian Census, and a cross-sectional survey of the general population (N = 1020). A mixed choice model is used to quantify the influence and elasticity of key factors shaping access mode choice. The findings indicate that greater provision of bike sheds and lockers is positively associated with cycling access to rail. The paper also identifies areas with higher predicted probabilities of using micromobility for rail access. Awareness of bike parking facilities is statistically associated with micromobility use, suggesting that awareness may be important alongside the availability of parking infrastructure. The findings provide insights for policymakers in Greater Sydney and other jurisdictions seeking to enhance rail–micromobility integration and advance sustainable transport outcomes.
Addressing the persistent spatial mismatch between electric vehicle (EV) charging supply and dynamic urban demand, this study proposes a novel, spatially-aware optimization framework coupling the Gaussian Two-Step Floating Catchment Area (Ga2SFCA) method, Geographically Weighted XGBoost (GeoXGBoost), and the NSGA-II multi-objective evolutionary algorithm. Taking the areas of Wuhan City and Changsha City as case studies, accessibility was rigorously quantified at a uniform H3 hexagonal grid scale, integrating dynamic daytime mobility heatmaps to effectively mitigate the Modifiable Areal Unit Problem (MAUP). The GeoXGBoost-SHAP semantic diagnostics uncovered intense cross-city spatial non-stationarity in built-environment determinants. Furthermore, a multi-scenario ablation study demonstrates that conventional unconstrained siting models are structurally flawed: ablating spatial equity penalties triggers a “central clustering” infrastructural concentration in core of the urban built-up area, while ignoring machine-learning-derived suitability weights leads to “blind gap-filling” and idle ghost stations in rural fringes. Guided by a dual-weight spatial tensor balancing equity urgency and environmental suitability, the optimized NSGA-II solver generated robust Pareto fronts across constrained resource budgets (Ψ=100∼1500). By extracting short-, medium-, and long-term phased deployment strategies, the coupled engine successfully navigated unviable “spatial investment traps” (e.g., hyper-congested cores) to reallocate infrastructure toward high-return fluid corridors and pericentral extended buffers. Ultimately, this framework provides precision-guided, context-aware decision support for developing equitable and financially resilient smart transportation ecosystems.
High-speed rail (HSR) stations are often accompanied by substantial changes in surrounding transport networks and built environment conditions, creating distinctive environments for nearby residents' daily mobility. Transit-oriented development (TOD) is often promoted around such hubs as a strategy to encourage transit-supportive environments, yet residents' actual public transit adoption in daily mobility remains a critical but underexplored behavioral dimension of such environments. Despite growing interest in HSR-centered TOD, limited attention has been paid to how residents’ everyday mobility is spatially organized in these station areas. Using anonymized mobile phone data from residents living around Beijing South Railway (BJS) station, this study examines spatial heterogeneity in residents' daily mobility patterns within a mature HSR-centered TOD and station-city integration environment and explores the factors associated with variations in public transit usage. Descriptive analyses show that travel time, distance, and activity areas are broadly similar across zones, while mode choice varies substantially. Residents living closer to the station exhibit lower public transit usage and higher car usage, and this distance gradient persists after accounting for built-environment characteristics. Spatial Durbin model results show that land use diversity and denser transit hub networks are associated with higher public transit usage, while excessive building density may reduce usage. These results indicate that proximity-related patterns and built-environment associations are not fully aligned within the mature HSR-centered TOD environment. Public transit usage is lower among residents living closer to the HSR station, while several built-environment characteristics remain associated with transit use in ways broadly consistent with conventional TOD expectations. These findings highlight the need for more context-sensitive and human-centered TOD planning in large-scale HSR station areas.
While electric vehicles (EVs) are promoted as low-carbon alternatives, evidence regarding their behavioral and spatial consequences remains limited. This study investigates these impacts using causal and spatial machine learning on 2020 travel diary data from Wuhan, China. We estimate the effect of EV ownership on daily vehicle kilometers traveled (VKT) while characterizing the non-linear, spatially heterogeneous influence of the built environment. Results from the causal model indicate that EV ownership is associated with an approximately 32% increase in VKT after accounting for confounders. Furthermore, electrification partly modifies established travel–built environment interactions: several built environment variables show nonlinear associations with VKT, and the car-reducing effects of compact development and public transit accessibility appear weaker among EV-owning households. The spatial model reveals a core-periphery pattern in two respects. First, EV-related VKT growth is concentrated in metropolitan peripheries, suggesting that reduced driving costs may support longer-distance travel and reinforce outward urban expansion. Second, the built environment–VKT relationship is not only nonlinear but also spatially heterogeneous along a core-periphery gradient. In particular, current built environment conditions in metropolitan fringe areas tend to further increase predicted EV-related VKT. Consequently, achieving sustainable transportation requires demand-side management tailored to spatial variations to complement technological substitution. By integrating causal and spatial machine learning, this study develops a framework that links treatment-effect estimation with local spatial diagnosis, enabling the identification of priority areas for built environment intervention and spatially differentiated planning policies.
Acai is a highly perishable fruit and historically a key component of Amazonian diets. From a decolonial perspective, açaí is a territorial and cultural cornerstone of Indigenous and riverside communities whose livelihoods depend on efficient access to markets. This study analyzes seasonal accessibility using three indices: two based on the available transportation network and one incorporating land use and land cover classifications. Potential routes were estimated between 103 production sites and nine commercial destinations in Mocajuba and Cametá, in both dry and wet seasons. Our methodology creates a very flexible approach for complex river systems that can be used to calculate river distances in an adaptive and efficient manner and that can be used in other regions of the world where rural communities must rely on rivers for transportation. Travel speed measures were weighted based on the channel hierarchy in the Lower Tocantins River basin, and were obtained through field measurements conducted via boat and car surveys by the National Institute for Space Research (INPE). To ensure the models remained effective, 23 field questionnaires were applied to better establish the parameters of transportation, logistics, and travel time between origin and destination. A multimodal transportation network framework was analyzed, integrating river and terrestrial routes. Additionally, an origin-destination matrix was generated along with the route's accessibility maps and graphs. The results highlighted Mocajuba's market with the highest accessibility index and Cametá's market with the lowest. The study ranks all 103 production sites (origin) according to their transportation accessibility across the accessibility methods.
