This paper presents a study regarding the factors affecting the maximum distance travelers are willing to walk to a bus stop. The study focuses on bus services provided at and walking routes towards bus stops. Travelers’ evaluations are collected using a stated preference experiment. Based on 7911 evaluations of 879 respondents, a latent class ordered logit model is estimated showing that both bus service and walking route related attributes significantly contribute to the maximum distance travelers are willing to walk from home to a bus stop. The parameter estimates show that the most influential attributes are the bus frequency at the bus stop and the presence of street lighting along the route between home and bus stop. The model also shows that based on the evaluations three different classes of respondents can be distinguished. Membership of a class depends on traveler related characteristics: gender, residential area, and bus use.

Even though COVID-19 no longer poses a significant threat to public health, it is crucial to reflect on this large-scale event to design equity and adaptability into services like public transportation systems for cities of the future. The case of Seoul presents a unique opportunity to analyse the pandemic's impact on transit ridership, serving as a natural experiment which is characterized by the implementation of a non-lockdown policy coupled with proactive transit management strategies. This study introduces a resilience index that magnifies the “unaveraged clues” of changes in Origin-Destination (OD) pairs to quantify the spatially unequal response of Seoul public transit use to external shocks from 2020 to 2023. Our findings reveal spatial heterogeneity in the resilience index of OD pairs and the dynamic change of related factors. OD pairs with high resilience during the outbreak were often associated with long-distance and labour-intensive industries, highlighting the need for transit systems to cater to “captive” travellers during the outbreaks. Despite overall ridership recovery in Seoul, factors like car ownership and the diversity of spatial functionality continued to influence patterns, which suggests that transit systems should focus on improving attractiveness to regain lost passengers post-pandemic. These insights are valuable for aligning policy with spatial and temporal dynamics to create equitable and sustainable public transportation systems.

Understanding how passengers perceive public transport interchanges is important to better explain current public transport mode and route choice behaviour and to better predict future demand levels. In this study we derive how passengers value a public transport interchange in a metropolitan context entirely based on recent, large-scale, Revealed Preference data, explicitly distinguishing between different types and modes of public transport interchanges. For this purpose we estimate three discrete choice models using maximum likelihood estimation, based on over 26,000 passenger route choices observed in June 2023 in the Greater London Area. We find that each public transport interchange is on average valued equivalent to 5 min uncrowded in-vehicle time. Additionally, our model results provide quantitative evidence that cross-platform interchanges between two metro journey legs are valued 20–25 % less negatively than a regular metro interchange where a level change is required. Multimodal bus-metro interchanges and out-of-station interchanges are perceived most negatively by passengers. Passengers value bus-bus interchanges on average about 60 % more negatively than metro-metro interchanges, possibly driven by factors such as comfort, service frequency, reliability and (perceived) safety. Our study results can be used for business case and appraisal purposes, when quantifying the impact of service changes which affect the number or type of interchanges.

Women in South-and Southeast-Asia encounter numerous barriers when traveling with informal and formal public transport modes. These barriers are often complex and rooted in societal norms. Previous works have predominantly focused on user satisfaction surveys to evaluate service quality of public transport journeys. There is a very limited understanding of women’s first-and-last mile trips, especially by income groups. The present study contributes to this knowledge gap by developing a detailed audit framework to evaluate whole public transport journeys, including first-and last-mile trips with intermediate public transport (informal) modes. Delhi, India is chosen as the case study city. The audit is used to evaluate 45 whole public transport journeys, with a variation in origin-destination pairs by income levels. In addition, anecdotal findings through conversations with female commuters enroute, from varying income levels, provide valuable insights into their constant awareness for personal safety and the detailed pre-planning undertaken to reach their destinations safely. It is evident from the results that low-income women are the most disadvantaged. Despite rides being subsidized by the government, they navigate poorly built environments and slow local bus services. Results also showed that first mile trips are the weakest links in the whole journeys. These findings provide evidence that despite attempts to improve the transport system, it remains inadequately designed for women, particularly those who are most vulnerable. The study concludes with recommendations for policymakers and practitioners.

To use public transit, riders must complete first and last mile trip segments. However, transportation planning measures of access to opportunity often assume that transit riders can complete first and last mile trips with ease. This paper contributes to the understanding of how disabled transit riders experience the first and last mile of public transit trips. Using a mobility justice framework, interviews with disabled transit riders reveal accessibility challenges along the first and last mile in Austin, TX and Seattle, WA. Participants in both Austin and Seattle faced myriad accessibility challenges along the first and last mile, indicating that transit network size and local politics did not influence travel experiences. Findings indicate disproportionate and intersectional barriers to accessing public transit in both cities, especially among female transit riders. I find that gender, social conditions, built environment quality, connectivity, and public engagement experiences influence access to transit for disabled people along the first and last mile. This work reveals how planners can better engage with disabled transit riders about their experiences and incorporate mobility justice goals to improve first and last mile accessibility.

Specific design features and functions on the platform and inside of the train carriage can significantly affect how efficiently and smoothly passengers can move in and out of the train. Industry follows the design standard and guidelines to satisfy the minimum requirement. However, the views of rail users are often overlooked and not well understood. This study fills the knowledge gap by investigating different platform and train carriage design features and understanding train passengers' perceptions and preferences for the proposed design. An online passenger survey targeting Melbourne Metro train users was conducted. By analyzing 429 valid responses, we gained insights into passengers' perceptions and preferences regarding train and platform design. A novel finding is that a vertical pole is the most preferred handhold type, while a rigid handrail is the least preferred handhold type. The 2+2 seating is voted as the most preferred interior layout, which is consistent with previous studies. The ranking on 2+2 seating layout is moderately associated with travel time and travel frequency, while the ranking on 2+3 seating is strongly associated with travel time. It is also found that the preferences for boarding sign and occupancy sign design are significantly associated with gender, age group, and travel frequency. Surprisingly, no statistically significant associations were found between any of the personal or travel variables with queuing sign preference, side seating ranking, or hanging strips ranking. The findings drawn from the analysis and the recommendation of this paper can provide valuable guidance for rail manufacturers in implementing design changes, as well as for service operators in adopting new strategies and interventions to enhance service quality and customer satisfaction.

The outbreak of COVID-19 caused unprecedented declines in public-transport use. As travel frequencies rebound, ridership is recovering, although it remains considerably below pre-pandemic levels. This study compares pre- to post-pandemic public-transit use among workers and non-workers, and the changing impact of local and regional accessibility. Additionally, we assess the impact of increased telecommuting on workers’ transit use before, during, and after the pandemic. We estimate two weighted multilevel linear regressions using a three-wave panel survey over the years 2019–2022 in Montréal, Canada. Results indicate that the factors that determine workers’ and non-workers’ transit patterns have tended to diverge after the pandemic. For workers, the relevance of accessibility in promoting utilitarian transit use considerably decreased, being responsible for close to 10% of the post-pandemic transit-use reduction. The increase of telecommuting frequency due to the pandemic contributed more than 10% of the post-pandemic transit-use reduction, but the effect of transit commuting time has remained relevant. For non-workers, the effect of regional accessibility by transit has increased after the pandemic, which has partly mitigated non-workers’ transit-use decline. Moreover, we find there is a joint effect of local and regional accessibility that has maintained after 2019 for non-workers. Results from this work have relevant implications for transit planners and policymakers. To help transit-use recovery, results suggest that providing good transit connection to the workplace promotes workers’ transit use, while promoting transit accessibility in lower-local-accessibility areas is key for non-worker transit ridership.

The world’s transportation system is overburdened by ever-growing travel demand, which brings mobility, safety, and pollution problems. To combat these issues and make better use of existing road capacity on urban arterials, public transit buses are designed to carry more people in fewer vehicles. However, transit buses travel slowly and make frequent intermittent stops, resulting in unreliable travel times and inconvenient riding experiences; this hinders people from choosing to travel on public transit buses. However, a research gap exists in using connected and automated vehicle (CAV) technologies specifically for transit bus operation optimization. To bridge this gap, the present study extended CAV applications to transit bus operation and developed a transit bus control method based on cooperative adaptive cruise control (CACC). The proposed model first optimized bus segment speeds to minimize schedule deviations and fuel consumption. Then, a CACC algorithm was integrated with optimal segment speed. The proposed control method was implemented through micro-simulations of an actual corridor in Jinan, Shandong, China. The evaluation results indicate that the proposed control method reduced the total arrival deviation up to 65.1% and total fuel consumption up to 6.8%. The study adapts CAV technologies to transit bus operation. The findings in this study validate CAV applications in transit bus operation.

Discussions of bus stop consolidation sometimes refer to average stop spacings, but there are no reliable statistics about spacings, nor methodologies for calculating them. This paper aims to clarify discussions of bus stop spacings by introducing clear definitions, a methodology for creating statistics from General Transit Feed Specification (GTFS) files, and a python package, gtfs-segments, which splits bus networks into isolated ‘segments.’ With the package, we calculate national-level statistics from 539 US transit providers and 83 Canadian providers, as well as agency-level statistics for 30 providers in the US, 10 in Canada, and a sample of 38 providers from other countries. Our estimates of US and Canadian mean spacings are both around 350 m (slightly wider than five stops per mile). US spacings are wider than sometimes claimed but narrower than those in other countries. Finally, the paper gives examples of metrics created by combining GTFS with data from other sources and proposes research ideas and applications to transit planning involving fine-grained stop spacing data.

Transit agencies conduct system-level ridership forecasting for planning, budgeting, and other administrative purposes. However, the COVID-19 pandemic introduced substantial changes in transit ridership levels and seasonal patterns, which has impacted the performance of ridership forecasting. Although time series methods are commonly used for forecasting transportation demand, they have received limited use in practice for public transit ridership forecasting. This study compares the performance of seven time series forecasting methods for predicting system-wide, monthly transit ridership for heavy rail agencies in the continental United States. The forecasting methods are: ETS, ARIMA, STL with ETS, STL with ARIMA, TBATS, a neural network, and a hybrid model. Ridership was forecasted for pre- and post-COVID periods (pre- and post- March 2020), as well as for the full series (January 2002 to December 2023). The MAPE and MASE were used to compare forecast performance. Using the pre-COVID period, 43% of the models produced a MAPE below 5% and 82% produced a MAPE below 10%. Using the full-series and post-COVID periods, only about 10% of the models produced a MAPE below 5% and half produced a MAPE below 10%. The classical and hybrid methods outperformed the other models using the full series period, and the TBATS, neural network, and hybrid methods outperformed the other methods using the post-COVID period. The findings suggest that even a few years into the post-COVID era, patterns that were typical of heavy rail ridership before the pandemic have not returned at most agencies in the United States, posing challenges to forecasting post-COVID ridership.