Journal of Public Transportation最新文献

The positive impact of coordinated timetable innovations throughout national railway networks has been shown exemplarily in the 1970 s and 80 s, when so-called integrated periodic timetables (IPT) were installed in the Netherlands and in Switzerland and then gradually improved. After large-scale changes of the former train offer, rail passenger demand increased significantly. A similar timetable innovation was recently decided for the German railway network. However, the project’s impact on overall demand is uncertain. To approach this question, an elasticity-based forecast of long-distance passenger demand is proposed and adopted to a modelized railway network section that has changed to an IPT. Massive travel time reductions turned out as the most important factor for demand growth followed by demand effects due to the increase of train frequency and changes of a modelized ticket price system. Additional factors influencing nationwide rail passenger demand are conceivable but difficult to generalize.

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.

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.

The COVID-19 pandemic highlighted transit's crucial role as a social service, ensuring access to essential destinations. Despite this, unprecedented ridership lows forced agencies to implement service cuts, disproportionately affecting essential workers and vulnerable populations. However, the full extent of these impacts remains underexplored. While existing literature examines transit agency responses during the pandemic, much of the focus has been on public health and safety measures, overlooking the specifics of service adjustment strategies implemented. This study contributes to our understanding of transit agency pandemic responses throughout the pre-, peak-, and post-pandemic phases by 1) characterizing patterns in transit service adjustments and 2) extending pandemic accessibility literature by examining job-specific impacts. The framework integrates time series clustering, qualitative review of agency press releases, and transit accessibility analysis, using only publicly available data. Through a case study of Bay Area Rapid Transit, we find distinct clusters of stations characterized by patterns in weekday morning peak service restoration and station area demographics. While impacts to accessibility varied by time of day, the relative ordering of accessibility levels across income and race/ethnicity remained consistent throughout the pandemic. These findings contribute to our understanding of service adaptation impacts and inform equitable response strategies for future service planning and disruptions.

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.

With the frequent global breakouts of infectious diseases such as Covid-19 and the likes, passengers feel unsafe traveling in crowded trains. The reluctance to share public transport with others due to the risk of disease transmission may lower the ridership as well as decrease the comfort level of passengers. Providing them with future crowdedness levels may allow them to plan accordingly, hence regaining the lost confidence and improving their patronage. This study explores the less frequently investigated relationship among occupancy levels at a particular station over several train runs, to predict the future occupancy level with a delay of one run (day). Tackling the issue as a classification problem rather than a regression problem, train occupancy data, station data, and weather data are merged to develop the final dataset. Training data is stepwise increased from 1 month to 3 months. Similarly, 1–5 days of known occupancy levels are added to each data instance. Among the three classifiers used, XGBoost provides the best results. Some practical challenges to occupancy level prediction are also discussed at the end.

Rural areas typically register low accessibility. This fact negatively affects their attractiveness, the well-being of their population in general, and population subgroups with limited access to private cars or strong space-time constraints (like minors, the elderly or members of large households). Collective autonomous vehicles (AVs) might improve this situation, e.g. by enhancing standard line-based services or introducing alternative shared schemes. Nevertheless, the collective usage of AVs in rural transport and their potential impacts on accessibility are still underexplored, with most research focused on the urban context. This study aims to fill this gap by analysing the public transport accessibility impacts that five alternative AV supply scenarios might generate in the rural valley of Mühlwald (South Tyrol, Italy). To this end, a variant of the standard space-time accessibility model developed by the authors is used. This focuses on accessibility by public transport specifically, and measures it to both fixed activities and discretionary opportunities. Accessibility impacts are first estimated at the person-based level for a sample of residents. Then, they are aggregated for the whole sample and six subgroups that tend to experience more substantial accessibility issues based on the literature. Results show that line-based AV applications provide limited accessibility benefits. Conversely, time-flexible applications like ride-shared vans or combinations of line-based trunks and on-demand feeders over peak and off-peak hours may provide the most evident advantages, especially for the subgroups with the tightest space-time schedules. Although these results do not reflect other possible impacts of AVs (e.g. environmental effects), they provide policymakers with valuable insights into the collective AV usages that could be most suitable in the rural context regarding person-based accessibility gains.

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.

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.