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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How do access and spatial dependency shape metro passenger flows

Spatial imbalances in metro ridership significantly reduce the overall efficiency of metro system. Understanding the factors that contribute to metro ridership is essential for developing targeted strategies to improve ridership equity and overall system performance. This study introduces novel spatial dependency indices based on spatial weight matrices and land-use function complementarity to explore how access and inter-station spatial dependency affect metro ridership, focusing on station-level boardings and alightings, as well as station-to-station flows. Using the data from the Xi\u27an Metro, the findings indicate that access to employment and residence from metro stations considerably enhances station-level boardings and alightings. Walking access emerges as a critical factor, especially in the context of station-to-station travel. Furthermore, the analysis reveals a complementarity feature within the metro system, where increases in boardings (alightings) at one station leads to a higher demand at others. Stations that serve areas with complementary land-use functions tend to attract more travel between them. These findings emphasize the critical role of access and spatial dependency in enhancing transit planning and system efficiency

Exploring nonlinear and interaction effects of TOD on housing rents using XGBoost

Understanding the relationship between transit-oriented development (TOD) and housing rents is crucial for formulating effective TOD strategies and optimizing housing market management. These strategies contribute to a healthy housing market and sustainable urban development. Traditional regression models used in existing studies often fail to capture the nonlinear, and interaction effects of TOD on housing rents. This study addresses these limitations by applying the eXtreme Gradient Boosting (XGBoost) algorithm combined with Shapley Additive Explanations (SHAP) analysis to evaluate the effects of TOD on housing rents within Wuhan\u27s Third Ring Road. Our approach not only identifies key TOD factors such as overall walkability, parking lot density, and commercial density but also uncovers significant nonlinear and threshold effects on housing rents. Moreover, we reveal the intricate interaction effects among key TOD variables, demonstrating how the local impact of one factor can be amplified or diminished by changes in another. This study provides novel insights into the complex mechanisms of TOD impacts on housing rents and offers actionable guidance for crafting targeted urban development strategies that promote urban equity and foster a sustainable housing market

Relationship between shared micromobility and public transit: The differences between shared bikes and shared E-bikes

Extensive research has been conducted on the usage patterns and potential impacts of shared micromobility, yet the distinct relationships with public transit between shared bikes and shared E-bikes – the two main micromobility modes in China – remain unexplored. Examining the potentially distinct modal shift patterns away from public transit is essential to understand the landscape of different micromobility modes and their different disruptions to traditional transportation modes. To bridge this gap, this study analyzed shared micromobility trip data from Ningbo, China, aiming to quantify the relationship between shared micromobility and public transit, and differentiate between the interactions of shared bikes and E-bikes with public transit. We employed a geospatial-based approach to categorize each shared micromobility trip into three types: Modal Substitution (MS), Modal Integration (MI), and Modal Complementation (MC), based on their interactions with buses and subways. Then we explored the spatial and temporal patterns of the shares of MS, MI, and MC trips, and investigated factors influencing these varied relationships using Spatial Autoregressive (SAR) models. Our findings indicate that shared E-bikes more frequently substitute for public transit, whereas shared bikes are predominantly used in MC roles. There are notable temporal and spatial variations in the usage of shared E-bikes and bikes: temporally, there is a morning peak of shared E-bikes that substitute public transit, and spatially, E-bike sharing has a higher concentration of substitution in suburbs while bike sharing has a higher concentration of complementation in the outer areas. The observed differences between E-bikes and bikes regarding their relationship with public transit are largely influenced by trip distance, speed, and public transit characteristics. This study highlights the importance of recognizing the diverse interactions between different shared micromobility modes and public transit, and sheds light on the development and management of shared micromobility and public transit systems

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

Exploring the factors shaping attitudes and intentions towards automated buses: Empirical evidence from Northeast England

This paper presents an insightful journey into understanding how travellers in the Northeast England perceive and interact with both conventional and emerging automated bus services. Employing a comprehensive methodology, our research scrutinizes data collected from 417 regional respondents via online questionnaires, integrating a blend of quantitative, qualitative, and spatial data points. This approach not only uncovers nuanced attitudes towards public transportation but also sheds light on the potential implications and acceptance of automated bus (AB) services, a fundamental element in shaping the future of urban mobility. The narrative aimed at (i) identifying present socio-demographic clusters and their travel behaviour, (ii) analysing perceptions regarding public transport with an emphasis on bus services, (iii) discovering attitudes towards AB systems and (iv) establishing the likelihood of a travel shift towards ABs. The quantitative analysis applied Multiple Correspondence Analysis, unveiling five distinct socio-demographic clusters: (1) full-time employed, car-dependent individuals; (2) flexibly working individuals with mixed travel modes; (3) retired, car-dependent persons; (4) unemployed individuals primarily relying on walking and bus services; and (5) employed students utilizing various public transport and active travel modes. Furthermore, Categorical Principal Component Analysis revealed four key attitudinal components influencing bus service perceptions: (a) safety, quality, and comfort; (b) accessibility and availability; (c) cost and reliability; and (d) punctuality and preferences for alternatives. The qualitative dimension covered content and sentiment analysis on responses to open-ended questions about automated buses. This analysis highlighted mixed sentiments, with 56 % positive and 44 % negative mentions. Key connotations with automated buses included expectations of reduced accidents and safer roads, enhanced reliability and frequency of services, potential job losses, diminished social interaction, and concerns over personal security. The study culminates in a spatial analysis, synthesizing the socio-demographic clusters, attitudinal components, and a comparative assessment of traditional versus automated bus services

Towards a better understanding of changes in cost per riders for bus routes before and after the COVID-19 pandemic in Montréal, Canada

The COVID-19 pandemic has severely impacted the finance of transit agencies by reducing farebox revenues. Combined changes in ridership and service operation levels have further transformed the financial efficiency of public-transit services. Understanding how these changes vary between routes is crucial to inform service optimization processes to reduce transit agencies\u27 operational deficits. Using data from the bus network in Montréal, Canada, for 2019 and 2022, we assessed changes in cost per rider at the route-level before and right after the COVID-19 pandemic. We categorized daytime multi-stops bus routes (N = 184) based on the income of the areas they served and their cost per rider across both years to assess diverging temporal and spatial patterns. Our results highlighted that high cost per rider routes were mostly located in the periphery of the study area and in the downtown core and that such patterns worsened following the pandemic, particularly for the downtown core. We observed that routes which served higher income areas tended to have higher cost per rider on average than middle- or low-income ones. We further confirmed this finding by categorizing bus routes by their cost per rider, finding that high cost routes in both 2019 and 2022 tended to be serving higher income areas than other routes. The consideration of both temporal, spatial and socio-economic variation of the cost of bus services provides nuance insight to transportation planners as they aim to optimize bus services while being mindful of potential ridership loss and vertical equity issues

What drives inequalities in Low Emission Zones’ impacts on job accessibility?

Low-emission zones (LEZs) aim to improve urban air quality and reduce emissions but often face public opposition due to their regressive impacts on accessibility. However, the causes of these regressive impacts remain poorly understood. This study investigates the factors driving inequalities in the impacts of LEZs on job accessibility across occupational categories in eight French cities. Using ex-ante open-source data, it computes expected job accessibility losses due to LEZs per occupational category. Additionally, it provides a counterfactual decomposition of the disparities in LEZs’ impacts between six drivers: ownership of polluting vehicles, workers’ residences and workplaces within the LEZ, accessibility of workers’ homes and workplaces via public transportation, and feasibility of active transportation modes for commuting between homes and workplaces. The findings reveal that LEZs are predominantly regressive in six out of the eight cities examined. Despite a higher concentration of high-income workers and jobs within LEZs, resulting in significant accessibility losses for this group, low-income workers bear a greater burden due to the limited availability of public transportation near their residences and workplaces, longer commutes to work, and higher shares of polluting vehicles. These findings help inform potential complementary policies to address the regressive effects of LEZs