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.
The comprehension of network-level consequences resulting from disruptive events is a main gray area in the evaluation of transportation network resilience at the regional level. Explaining hazard impacts on regional network infrastructures and identifying significantly affected areas are important for communicating the need for building resilient infrastructure. This paper presents a framework for assessing the regional network resilience by leveraging scenario-based traffic modeling and GIS techniques. High-impact-zone location identification metrics were developed and implemented in preliminarily identifying areas affected by bridge closures. Resilience was estimated, and an index developed by utilizing practical functionality metrics based on vehicle distance and hours traveled. These are illustrated for the Tampa Bay, Florida, area. Findings for 10 bridge closure scenarios and recovery schemas indicated significant regional resilience losses. The I-275 bridge closure indicated the highest functional loss to the regional network: the aggregated resilience index below 0.5 reflects severe network performance deficit and mobility limitations.
The AASHTO Pontis bridge management system has been used to support network-level and project-level decision making on the condition and functional obsolescence of bridges. State departments of transportation often develop bridge inspection data collection methods, deterioration models, cost models, and other preservation analysis capabilities to comply with the requirements of the federal Government Accounting Standards Board. The bridge health index (BHI) in the Pontis bridge management system has been used in the evaluation of the condition of bridges and elements at the project and network levels. This paper investigates three issues in the computation of the BHI: the effects of using linear and nonlinear scales for the condition state weights when computing the element health index (EHI); the application of amplification weights to EHI values to emphasize bridge elements in bad condition; and the development of element weights based on element replacement costs, element long-term costs, element vulnerability to hazard risks, and a combination of these measures. Historical condition data from element-based inspection were used to evaluate these effects at the network level.
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