Search

The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space–time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedule-based and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning. [ABSTRACT FROM AUTHOR]

Continuous developments in automotive technology brought about the reality of having an increasing number of autonomous vehicles (AV) on roadways soon. However, this advancement comes with the uncertainty of how the interaction between the autonomous and non-autonomous (non-AV) vehicles will play out. Using traffic simulators to model both AVs and non-AVs, and studying the effect of AVs on roadway mobility can help researchers in learning what to expect in real life. In this paper, an open-source simulator called Simulation of Urban Mobility (SUMO) was used to develop AVs by modifying Intelligent Driver Model (IDM) car following and LC2013 lane changing models. Then, three actual roadways in Columbus, Ohio were modeled, and 40 simulations were run for each traffic network. The first set of simulations focused on the impacts that AVs have on vehicle roadway mobility with non-AVs when all the AVs have the same autonomy level. The second set of simulations had mixed AV autonomy levels (SAE Level 2, 3 and 4) with mixed AV and non-AV traffic. The analysis revealed that mobility of AVs and non-AVs increased generally as autonomy level increased. When the AV autonomy levels were mixed, the non-AVs experienced lower average speeds and decreased roadway mobility. [ABSTRACT FROM AUTHOR]