Investigating the Spatiotemporal Imbalance of Accessibility to Demand Responsive Transit (DRT) Service for People with Disabilities: Explanatory Case Study in South Korea

This study analyzed the operation-related historical data of the call taxi service for disabled people in Seoul, South Korea. The study investigated how unevenly distributed the accessibility of disabled people to transportation is in terms of time and space. In addition, the reasons that cause imbalanced accessibility were investigated in areas with good and poor accessibility. Accessibility was defined as how quickly call taxi services for the disabled are available at specific times and locations. For the analysis, the log data for tracking the status of taxis in time and space were processed to calculate their availability, an index that reflects the dwelling time and the number of taxis available at a specific time and in a specific area. This index was divided into time and space and used as a surrogate measure to assess accessibility. The results showed that there were spatial and temporal accessibility imbalances in demand responsive transit (DRT) service. The insufficient supply during the night resulting from the current DRT operating schedule has reduced the accessibility of call taxis for the disabled, and the concentration of drivers’ breaks also affected the accessibility of service during the daytime. This suggests the need for (1) an increase in supply and (2) evenly distributed breaks for the drivers. In terms of space, the outer areas of Seoul generally were found to be more accessible than the central areas. In addition, areas near depots that serve as hubs and resting places for taxi drivers, areas with excellent medical infrastructures for people with disabilities, and areas with good traffic environments tended to have good accessibility; this suggests the need to reallocate garages and improve the traffic environments to improve accessibility.

Demand Responsive Transit Simulation of Wayne County, Michigan

Demand responsive transit (DRT) can provide an alternative to private cars and complement existing public transport services. However, the successful implementation of DRT services remains a challenge as both researchers and policy makers can struggle to determine what sorts of places or cities are suitable for it. Research into car-dependent cities with poor transit accessibility is sparse. This study addresses this problem, investigating the potential of DRT service in Wayne County, U.S.A., whose dominant travel mode is private car. Using an agent-based approach, DRT is simulated as a new mobility option for this region, thereby providing insights into its impact on operational, user, and system-level performance indicators. DRT scenarios are tested for different fleet sizes, vehicle occupancy, and cost policies. The results show that a DRT service in Wayne County has a certain potential, especially to increase the mobility of lower-income individuals. However, introducing the service may slightly increase the overall vehicle kilometers traveled. Specific changes in service characteristics, like service area, pricing structure, or preemptive relocation of vehicles, might be needed to fully realize the potential of pooling riders in the proposed DRT service. The authors hope that this study serves as a starting point for understanding the impacts and potential benefits of DRT in Wayne County and similar low-density and car-dependent urban areas, as well as the service parameters needed for its successful implementation.

Path-Based Dynamic Vehicle Dispatch Strategy for Demand Responsive Transit Systems

Demand responsive transit (DRT) has the potential to provide passengers with higher accessibility and lower travel time as compared with conventional transit, and at the same time make more efficient use of vehicle capacity than traditional taxi. In many current systems, vehicles are assigned to passengers along travel paths that are chosen myopically. When information on future demand distribution is available, it would be more beneficial to dispatch transit vehicles strategically to areas with a higher probability of generating passengers. This paper proposes a mathematical model for a dynamic DRT vehicle dispatch problem. It determines in real time how the operating vehicles shall be used to serve arriving passenger demand, and which paths the vehicles should choose to achieve a balance between operator and passenger costs. The model is solved by an approximate dynamic programming (ADP) based solution approach. Case studies, including a hypothetical numerical example and a real-world case in Qingdao, China, have been conducted to demonstrate the applicability of the proposed modeling framework. Results show that the proposed ADP solution can significantly improve the overall system performance as compared with myopic benchmarks.

Evaluating demand responsive transit services using a density-based trip rate metric

Demand responsive transit (DRT) is attracting increased attention as a means to provide public transit to low-density populations. This research aims to provide a suite of evaluation metrics with low data requirement and widespread availability, so that operators, funders, regulators, and practitioners can better evaluate the performance of DRT services. Trip numbers can be divided by a number of available variables (period, trip length, population, and density) to create a number of derived metrics. By applying these variables across three different DRT service areas in Logan City, Australia, where other key factors are held constant, one can see how different formulations lead to very different readings of DRT system performance. The results confirm the dilemma of cost efficiency versus equity in service provision in low-density environments. This paper also highlights current data limitations and calls for better data collection to facilitate the development of new evaluation methods for DRT services and a new composite metric that can be used for inter-service comparison.