Search Results (156)

Dynamic wireless power transfer can reduce electric vehicles’ charging downtime and range anxiety, but its benefits depend on route characteristics and system design. This work develops an integrated numerical framework combining (i) route-specific drive-cycle analysis, (ii) identification of candidate charging segments based on speed, stops and slope constraints, (iii) a physics-informed inductive wireless power transfer model and (iv) a Matlab/Simulink vehicle energy model to quantify energy demand, transferred energy and state-of-charge evolution. Two vehicle types (a passenger light-duty vehicle and a light commercial van) and multiple Lisbon Metropolitan Area routes are analyzed, including commuting, ride-hailing and urban logistics operations. Results show that low-speed, stop-rich urban corridors achieve the highest transfer rates (typically 0.4 kWh/km and over 2 kWh for more than 15 stops in the analyzed cases), whereas expressway deployments are much less effective (down to 0.1 kWh/km and 0.5 kWh below 5 stops) unless congestion lowers average speeds. The proposed workflow provides a replicable basis to identify candidate segments and to size wireless power transfer and corridor length for city-scale deployment scenarios. Full article

Driven by the global agenda of low-carbon urban development, local governments in China have implemented targeted policies requiring new energy vehicle adoption in the ride-hailing industry. This study focuses on a key issue in the development of sustainable smart public transportation systems: the factors affecting the acceptance of electric vehicles (EVs) in ride-hailing services among full-time and part-time drivers. Using 432 valid samples of ride-hailing drivers from Zhangzhou, a third-tier city in China, we compared the basic personal attributes of full-time and part-time drivers. Ordered logit models were developed to explore differences in factors influencing their acceptance of electric ride hailing (ER). Findings reveal: (1) Drivers’ perceived significance of EVs in green transportation is positively associated with their acceptance of ER. (2) Endurance mileage and charging efficiency have no significant effect on acceptance among drivers in underdeveloped cities. (3) Full-time drivers exhibit relatively low concern for subsidy policies, whereas part-time drivers express a pressing need for vehicle purchase subsidies and operational subsidies. (4) Overall, part-time drivers demonstrate higher acceptance of ER than full-time drivers. Based on these findings, this paper offers policy recommendations for governments to enhance ER acceptance among both driver groups. It is important to note that the present study utilizes survey data collected from Zhangzhou. The research conclusions should be treated with caution when applied to other cities, and further studies can be conducted in different regions to verify the results. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

Urban accessibility is commonly evaluated using static spatial indicators, which assume stable travel conditions throughout the day. Road congestion, network saturation, and service variability change the function and experience of the built environment (BE). This study tests the Temporal City Framework (TCF) by examining how time of day (TOD) reshapes urban accessibility and travel behaviour with varying levels of congestion. Using 30,288 trip records from the 2022 US National Household Travel Survey (NHTS), duration is operationalised as a sixth dimension of the BE. A time-normalised impedance metric, measured in minutes per mile (MPM), is used that captures realised congestion independently of distance. Temporal impedance (TI) varies strongly with TOD, with substantially higher MPM during peak and midday periods than at night. Compared with nighttime conditions, midday travel requires approximately 19% more time per mile. This indicates a measurable contraction in functional accessibility under identical BE conditions. The TI model outperforms duration-only models, with impedance remaining dominant when both measures are included. These results support interpreting duration as a structural dimension of urban accessibility. TI significantly increases the relative likelihood of active and public transport compared to private cars, even after accounting for absolute trip duration. Hired transport modes (taxi and ride-hailing services) are most prevalent at night, reflecting a greater reliance on on-demand services outside regular daytime schedules. This study tests duration as a structural dimension of the BE by operationalising time-normalised TI. Associations are interpreted as trip-level behavioural constraints rather than causal effects. Planning frameworks based on static travel times systematically misrepresent exposure, equity, and travel mode feasibility. Time-stratified accessibility metrics should therefore be integrated into transport and land-use evaluation and associated policies. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare. Full article

Over nearly two decades, ride-hailing has become a major component of urban travel, and its tendency to increase vehicle miles traveled (VMT) and worsen congestion is now well established. What remains poorly understood is why pooling, the most frequently proposed remedy, consistently falls short of theoretical expectations. With access to proprietary platform data still limited, high-fidelity simulation offers a promising path to untangle these dynamics. Here, we implement three pooling algorithms alongside a demand-following repositioning algorithm, within Berkeley Lab’s BEAM (Behavior, Energy, Autonomy, and Mobility), an open-source, agent-based regional transportation model. In a high ride-hailing adoption scenario for the San Francisco Bay Area, we find a counterintuitive result: the more stringently point-to-point pooling is promoted, the more detour burdens erode matching feasibility and reduce vehicle occupancy rather than increase it, thereby compounding rather than offsetting VMT and congestion impacts. Sensitivity analysis further identifies inflection points in pooling match rates and repositioning sensitivity beyond which deadheading and negative network feedbacks begin to dominate. These results show that pooled ride-hailing has a constrained ability to reduce network-wide impacts and that effective shared mobility requires treating pooling, repositioning, and fleet sizing as interdependent levers. Full article

Public transport is vital for social and economic life, but many people with disabilities still face exclusion due to both physical and psychosocial barriers. This study examined how psychosocial barriers influence public transport travel behavior among people with mobility, vision, and hearing disabilities in the City of Tshwane, South Africa. A quantitative survey was conducted using a structured questionnaire among 214 respondents. The results showed that fear of crime, lack of personal safety, anxiety when travelling alone or to unfamiliar places, and negative treatment by drivers and co-passengers are major deterrents to public transport use. Psychosocial barriers were significantly associated with travel behavior and a strong preference for private cars as well as ride-hailing services. Group comparisons revealed that individuals with vision disabilities experience significantly higher levels of transport-related fear compared to other groups. People with mobility and vision disabilities are more affected by negative attitudes from co-passengers compared to people with hearing disabilities. Psychosocial barriers are associated with low trip frequencies for non-essential activities, indicating suppressed travel. The study concludes that achieving inclusive urban mobility requires addressing psychosocial barriers alongside physical accessibility to ensure safe, dignified, and independent travel for people with disabilities. Full article

In the context in which on-demand mobility services are rapidly gaining popularity in the transportation sector, this article provides a literature review focusing on the emerging research topics related to ride-hailing. Based on a comprehensive review of the existing scientific literature, ten main research areas are identified, covering aspects ranging from operational algorithms to macro-level policy impacts enforced by local authorities. Each topic is discussed and analyzed based on available published research. This work analyzes state-of-the-art research directions such as demand forecasting, passenger–driver matching algorithms, pricing strategies, electric vehicle integration, trust and security aspects, quality of service and user satisfaction, integration with public transportation, and robotaxi integration. The solutions identified pave the way for new, evolving technologies related to on-demand mobility services and ride-hailing, a domain at the intersection of data science, artificial intelligence, and futuristic urban planning. Finally, the main results of this work are focused on the integration of AI, the optimization of the latency–security trade-off, and the development of unified global transportation standards that better address the balance between technological efficiency, sustainability, environmental protection, and social equity. Full article

This study investigates how algorithmic governance, a core feature of modern e-commerce platforms, impacts the consumption behavior of its service providers—specifically, ride-hailing drivers’ preference for high-calorie food. From an e-commerce ecosystem perspective, the dynamic interaction between platforms and their service providers is critical for long-term value co-creation and platform sustainability. By examining how algorithmic control mechanisms spill over into drivers’ off-platform behaviors, this research offers crucial insights for designing more sustainable and human-centric platform business models. Analyzing 710 survey responses from ride-hailing drivers in China via PLS-SEM, our findings reveal that algorithmic tracking evaluation and behavioral constraints are positively associated with high-calorie food consumption, with emotional exhaustion acting as a key mediator. Notably, standard guidance algorithms showed no significant effect. These results contribute to the e-commerce literature by demonstrating how platform-centric control can inadvertently lead to adverse externalities that may undermine service quality and provider well-being, ultimately posing a risk to the platform’s brand reputation and operational stability. We offer practical recommendations for e-commerce platform managers on optimizing algorithmic strategies to foster a healthier and more sustainable gig worker ecosystem. Full article

Deploying public EV charging infrastructure while balancing efficiency, equity, and implementation feasibility remains a key challenge for sustainable urban mobility. This study develops an integrated, grid-based planning framework for Wuhan that combines attention-enhanced ConvLSTM demand forecasting with a trajectory-derived, rank-based accessibility index to support equitable network expansion. Using large-scale charging-platform status observations and citywide ride-hailing mobility traces, we generate grid-level demand surfaces and an accessibility layer that helps reveal structurally connected yet underserved areas, including demand-sparse zones that may be overlooked by utilization-only planning. We screen feasible grid cells to construct a new-station candidate set and formulate expansion as a constrained three-objective optimization problem solved by NSGA-II: maximizing demand-weighted neighborhood service coverage, minimizing the Group Parity Gap between low-accessibility populations and the citywide population, and minimizing grid-connection friction proxied by road-network distance to the nearest power substation. Practical deployment plans for 15 and 30 sites are selected from the Pareto set using TOPSIS under an explicit weighting scheme. Benchmarking against random selection and single-objective greedy baselines under identical candidate pools, constraints, and evaluation metrics demonstrates a persistent coverage–equity–cost tension: coverage-driven heuristics improve demand capture but worsen parity, whereas equity-prioritizing strategies reduce gaps at the expense of coverage and feasibility. Full article

The resilience of urban transportation networks refers to the system’s ability to resist, absorb, and recover performance when facing external shocks. Traditional methods have obvious limitations in temporal granularity, data fusion, and predictive capabilities. To address this, this study proposes a minute-level real-time resilience measurement model driven by ride-hailing big data. First, the spatio-temporal characteristics of urban ride-hailing data are analyzed, and a transportation cost indicator is introduced to construct a multidimensional road network resilience measurement framework encompassing transport supply–demand, efficiency, and cost. Second, a high-precision hybrid LSTM-Transformer prediction model integrating spatio-temporal attention mechanism is developed, and a time-varying node identification method based on RMSE curves is proposed to accurately capture the disturbance onset time and recovery completion time. Finally, empirical validation shows that, taking Taixing City as an example, the model achieves minute-level resilience measurement with an average prediction accuracy of 96.8%, making resilience assessment more precise and sensitive. The research results provide a scientific basis for urban traffic management departments to formulate emergency response strategies and improve road network recovery efficiency. Full article

In the absence of system-wide planning and coordination, emerging mobility services have been integrated into urban transportation systems as independent network layers. Meanwhile, their interactions with traditional public transit give rise to complex self-organizing patterns in population mobility, manifested as coopetitive dynamics. To systematically analyze this phenomenon, this study constructs a four-layer temporal network—consisting of ride-hailing, metro, combined, and potential layers—based on a vectorized multilayer network model and inter-layer mapping relationships. An analytical framework is then developed using node strength, cosine similarity, and rich-club coefficients, along with two newly proposed indicators: the intermodal index and the node importance coefficient. The results reveal, for the first time, a spontaneously emergent intermodal phenomenon between ride-hailing and metro networks, manifested through both cross-day modal substitution and intra-day intermodal chains. The analysis further demonstrates that when sufficiently large and homogeneous demand cohorts are present, the phenomena can emerge even on non-working days. Based on the characteristics of this phenomenon, a method has been developed to identify intermodal nodes across different transport networks. Furthermore, the study uncovers a time-varying multicentric hierarchical structure within the metro network, characterized by small-scale core rich nodes and larger-scale secondary rich-node clusters. Overall, this study provides novel insights into the formation, coordination, and optimization of intermodal urban transport networks. Full article

Due to low passenger retention rates, autonomous Ride-hailing Vehicles (ARVs) face a critical bottleneck in commercialization, especially in the Chinese market. Based on 312 survey responses from Wuhan, this study systematically explored the mechanisms influencing customers’ continuance usage intention toward autonomous Ride-hailing Vehicles (ARVs), by integration of Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The empirical findings revealed that perceived usefulness, trust in technology, perceived value, perceived price fairness, and psychological ownership exert significant positive effects on sustainable usage intention, with trust in technology demonstrating the strongest direct effect. In contrast, concerns about safety equality demonstrate a significant negative impact. Trust in technology serves as an indirect mediator and emerges as a necessary condition in high-intention fsQCA configurations. Building on all insights, the study proposed a four-dimensional “Technology-Psychology-Safety-Economy” (TPSE) driving model, established a novel theoretical framework for user behavior research in intelligent transportation, and offered empirical guidance for differentiated corporate strategies and technology adoption. Full article

Robotaxi services represent a major step in the commercialization of autonomous driving, offering efficiency, consistency, and safety benefits. However, despite technological advances, their large-scale adoption is far from guaranteed. Most urban users already rely on mature ride-hailing platforms such as Didi and Uber, making the real behavioral question not whether to adopt Robotaxi, but whether to migrate from existing services. Prior studies based on TAM, UTAUT, or trust models have primarily examined users’ initial adoption decisions, overlooking the substitution behavior that better captures how people shift between competing mobility services in real contexts. This study addresses this gap by applying the Push–Pull–Mooring (PPM) framework to examine users’ migration from ride-hailing to Robotaxi services, based on survey data collected from 1206 respondents across four Chinese cities (Beijing, Shanghai, Guangzhou, and Wuhan). The model was tested using structural equation modeling and multi-group analysis (SEM–MGA). Push factors reflect negative experiences with ride-hailing, including social anxiety and insecurity caused by drivers’ behaviors; pull factors emphasize Robotaxi’ autonomy and service reliability; while mooring factors capture habitual ride-hailing use and perceived Robotaxi risk. Findings indicate that push and pull factors significantly promote migration intentions, whereas mooring factors hinder them. Among all factors, perceived risk exerted the strongest negative effect (β = −0.36), underscoring its critical role as a barrier to Robotaxi migration. Gender differences are also evident, with women more sensitive to risks and men more influenced by reliability. By situating adoption within a migration context, this study enriches high-risk innovation theory and offers practical guidance for designing gender-sensitive and user-specific promotion strategies. Full article