Journal of Public Transportation最新文献

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.

Fare-free transit policy is not new to several public transit systems and communities in the U.S., as some local transit agencies have begun implementing fare-free transit policies or variations of them since the 1960s. Over time, the discussion regarding fare-free transit has been reignited by decreasing ridership trends in recent years and other thematic inquiries surrounding access, mobility and equity, operational efficiency, agency financial health, and community impacts. This research empirically investigates the effects of fare-free policy on transit ridership, labor force participation and income inequality. Using panel data regression models, we draw several conclusions: 1) Fare-free transit significantly increases ridership. 2) Fare-free transit neither significantly increases labor force participation rate nor reduces income inequality in small and medium-sized urbans. 3) Fare policy aside, external factors such as increased household income and work-from-home significantly reduce the demand for transit in small-urbanized areas.

This study aims to assess the public acceptance of Autonomous Modular Transit (AMT) and identify key factors influencing people’s intentions regarding the future use of AMT. While the integration of autonomy and modularity in transport systems has gained attention in recent years among researchers, the study of AMT acceptance is non-existent in the literature. To address this research gap, the study proposes an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model to explore public acceptance of AMT. It integrates the original model’s constructs with two additional factors: trust and perceived green usefulness, while also investigating the role of public transport usage behavior in intention. Data were collected through an online survey involving 1662 participants from different cities in Iran. Structural equation modelling was used to evaluate the conceptual model. Multiple t-tests and ANOVA tests were conducted to evaluate the effects of public transport usage behavior and demographics on the model’s constructs. The results showed that the majority of respondents showed their acceptance toward using AMT when available despite their limited prior knowledge about the system. Perceived usefulness was the strongest predictor of intention, followed by social influence and hedonic motivations. Although the COVID-19 pandemic significantly reduced the number of public transport (PT) regular users, this group showed a higher intention to use AMT compared to PT non-regular users. Furthermore, respondents aged over 60 years stated a lower intention to use AMT than their younger counterparts. Given that the mass adoption of AMT depends on public acceptance, this study is expected to serve as a benchmark for comparing countries with similar cultural contexts.

This study analyzes the use of ride-hailing and bus services from a multi-modal user perspective by jointly modeling individuals’ monthly frequency of use of both bus and ride-hailing services. A bivariate ordered probit model is estimated to capture the influence of socio-demographic and attitudinal characteristics as well as unobserved factors that may simultaneously influence the travel frequency by both modes. We use data collected in a survey of travelers from Porto Alegre, Brazil. The results characterize the profile of local frequent ride-hailing users as young, medium-income, self-employed individuals with a propensity toward the use of technology and a low inclination for car ownership. Most importantly, we observe that after controlling for unobserved factors that simultaneously contribute to higher trip frequencies by both modes, frequent bus users demonstrate a lower propensity toward ride-hailing use.

As the airspace is increasingly gaining importance as a new frontier to improve urban mobility, aerial cable cars are being discussed and already appropriately implemented worldwide to supplement conventional modes of transport in urban areas. Transit planners and designers should carefully consider the interoperability and integration of cable car services with conventional modes of transport. In particular, excessive delays and overcrowding conditions due to deficits in interoperability should be avoided. This challenge of interoperability arises as conventional modes of transport operate predominantly on a timetable, and most cable car technologies operate in such close headways that they can be considered as almost continuous conveyors. The advantage of having almost always a transport vessel of a cable car ready for boarding ceases when large volumes of passengers arrive in batches, for example from higher-capacity modes of transport or at large events, resulting in long queues. Traditional manuals do not yet reflect these aspects of interoperability adequately. Consequently, this work filled this research gap about the interoperability of cable cars related to handling high volumes of incoming passenger arrivals that transfer in larger batches from feeder modes and often result in queues at cable car stations. The following objectives were targeted: (1) determine passenger capacity limits of conventional modes of transport acting as feeders to cable cars and (2) specify space requirements to be provided due to the potential queues that arise. To answer these, methods of Queuing Theory were used and results were placed in Levels of Services of traditional manuals. Key performance indicators included queue length, waiting time, and corresponding queue space. The results revealed that cable cars can be a useful complement to public transit but are of limited feasibility due to cumulative queues at arrival rates with larger crowds. High-capacity feeder modes (e.g., commuter rails) are limited to 20-minute headways depending on cable car technologies. Further, queuing areas of up to 1000 square meters (around 10,800 ft²) should be considered. Several operational limitations are presented as guidance for practitioners and policymakers.

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.

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.

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.

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.