Journal of Public Transportation最新文献

In order to improve the performance of a public transport system, it is important to know alighting counts as well as boarding counts at bus stops. While boarding is almost always available through a fare collection system, public transport systems usually do not count alighting passengers. This is either due to the overhead that may be required for passengers at exit or the installation of relatively expensive Automatic Passenger Counters (APC) counters at each vehicle. Therefore, such expensive deployments are mostly not encountered in public-transport systems. In our research, for round trip lines that are balanced in daily passenger counts on both forward and backward routes, the alighting counts of a target route are inferred using only the daily boarding data. Vehicle occupancy levels are determined on a trip basis owing to the characteristic boarding pattern of each line. The validity of the proposed method was determined and verified using video recordings of arbitrarily selected trips. Consequently, it may be possible to modify scheduling algorithms to improve vehicle fleet utilization and increase passenger comfort in public transportation.

Increasing the operating speed in public transport systems can increase the system capacity, reduce the overall passenger travel time and improve experienced comfort. Skip-stop operation, where subsets of the trains operating on the same tracks skip certain intermediate stops, can accelerate the service and improve passengers’ overall travel experience. This paper considers the problem of deciding whether skip-stop operation is beneficial for a given line and which stopping scheme is the most effective. In particular, we investigate whether a simple decision rule for determining the stopping pattern under a skip-stop strategy, derived from the expected weighted time benefits to the passengers, can reliably determine the most suitable skip-stop scheme. To evaluate the impact of alternative stop-skipping strategies, we adopt the existing public transit assignment model BusMezzo, which allows for a realistic representation of passengers’ experienced waiting and in-vehicle travel times and the resulting trade-offs between passenger costs and benefits. The decision rule is applied to a set of high-frequency urban rail lines in Stockholm, Sweden. We show that a simple decision rule may not be a robust way of determining a beneficial skip-stop scheme. The results from the case study reveal that the skip-stop operation can have an overall positive impact on passenger generalized travel time but only under certain conditions at the stops along the line.

Fleet replacement is an important decision-making activity in public transit planning and operations. Recently, with the goal of reducing carbon dioxide emissions and achieving carbon neutrality, diesel buses are being replaced with new electric buses (EBs) in many cities around the world. The study proposes a methodology to analyze and compare the life-cycle cost and passenger waiting time of multi-type EBs, so as to assist transit agencies in determining the optimum EB type to replace existing diesel buses. The proposed methodology is applied to a real-life case study of a bus line in Dandong, China. The case study includes six types of EBs categorized by their charging technologies and passenger capacities, including three slow-charging EBs with capacities of 40, 60, and 76 passengers, as well as three fast-charging EBs with capacities of 50, 65, and 75 passengers. The results show that no matter which charging technology is considered, the life-cycle cost of large-size EBs is the lowest. From the perspective of passengers, small-size EBs resulting in shorter waiting time are more favorable. When passenger demands on all route segments change with the same rate, the total passenger waiting time for each vehicle type will not change. But, with the increase of passenger demand, the average passenger waiting time decreases for all vehicle types. The results also indicate that the bus line length does not have a significant impact on EB vehicle selection. In addition, the phase-out of the vehicle purchase subsidy policy will have no impact on EB type selection due to the current low-level purchase subsides.

Several studies highlight how the coronavirus (COVID-19) lockdowns have permanently changed the way people travel, but there has been less focus on impacts outside major metropolitan areas, where alternative transportation modes are more limited or scarce. This case study focuses on the long-term effects of the COVID-19 lockdowns on public transit and bikesharing using micro-level data in Lane County, Oregon. Lane County reflects the nearly 3,000 small U.S. counties that are a mixture of rural and urban landscape. Findings show that both transportation modes have begun to recover after large reductions in usage, though bikesharing seems to be less resilient relative to public transit 18-months post-lockdown. Additionally, findings show changes in bus ridership and bikeshare trips differed based on sociodemographic characteristics, though bikeshare trips were more sensitive to change. For example, there were statistically significant differences in bikeshare trips among tracts with a higher proportion of Black, Hispanic, or low-income residents, but no such differences for bus ridership. Given that small U.S. counties are typically less diverse, if not addressed, inequitable access could worsen in the long-run. Results from this case study could help inform other small U.S. counties, especially those that do not have the resources to collect micro-level data and are based around a university or where public transit is free for a large portion of the population, on future local policies regarding urban mobility and equity.

Several approaches have been proposed and adopted by researchers and decision-makers to improve and deal with public transport operation issues, especially travel demand management (TDM) measures. Disruptions like lockdowns provoked by weather conditions, political riots, special events, natural disaster issues, or the recent COVID-19 pandemic create a need for tools to manage public transport demand and supply o keep users circulating in an efficient, convenient and safe manner. Our work develops a simulation tool of the operations of a public transport system using smart card, GTFS and census data to evaluate the impacts of different intervention scenarios using the pandemic context as a case study. Using a pre-pandemic baseline scenario, we study the impact of several travel demand and public transport supply measures, focusing the analysis on waiting times and crowding conditions inside vehicles and platforms. As a result, we generate easy-to-analyze visual outputs that facilitate prioritizing actions at the metropolitan and district level, identifying where and when waiting times and crowding conditions would exceed certain thresholds.