Search Results (542)

Pooled rideshare is considered an effective future travel mode for improving vehicle utilization and reducing congestion in urban transportation systems. However, its adoption remains limited due to insufficient passenger acceptance and uncertain economic benefits for transportation network companies (TNCs). The emergence of autonomous vehicles brings new momentum to pooled ridesharing services through centralized fleet management. Nevertheless, most existing studies examine traveler behavior and fleet operations separately, leaving the interaction between passenger preferences and operational strategies insufficiently represented. This study proposed an integrated behavioral–operational framework that jointly considers traveler choice behavior and fleet management decisions. An Integrated Choice and Latent Variable (ICLV) model is estimated using 8296 national survey responses collected in the United States in 2025 to capture post-pandemic traveler attitudes toward pooled rideshare. The behavioral model is embedded into a proactive assignment and repositioning strategy implemented on the POLARIS agent-based simulation platform. Simulation experiments are conducted in two urban networks, Greenville (SC) and Austin (TX), under multiple fleet size scenarios. Results show that the new pooling behavior model significantly increases pooling adoption compared with the previous mixed logit model, indicating that it better captures real-world traveler behavior. And the higher pooling adoption also reshapes the TNC trip structure in Greenville. Compared to the baseline in the POLARIS platform, the integrated framework increases pooling adoption and TNC profitability while reducing VMT, empty seat rates, and overall energy consumption. These findings provide insights for the sustainable deployment of pooled SAV services in heterogeneous urban environments. Full article

In order to maximize passenger travel satisfaction and enhance the sustainability of the intercity multimodal transportation system, this paper proposes a two-stage model for intercity multimodal timetable coordination optimization under uncertainty. In the first stage, a robust spatio-temporal graph is built to allocate intermodal passenger flows in order to determine passengers’ route selection results to minimize the total travel cost. At the same time, explicit capacity constraints and transfer behaviors are considered in order to be more realistic. In addition, passengers can take multiple transportation modes (High-speed Rail, Ordinary Rail, EMU, and Coach) in a single trip. The outputs of the first stage are subsequently integrated into the second-stage interval multi-objective timetable optimization model to determine departure times and stopping patterns under uncertain dwell and travel times. It is able to achieve the maximum reduction of passenger travelling time and waiting time within the minimum timetable adjustment, which further improves the integration level of transportation services. To ensure the diversity and convergence of model solving on the basis of retaining uncertain information, we propose an integrated algorithm PSO-IMOEA-MC involving Particle Swarm Optimization algorithm (PSO) and Interval Many-objective Evolutionary Algorithm combined with Monte Carlo (IMOEA-MC). Finally, the effectiveness of the proposed two-stage model and algorithm is validated using three intercity networks: Beijing–Zhangjiakou, Chengdu–Chongqing, and Guangzhou–Qingyuan. The results demonstrate the performance of the method in finding high-level solutions that retain more uncertainty. The findings of this study provide technical support for timetable adjustments under diverse operational scenarios. Full article

Urban passenger transportation contributes substantially to greenhouse gas emissions, yet the relative effectiveness of different decarbonization strategies remains difficult to assess due to inconsistent travel demand assumptions across studies. This study conducts a city-scale scenario analysis of daily passenger transportation CO₂ emissions for the Island of Montréal using a reconstructed representation of weekday passenger trips. An externally generated, survey-calibrated travel demand dataset is used as a fixed baseline, enabling consistent comparison across six decarbonization scenarios spanning vehicle electrification, modal shift, active travel substitution, and ride pooling. By holding daily travel demand constant, the analysis isolates the emissions impacts attributable to each intervention rather than to changes in mobility patterns. The scenario results represent upper-bound technical mitigation potential and provide system-level insight into how different strategies affect emissions across modes, vehicle categories, and network segments. The study demonstrates the value of city-scale scenario analysis for informing urban transport decarbonization under data-scarce conditions. Full article

The integration of electric micromobility into urban transportation systems can significantly reduce the energy consumption and emissions associated with short-distance travel. However, quantitative energy-based assessments remain limited, particularly in complex environments such as port–city interfaces. This paper presents a scenario-based energy assessment framework combining survey data and energy modelling. Empirical data were collected through a user survey (n = 138) targeting port workers and nearby residents, providing information on trip distances, travel frequency, modal choice, and willingness to shift from private car use. These data were combined with an energy modelling framework based on mode-specific energy intensity values expressed in kWh per passenger-kilometre. Three scenarios were analysed: a baseline scenario without intervention, a modal shift scenario supported by basic infrastructure measures, and an integrated scenario including transport management measures and local photovoltaic energy coupling. Results indicate that a moderate modal shift of 35% from private cars to electric micromobility for short-distance trips can generate aggregated annual energy savings of approximately 30 MWh and reduce CO₂ emissions by around 7 t per year across the analysed cases. According to the proposed energy model, electric micromobility achieves up to a 95% reduction in energy use per passenger-kilometre compared to private car travel. Furthermore, photovoltaic coupling could supply between 55% and 85% of the annual charging demand. The proposed framework is transparent and transferable, supporting energy-efficient and electrified future mobility planning. Full article

Fully integrated airport access requires managing many aspects from both the passengers’ and the operational point of view. It is noted that air passenger preferences, influenced by distance, time, and other travel-related factors, are one of the fundamentals for understanding airport choice within multi-region airport systems. Therefore, an online survey was conducted in Europe, collecting more than two thousand responses, from which passengers’ attitudes and motives for selecting airport access travel modes were obtained. On the basis of the mobility profile of respondents, Fuzzy C-means (FCM) clustering analysis was performed to identify segments with similar travel attributes. The outcomes of clustering were validated through the comparison between the FCM and K-means clustering algorithms. The results of the study showed that (i) the car was the most preferred mode of transport across different age groups, and (ii) waiting time, travel costs, and travel time were rated as important, with reliability identified as the most important factor when making travel mode choices. These findings may serve as a reference for improving multimodal airport access services and encouraging a shift from private to public transportation modes. Full article

This study investigates the spatiotemporal characteristics and travel patterns of micro-electric vehicles (micro-EVs) by analyzing real-world trip data collected over three years from shared micro-EV services operating in three regions of South Korea. Individual trips were extracted from GPS-based trajectory data, and a network-based detour ratio was introduced to capture non-linear trip characteristics. In addition, a hierarchical clustering analysis was applied to identify heterogeneous micro-EV trip patterns. The results show that micro-EVs are predominantly used for short-distance urban trips, while a smaller but behaviorally distinct subset of trips demonstrates their capacity to support medium-distance travel under specific functional contexts. The clustering analysis identified six distinct trip pattern groups, ranging from dominant short-distance routine travel to less frequent patterns associated with adverse weather conditions and extreme detouring behavior. Overall, the findings suggest that micro-EVs function as a complementary urban mobility mode, primarily supporting localized travel while selectively accommodating extended-range and specialized trips. From a sustainability perspective, these findings highlight the role of micro-EVs as energy-efficient, low-emission alternatives to conventional passenger vehicles for short- and medium-distance urban trips. By empirically identifying heterogeneous and long-tailed micro-EV travel patterns, this study provides practical insights for sustainable urban mobility design and environmentally responsible transportation policies. Full article

In medium-sized cities, daily travel often follows routine patterns, which may lead to suboptimal route choices. This study examines such trips and evaluates them to assess the influence of travel information. The research is motivated by the growing importance of sustainable urban mobility and the need to address traffic congestion, environmental concerns, and inefficient transportation choices in the city of Volos, Greece. To achieve that, a survey of two phases was performed. First, self-reported and GPS data of an examined group of 96 participants from the University of Thessaly, Volos, Greece, were collected. The data were used to evaluate the daily trips in terms of travel time, cost, and environmental friendliness. Second, a stated preference survey was designed, targeting motorized vehicle users of the examined group. The survey investigated the extent to which shared information on social media can be used to recommend a different route than the usual one or convince them to shift to a sustainable way of transportation. The analysis shows that travelers are more inclined to accept the recommended route after receiving travel information; however, this effect does not translate into choosing a sustainable mode of transport. We also found that women are more likely to change routes than men. Full article

The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have not yet been deployed. This study investigates the willingness to adopt AVs in a developing city where AVs have not been deployed yet. A comprehensive travel behavior questionnaire was conducted among local commuters in Alexandria, Egypt, to identify the influential variables affecting AV choice. The well-known machine learning classifier, Extreme Gradient Boosting (XGB), was employed to develop a forecasting model, which indicated a notable accuracy. The results indicated that trip cost was the most influential feature. On the other hand, there is a considerable level of mode captivity, since most travelers prefer to remain with their current mode, regardless of the effects of other variables. A significant share of travelers expressed concerns about shifting to AVs due to safety worries associated with the travel behavior of other transportation modes’ commuters. The analysis provides nuanced perspectives on the variables promoting modal shift toward the AVs, supporting future policies for smart urban mobility. Full article

To address the issues of inefficiency and high costs in obtaining data on the residential distribution of public transport passengers at present, this paper proposes an approach of “estimating the residential distribution of public transport passengers based on characteristics such as housing prices of residential Point of Interest (POI) and the convenience of public transport and its stops”. First, from two aspects—public transport travel and the selection of public transport stops—eight influencing factors for the selection of public transport stops during travel are identified. Based on these factors, a regression model for the number of public transport passengers from residential POI to their corresponding stops is constructed, through which the number of passengers traveling from each residential POI to all accessible public transport stops is obtained. This number is then used as a weight to allocate the actual passenger flow of each public transport stop to the respective residential POI, thereby realizing the estimation of the residential distribution of public transport passengers. Furthermore, this approach enables the estimation of the proportion of trips made from residential areas to specific public transport stops and the overall proportion of public transport trips among all travel modes from residential areas. The proposed estimation method is verified and evaluated using Shenzhen as a case study. Full article

This paper presents a methodological framework for inferring energy-related operating states of plug-in hybrid electric vehicles (PHEVs) under conditions of limited and incomplete on-board diagnostic (OBD) data. The proposed approach is illustrated using a single short real-world urban trip recorded for one PHEV operating in electric mode. Unsupervised clustering based on k-means is applied in progressively expanded state spaces (3D–5D) to decompose the driving process into physically interpretable operating states, despite the absence of direct measurements of key variables such as regenerative braking power. Cluster validity indices, per-cluster silhouette values, temporal segmentation, and robustness checks are employed to support the interpretability and internal consistency of the results. The study demonstrates that even a single, non-representative OBD time series contains sufficient internal structure to recover meaningful energy-related information when appropriate state-space decomposition is applied. While no statistical generalization is intended, the results highlight the potential of the proposed framework for analyzing real-world vehicle operation under constrained data availability. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

Transportation is the highest emitting sector in the US, and electrifying transportation is an effective way to reduce emissions. However, electrification efforts have typically focused on battery electric vehicles (BEVs); but extended-range EVs (EREVs), EVs with a backup gasoline generator, could play a major role. Nonetheless, reducing transportation-related costs and carbon emissions hinges on understanding how an EREV’s range and charging profile affect electric miles driven and, by extension, emission savings. This study evaluates the distribution of vehicle miles traveled (VMT) between electric and gasoline modes for EREVs across electric range (25–150 miles) and charging frequency scenarios. Using 2023 U.S. trip data by distance and monthly VMT benchmarks, we apply a dynamic mean-distance estimation method to match observed totals and allocate VMT to EV or gasoline power based on trip length. We explore different charging, efficiency, and cost scenarios. Our results show, at current average efficiencies, that EREVs with a 50-mile range (13.7 kWh battery) could electrify 73.3% of national VMT, while 150-mile range EVs could electrify 86.8% illustrating that there are diminishing returns at higher ranges. We also compute corresponding carbon emissions savings using national fuel economy and emissions factors. Results highlight the nonlinear trade-offs between range and emissions reduction. Findings suggest that expanding the EREV range significantly boosts electrification potential up to 100 miles but offers marginal gains beyond. However, if users charge infrequently, larger range EVs are needed to maintain the benefits of vehicle electrification. Our results imply that policymakers and manufacturers should prioritize moderate range EREVs for households who frequently charge (e.g., homeowners) and long range BEVs for infrequent users (e.g., apartment dwellers). Full article

Background: Understanding public acceptance of autonomous vehicles (AVs) is essential for cities transitioning toward smart mobility systems. Dubai aims to transform 25% of trips to autonomous mode by year 2030, yet little is known about residents’ readiness. Methods: An online survey (N = 302; 2024/2025) measured awareness, perceived benefits/risks, trust, cybersecurity concerns, and behavioral intention (BI). Constructs were analyzed using descriptive statistics and regression. Results: Cybersecurity concern was the strongest negative predictor of BI, while perceived usefulness (accident reduction) showed a weak, marginal positive effect. Gender, age, and cost effects were not statistically significant. Conclusions: Public acceptance is shaped more by trust, safety perception, and perceived system reliability than by demographics or cost. Policy actions should focus on transparent regulation, cybersecurity audits, and public AV pilots. Full article

The issue of CO₂ emissions and energy use is particularly important during short trips, where cold starts cause higher fuel consumption and increased emissions. These conditions, common in daily commuting, make vehicle efficiency a key concern. To reduce their impact, hybrid and electric powertrains have been introduced, allowing electric-only operation that eliminates direct tailpipe emissions, although indirect emissions from electricity generation remain. Real-world data show that hybrid vehicles often consume more fuel and emit more CO₂ than type-approval results indicate, mainly due to the medium battery state of charge (SOC), which forces the combustion engine to operate even over short distances. Additionally, engine thermal state and ambient temperature strongly influence energy use and emissions. This study fills a research gap by comparing vehicles with different powertrains under controlled chassis dynamometer conditions, analyzing fuel (energy) consumption and CO₂ emissions over the same driving cycle at various temperatures. The results show how temperature and thermal conditions affect total energy use and emissions over time and distance. The highest consumption and emissions during short trips were recorded for the plug-in hybrid vehicle in charge-sustaining mode at −6 ± 1 °C, while the electric vehicle showed the most favorable performance. Full article

Promoting cycling as a sustainable mode of transport is a pressing priority in contemporary urban mobility planning. This study examines the infrastructure characteristics that most strongly influence bicycle use in dense metropolitan contexts. A mixed-methods approach was adopted, combining a systematic review of current design guidelines with a large-scale empirical analysis of Barcelona’s Bicing bike-sharing system. The dataset comprised more than 54 million recorded trips, enabling the identification of the most and least frequented routes and the subsequent assessment of their infrastructural attributes. The results indicate that network configuration, continuity, and adaptation to topographic conditions have the greatest influence on cycling uptake. By contrast, factors frequently emphasized in design recommendations, such as lane width, were not decisive, as several of the city’s most intensively used corridors did not conform to these standards. These findings suggest that the expansion of network coverage and the improvement of route connectivity are more effective strategies for increasing cycling adoption than isolated design optimizations. This study contributes evidence-based guidance for urban planners and policy-makers seeking to advance cycling as a principal component of sustainable urban mobility in Barcelona and other comparable urban environments. Full article