Understanding travel patterns of ride-hailing service sub-population groups and effects of transit investment on ride-hailing users’ potential mode switching: A case study of Ho Chi Minh City, Vietnam

Ride-hailing services (especially motorcycle-based ride-hailing services -MBRH) have seen a boom in Vietnamese cities because these services can serve as a more efficient alternative to urban mobility. However, relatively little is known about travel patterns of sub-population groups of ride-hailing services, including the spatiotemporal demand, origin, and destination patterns of ride-hailing users, and the effects of transit investment on the mode switching from current ride-hailing users. This information is particularly important for the implementation of traffic management measures focusing on public transport, in light of concerns about the reverse side of the ride-hailing services, such as aggravating the traffic conditions and causing losses in the public transport market. In this paper, we present an in-depth analysis of the travel patterns of MBRH, based on large-scale household survey data collected in Ho Chi Minh City, Vietnam, with the statistical technique of Chi-square and Kruskal-Wallis tests. In addition, the independent and combined effects of Revealed Preference (RP) and Stated Preference (SP) data on the mode switch for MBRH users were studied using the Nested Logit models. The results indicate that speed and flexibility are seen as outstanding features of MBRH in attracting users. Furthermore, mode switch model estimation results show that traditional attributes (i.e., travel time and cost) and transit design factors (i.e., accessibility) are of lower importance to mode-switching behavior compared with sociodemographic factors. These findings suggest that MBRH services fill an important transportation niche and may affect the environment and transportation equity

Hierarchical Nearest Neighbor Gaussian Process models for discrete choice: Mode choice in New York City

Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision-making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable Hierarchical Nearest Neighbor Gaussian Process (HNNGP) model to account for spatially correlated unobservables in discrete choice analysis. Gaussian Processes (GPs) are often regarded as lacking interpretability due to their non-parametric nature. However, we demonstrate how to incorporate GPs directly into the latent utility specification to flexibly model spatially correlated unobserved effects without sacrificing structural economic interpretation. To empirically test our proposed HNNGP models, we analyze binary and multinomial mode choices for commuting to work in New York City. For the multinomial case, we formulate and estimate HNNGPs with and without independence from irrelevant alternatives (IIA). Building on the interpretability of our modeling strategy, we provide both point estimates and credible intervals for the value of travel time savings in NYC. Finally, we compare the results from all proposed specifications with those derived from a standard logit model and a probit model with spatially autocorrelated errors (SAE) to showcase how accounting for different sources of spatial correlation in discrete choice can significantly impact inference. We also show that the HNNGP models attain better out-of-sample prediction performance when compared to the logit and probit SAE models, especially in the multinomial case

Mind the Gap! Gender differences in the predictors of public transport usage intention

Public transport systems continue to gain ground as a cornerstone of sustainable urban mobility, offering alternatives to private car use, city congestion, and pollution. In this context, the shift toward regular public transport use seems influenced by several factors, with previous studies suggesting that safety concerns, service quality, and environmental value are key predictors of public transport usage intention. However, gender-based differences in travelers’ intentions and choices remain underexplored

The bus-ergonomics matrix for comprehensive evaluation of buses and bus drivers’ exposure

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Integrating Energy-Efficient Train Control in railway Vertical Alignment Optimization: A novel Mixed-Integer Linear Programming approach

Incorporating train control into the railway design process enables a practical and comprehensive evaluation of the lifecycle utility of a track profile. This paper proposes a novel integrated approach, termed EETC-VAO, which combines railway track Vertical Alignment Optimization (VAO) and Energy-Efficient Train Control (EETC). Initially formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, EETC-VAO aims to meet various geometric constraints and simultaneously minimize construction costs, traction energy consumption, and section running times in both directions. The model is subsequently reformulated into an equivalent Mixed-Integer Linear Programming (MILP) model using linearization methods and is further enhanced with valid inequalities, logic cuts, and a warm start algorithm with random velocity generation. The model has been extensively tested across a variety of case studies and train types, from synthetic small-scale scenarios to challenging real-world cases spanning from 3 to 71.2 km. Our findings demonstrate that operational costs can be significantly reduced with only marginal increases in construction costs. The integrated approach achieves reductions in total lifecycle costs of up to 40%, revealing a critical trade-off between construction and operational expenses. Notably, our results also indicate that lower construction costs do not inherently conflict with reduced operational costs, emphasizing the critical importance of integrating the train control scheme into the VAO problem

Unveiling overall satisfaction of metro: Integrating quantitative and qualitative approaches

This study aims to employ a novel hybrid approach based on partial least squares structural equation models (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to assess how satisfaction with different combinations of attributes is associated with high metro satisfaction by analyzing survey data from Nanjing. For daily commuting by metro, three stages are considered: the access stage, on-metro, and egress stage. Corresponding attributes related to these three stages are selected. The quantitative results demonstrate that all the selected attributes positively affect satisfaction. Qualitative analysis shows that satisfaction with egressing trips is a necessary condition for high overall satisfaction. Four configurations of satisfaction with attributes at different stages are sufficient to result in high overall satisfaction. Low satisfaction with certain attributes is offset by high satisfaction with others. Besides, to experience high overall satisfaction, commuters don’t need to experience high satisfaction with all attributes

Towards sustainable neighbourhoods? Tensions and heterogeneous transport priorities among suburban residents

A major challenge in North America’s car-centric suburbs is developing sustainable transportation strategies that align with residents’ diverse needs and preferences. Using a survey of 1,850 residents in Scarborough, an eastern suburb of Toronto, we used descriptive statistics and an exploded logit model to identify which environmental factors, sociodemographic characteristics, travel behaviors, political values, mobility options and transport barriers, and aspirations influence residents’ transport priorities in terms of space and investment. Overall, transit investments are considered the top priority, followed by walking, driving, and cycling, with clear neighbourhood-specific variations. Newcomers, older adults, and racialized groups prefer sustainable transport options, while women, white and right-wing individuals prioritize car investment. Moreover, transport priorities are closely linked to people’s lifestyles and neighborhood aspirations, as reflected in the destinations they want near their homes. These findings enhance our understanding of transportation preferences and offer valuable insights for developing effective, context-specific sustainable transportation strategies

Investigating Indian Commuters’ Perceived Crime Risk on Autonomous Public Buses and Ride-Pooling Services

Autonomous vehicle technologies are anticipated to transform road transportation systems, promising enhanced traffic safety and efficiency across different modes, including public buses (PB) and ride-pooling services (RPS). However, in India, there is a growing security concern/fear of crime concerning conventional PB and RPS because of the recent rise in crimes committed on them. Moreover, the introduction of driverless modes of PB and RPS may further heighten commuters’ crime concerns on such services because of the absence of a driver. Thus, this study investigates the acceptance of autonomous public buses (APB) and autonomous ride-pooling services (ARPS), as well as how commuters’ characteristics influence the perceived risks of crime and victimization and their willingness to use the modes. To achieve this, a stated preference survey was designed and conducted across India. The survey resulted in 732 complete responses. The results show that socioeconomic attributes, vehicle automation, and security-related measures significantly influence commuters’ perceived fear of crime and willingness to use APB and ARPS in India. More specifically, young commuters demonstrate higher willingness to use APB and ARPS, while females exhibit lower willingness to use APB and ARPS. In addition, the presence of a security officer on these modes decreases commuters’ concerns about crime. Moreover, travel distance is positively associated with commuters’ perceived level of crime and victimization, while it has a negative relationship with their unwillingness to use APB and ARPS. APB and ARPS are yet to be introduced in India, and Indian commuters have not experienced the security concerns associated with them; thus, the results of this study can serve as the base for guideline formulation for security concerns in India. Based on the results of this study, a set of policy implications, such as female-only transit units, enhancing security measures on the automated modes, and design framework and infrastructure, were proposed. These policy implications can be instrumental in increasing the acceptability of APB and ARPS in India and other countries with similar characteristics

Energy-efficient multi-curve optimization in urban rail transit: Stability enhancement under operational uncertainties and curve adjustments

The multi-curve optimization problem involves selecting train speed curves for nominal timetables and configuring candidate curves embedded in the Automatic Train Operation (ATO) system for train rescheduling. In practice, train speed curves planned under nominal conditions are frequently disrupted by uncertainties such as delays and fluctuations in passenger flow, which may require rescheduling, where the actual speed curves can only be selected from the candidate train speed curves. This rescheduling process leads to deviations between rescheduled (actual) and nominal energy performance. Existing research has not fully addressed the impact of rescheduling on energy consumption from a planning perspective, a critical gap for improving the efficiency of energy-efficient timetables under uncertainty. To fill this gap, we define the stability of energy-efficient train timetables as a quantifiable metric, assessing deviations in terms of both energy reduction and delay control. To minimize actual energy consumption, this study incorporates stability-based constraints into a two-stage stochastic programming model, combining an energy-efficient scheduling stage with a bi-level programming stage for speed curve rescheduling, which introduces nonlinear complexities. Two logic-based Benders decomposition algorithms, including a novel multi-scenario dynamic programming method, solve the model. Using actual data from the Beijing Yizhuang Line, we conducted two sets of numerical experiments to validate the performance of the model and algorithms. Compared to a benchmark two-stage model without optimizing the candidate train speed curves, our approach achieves average stability improvements of 2.74% for in-sample tests and 2.40% for out-of-sample tests, with gains surpassing 4.00% under more challenging delay scenarios, alongside reductions in energy consumption

Enhancing public transportation sustainability: Insights from electric bus scheduling and charge optimization

This study presents a joint optimization model for optimizing the scheduling and charging of electric buses in urban transit systems, integrating fleet size determination, trip scheduling, and charging infrastructure planning. The model is solved using a genetic algorithm and validated through constrained particle swarm optimization. Results demonstrate that by efficiently incorporating time-of-use pricing, optimized partial charging, and dynamic speed variations, the model achieves a 2.5% cost reduction compared to full charging and improves operational efficiency by over 7% within changing speed scenarios. Sensitivity analyses confirm the model’s robustness, identifying the minimum charge duration of 15 min and discharge depth of 90% as economically optimal. The study provides valuable insights for transit agencies seeking to optimize electric bus fleet operations and transition to more sustainable and cost-effective public transportation