Search Results (147)

Smart-city last-mile rail access, referred to in this entry simply as last-mile access, captures how travelers connect to and from rail stations during the first or last leg of a journey. It encompasses both the design of multimodal connections and the experience of accessibility that results from them. On the supply side, last-mile access involves the coordination of walking, cycling, micromobility, and feeder transit with rail services, supported by digital systems that unify planning, ticketing, and payment. On the demand side, it reflects how efficiently and equitably travelers can reach stations within these coordinated networks. Together, these physical and institutional dimensions extend the functional reach of rail, reduce transfer barriers, and reinforce its role as the backbone of sustainable urban mobility. As cities strive to reduce car dependency while promoting inclusivity and accessibility, last-mile access has become a key indicator of how infrastructure, technology, and governance intersect to deliver more equitable transportation systems. Full article

This study analyzes the potential impact of integrating bicycles with the Quito Metro transport system to address connectivity barriers and improve urban mobility. Based on data from surveys carried out on students of the Central University, it was identified that a significant percentage of users face difficulties in accessing metro stations due to the lack of direct connections with other transport systems. In addition, the time savings by complementing access to the metro with bicycles were evaluated, suggesting that stations such as Quitumbe and Morán Valverde could particularly benefit from this strategy. Recommendations include the implementation of cycling infrastructure, awareness campaigns and integrated fare policies to maximise adoption of the system Full article

E-scooters have rapidly become a popular option for first- and last-mile mobility, yet their integration into urban transportation systems has raised significant safety concerns. This study investigates the feasibility of permitting E-scooter riding on sidewalks under controlled conditions to minimize pedestrian conflicts. Analysis of E-scooter crashes in Daejeon, South Korea, showed that 98.09% of crashes were caused by rider negligence, with “Failure to Fulfill Safe Driving Duty” as the leading factor. To investigate the applicability of safe sidewalk usage, a VR-based simulator experiment was conducted with 41 participants across four scenarios with varying sidewalk widths and pedestrian densities, under speed limits of 10, 15, and 20 km/h. Riding behaviors—including speed stability, braking, steering, and conflict frequency—and gaze behaviors were measured. Results showed that riding at 10 km/h improved riding stability and minimized conflicts. Regression analysis identified pedestrian density as the strongest predictor of conflicts, followed by sidewalk width and riding speed. These findings suggest specific policy needs: ensuring a minimum sidewalk width of 4 m for safe shared use, restricting operation to environments with low-to-moderate pedestrian density, and implementing a 10 km/h speed limit. This study provides evidence-based recommendations for safer integration of E-scooters into pedestrian environments. Full article

This paper investigates the spatiotemporal patterns and accessibility implications of shared e-scooter use in Tel Aviv, drawing on a complete year (2024) of trip-level data from all licensed providers. Shared micromobility services are often promoted as tools for reducing car dependency and improving urban accessibility, yet their actual usage patterns and equity outcomes remain underexplored, especially outside North America and Western Europe. This study aims to address this gap by integrating over 9 million reconstructed scooter trips with public transport accessibility data, local weather records, and institutional calendar effects. Multivariate regression was applied to quantify temporal and environmental determinants of demand, seasonal-trend decomposition to reveal cyclical usage patterns, and spatial analysis to assess whether scooters extend or reinforce existing mobility hierarchies. Findings indicate that scooter use in Tel Aviv is highly structured, peaking during afternoon hours, dropping during holidays and rain, and reflecting the weekly rhythms of the workweek in Tel Aviv. However, spatial patterns show a strong concentration of usage within already well-connected central areas, with limited activity in low-accessibility zones. These results suggest that shared e-scooters are not currently fulfilling their potential as first- or last-mile connectors; instead, they primarily serve as short-range, intra-core alternatives to walking. Full article

Urban air mobility (UAM) introduces electric vertical takeoff and landing (eVTOL) systems, creating new requirements for infrastructure planning. Vertiport siting is central, yet existing approaches such as multi-criteria decision analysis and optimization often rely on fixed criteria and seldom integrate ground transportation, which is critical for first- and last-mile access. Large language models (LLMs) show strong capabilities in reasoning and tool orchestration, but their role in siting tasks remains underexplored. This study proposes a strategy-aware LLM-based framework that connects heterogeneous spatial data with planning strategies expressed in natural language. A reflective loop connects the planner, executor, and validator for iterative refinement using two methods: multi-criteria decision analysis for interpretable mapping and a genetic algorithm for nonlinear optimization. Experiments in Los Angeles highlight both the potential and challenges of applying LLM agents to siting: outcome evaluation shows that strategies can be translated into distinct trade-offs, while process evaluation demonstrates the benefits of iterative refinement. The study suggests that LLM-based agents can formalize qualitative strategies into reproducible workflows, indicating their potential for UAM siting and promise for broader use in urban planning. Full article

The increasing variety in last-mile delivery demands requires diverse vehicle-drone collaboration models to meet various scenarios. Meanwhile, growing environmental concerns demand that we optimize not just delivery efficiency but also sustainability. This study thus proposes a unified multi-mode framework for collaborative multi-vehicle, multi-drone delivery networks to enable fair model comparisons. We introduce a hybrid metaheuristic algorithm combining NSGA-II and VND using specialized encoding and neighborhood structures to handle complex constraints, thereby comprehensively enhancing both efficiency and sustainability. Experiments on nine benchmark instances across three models reveal a nonlinear trade-off between efficiency and sustainability, with our migratory-relay model consistently outperforming others in terms of the Pareto front across multiple comparisons. Sensitivity analysis shows diminishing returns from adding more drones; while the first drone can cut emissions by up to 23.1%, additional drones bring progressively smaller reductions. These findings provide a strong framework and practical insights for designing sustainable urban logistics systems. Full article

With the rapid growth of e-commerce and rising demand for faster, reliable last mile delivery, optimizing the spatial layout of terminal logistics facilities is critical. This paper proposes a two-stage location framework for mini-depots and lockers considering spatiotemporal customer demand. In the first stage, Affinity Propagation (AP) clustering identifies candidate mini-depot locations and locker layouts based on temporal and spatial demand characteristics. In the second stage, an Adaptive Heuristic Electric Eel Foraging Optimization (AHEEFO) determines the optimal mini-depot location strategy to minimize total cost. A dataset of 1157 Beijing customer points, including latitude, longitude and demand information, is used for model validation. Results show that Scenario 2, with dispersed demand, outperforms Scenario 1 and traditional strategies in both total cost and customer satisfaction; dispersed demand can be effectively supported via crowdsourced delivery and locker layout, whereas concentrated demand requires more professional courier resources. Comparative experiments reveal AP clustering is more stable, reducing clustering-stage cost by 13.57% compared with K-means, and AHEEFO outperforms other algorithms in cost optimization, computational efficiency, and significance tests under random demand surges. Finally, the sensitivity analysis highlights the effects of different algorithmic and operational parameters, offering valuable insights for both managerial practice and academic research. Full article

Urban mobility in Bangkok is constrained by congestion, modal fragmentation, and gaps in First and Last Mile (FLM) access. This study develops a GIS-based framework that combines maximal-coverage location allocation with post-optimization accessibility diagnostics to inform intermodal hub siting. The network model compares one-, three-, and five-hub configurations using a 20 min coverage standard, and we conduct sensitivity tests at 15 and 25 min to assess robustness. Cumulative isochrones and qualitative overlays on BTS, MRT, SRT, Airport Rail Link, and principal water routes are used to interpret spatial balance, peripheral reach, and multimodal alignment. In the one-hub scenario, the model selects Pathum Wan as the optimal central node. Transitioning to a small multi-hub network improves geographic balance and reduces reliance on the urban core. The three-hub arrangement strengthens north–south accessibility but leaves the west bank comparatively underserved. The five-hub configuration is the most spatially balanced and network-consistent option, bridging the west bank and reinforcing rail interchange corridors while aligning proposed hubs with existing high-capacity lines and waterway anchors. Methodologically, the contribution is a transparent workflow that pairs coverage-based optimization with isochrone interpretation; substantively, the findings support decentralized, polycentric hub development as a practical pathway to enhance FLM connectivity within Bangkok’s current network structure. Key limitations include reliance on resident population weights that exclude floating or temporary populations, use of typical network conditions for travel times, a finite pre-screened candidate set, and the absence of explicit route choice and land-use intensity in the present phase. Full article

Rural communities face persistent transport disadvantages due to low population density, limited-service availability, and high operational costs, restricting access to essential services and exacerbating social inequality. Autonomous public transport systems offer a transformative solution by enabling flexible, cost-effective, and inclusive mobility options. This study investigates the operational determinants for autonomous road-based transit systems in rural and peri-urban South-East Queensland (SEQ), employing a structured survey of 273 residents and analytical approaches, including General Additive Model (GAM) and Extreme Gradient Boosting (XGBoost). The findings indicate that small shuttles suit flexible, non-routine trips, with leisure travelers showing the highest importance (Gain = 0.473) and university precincts demonstrating substantial influence (Gain = 0.253), both confirmed as significant predictors by GAM (EDF = 0.964 and EDF = 0.909, respectively). Minibus shuttles enhance first-mile and last-mile connectivity, driven primarily by leisure travelers (Gain = 0.275) and tourists (Gain = 0.199), with shopping trips identified as a significant non-linear predictor by GAM (EDF = 1.819). Standard-sized buses are optimal for high-capacity transport, particularly for school children (Gain = 0.427) and school trips (Gain = 0.148), with GAM confirming their significance (EDF = 1.963 and EDF = 0.834, respectively), demonstrating strong predictive accuracy. Hybrid models integrating autonomous and conventional buses are preferred over complete replacement, with autonomous taxis raising equity concerns for low-income individuals (Gain = 0.047, indicating limited positive influence). Integration with Mobility-as-a-Service platforms demonstrates strong, particularly for special events (Gain = 0.290) and leisure travelers (Gain = 0.252). These insights guide policymakers in designing autonomous road-based transit systems to improve rural connectivity and quality of life. Full article

Three-wheelers (3Ws—autos or auto-rickshaws) occupy a unique yet salient and substantive position within the context of India’s urban mobility. They provide critical first-and-last-mile connectivity, fill public transit coverage gaps, boost local and urban agglomeration economies, and are a major income source for millions. Their value and utility are especially pronounced in rapidly emerging Tier-II cities such as Amritsar. The city’s 7500-strong diesel 3W (d3W) fleet is the backbone of its transportation network but also contributes to air pollution. Though Amritsar’s favorable policies to transition the d3W fleet to electric (e3W) have reduced purchase costs by 40–60%, barriers remain. This study investigates the influence of the e3W user experience through a first-of-a-kind three-day pilot trial for ~300 d3W drivers. By leveraging a pre- and post-intervention framework combining surveys and trip diaries, this study evaluated how direct exposure influences adoption intentions, perceptions, and the social dynamics underpinning decision-making. In total, ~6% of participants switched to e3Ws following the trial, and there was a 20% drop in “don’t know” answers regarding charging duration and range. The results show non-random and meaningful shifts in attitudes, a greater awareness of range and charging times, improved views on charging convenience and vehicle safety, and air quality benefits. Full article

Multimodal transportation systems, particularly those combining metro and bikesharing, have become central to addressing the first- and last-mile connectivity challenges in urban environments. This study presents a comprehensive data-driven framework to analyze the spatiotemporal interplay between metro and dockless bikesharing usage using real-world data from Tianjin, China. Two primary methods are employed: K-means clustering is used to categorize metro stations and bike usage zones based on temporal demand features, and non-negative Tucker decomposition is applied to a three-way tensor (day, hour, station) to extract latent mobility modes. These modes capture recurrent commuting and leisure behaviors, and their alignment across modes is assessed using Jaccard similarity indices. Our findings reveal distinct usage typologies, including mismatched (misalignment of jobs and residences), employment-oriented, and comprehensive zones, and highlight strong temporal coordination between metro and bikesharing during peak hours, contrasted by spatial divergence during off-peak periods. The analysis also uncovers asymmetries in peripheral stations, suggesting differentiated planning needs. This framework offers a scalable and interpretable approach to mining multimodal travel patterns and provides practical implications for station-area design, dynamic bike rebalancing, and integrated mobility governance. The methodology and insights contribute to the broader effort of data-driven smart city planning, especially in rapidly urbanizing contexts. Full article

Advancements in multi-rotor quadcopter technology and sensing capabilities have led to their increased utilisation for last-mile delivery. However, battery capacity constraints limit their use in extended-distance delivery scenarios. A visual servoing implementation is first proposed that leverages a CUDA-accelerated tag detection algorithm for real-time pose estimation of the target. A new approach is then developed to enhance quadcopter package collection by implementing a control scheme to attenuate aggressive load-swing in a payload arm that shifts from horizontal to vertical after obtaining a vertically mounted payload. The motion of the payload arm imposes a shift in the system’s centre of mass, leading to a possible instability. A non-linear control scheme is then introduced to address this problem through attenuation of the residual energy from payload oscillation. The performance of the visual servoing approach is validated through both numerical simulations and a physical quadcopter implementation, along with the performance of the load-swing attenuation through numerical simulations. Full article

The importance of managing goods delivery in urban areas has reached its peak in recent years, driven by the constant and rapid growth of online commerce. Under such conditions, where smaller quantities of goods are ordered, yet the number of shipments continues to rise, the question of last-mile delivery (LMD) efficiency becomes increasingly relevant. This paper addresses the issue of last-mile delivery zone efficiency through the application of a new methodological approach. First, the concept of measuring last-mile delivery productivity is defined using a specific example from an urban environment. Next, Key Performance Indicators (KPIs) are established to enable a proper assessment of urban zone efficiency in line with the LMD concept. The main contribution of this study is the development of the IRN OWCM (Interval Rough Number Opinion Weight Criteria Method), which is used to calculate the weights of the criteria. To assess suitable delivery zones in terms of efficiency based on the defined KPIs, the previously developed IRN OWCM method is integrated with IRN AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization). The results identify delivery zones that are suitable in terms of meeting standardized user needs. The developed model demonstrated stability through additional verification tests and can be adequately applied in cases when it is needed to minimize subjectivity and uncertainties. Full article

Combining public transportation (PT) with Bike-Sharing Systems (BSSs) offers a pathway toward the sustainable development of urban mobility. These systems can reduce fuel consumption, air pollution, and street congestion, especially during peak hours. Moreover, PT and BSS are frequently used by individuals without access to private vehicles, including low-income groups and students. Whereas increasing PT network infrastructure is constrained by issues such as high capital costs and limited street space (which inhibits mass transit options like BRT or trams), BSS can be used as an adaptable and affordable solution to fill these gaps. In particular, BSS can facilitate the “first-mile–last-mile” legs of PT journeys. However, many transit agencies still rely on traditional joint service planning and overlook BSS as a critical mode in integrated travel chains. This paper proposes that PT and BSS be considered as a unified network and introduces a framework to assess whether access to this integrated system is equitably distributed across urban areas. The framework estimates demand for travel using public mobility options and supply at the level of Traffic Analysis Zones (TAZs), treating PT and BSS as complementary modes. Spatial accessibility analysis is employed to examine connectivity using factors that affect access to both PT and BSS. The proposed approach is tested by taking Tehran as the focus of the case analysis. The results identify the most accessible areas and highlight those that require improved PT-BSS integration. These findings provide policy-relevant suggestions to promote equity and efficiency in urban transport planning. The outcomes reveal that central TAZs in Tehran receive the highest level of PT-BSS integration, while the western and southern TAZs are in urgent need of adjustment to ensure better distribution of integrated public transportation and bike-sharing services. Full article

Urbanization and the rapid growth of e-commerce have significantly increased delivery volumes in cities, creating challenges in terms of cost, efficiency, and sustainability in last-mile delivery (LMD). To address these challenges, this paper proposes an innovative methodological framework for selecting optimal delivery strategies in urban environments, grounded in the principles of collaboration. The framework integrates an Extended FullEx method, developed to calculate criteria weights while accounting for expert reputation based on education and experience, with the MARCOS multi-criteria decision-making (MCDM) method used to rank delivery strategies. The Extended FullEx method proposed in this paper differs from the original FullEx by providing two improvements. The first concerns the introduction of the normalization procedure in the calculation of experts’ reputations, while the second addresses the different scoring of educational degrees, providing a more precise mathematical basis for the process. Four collaborative delivery strategies are evaluated against twelve sustainability-related criteria identified through an extensive literature review. The proposed framework is applied to a real-life case study in Novi Sad, Republic of Serbia. Results indicate that the most suitable delivery strategy is a hybrid model that combines the use of a consolidation center with smaller urban delivery hubs, providing practical insights for enhancing the sustainability and efficiency of urban delivery. This study contributes both methodologically, by advancing MCDM techniques, and practically, by offering decision-makers a comprehensive tool that integrates subjective expert knowledge and objective criteria assessment in the selection of sustainable LMD solutions. Full article