Autonomous vehicles (AVs) can dramatically reduce the number of traffic crashes and associated fatalities by eliminating the avoidable human-error-related crash contributing factors. Many companies have been conducting pilot tests on public roads in several states in the U.S. and other countries to accelerate AV mass deployment. AV pilot operations on Californian public roads saw 251 AV-involved crashes (as of February 2020). These AV-involved crashes provide a unique opportunity to investigate AV crash risks in the mixed traffic environment. This study collected the AV crash reports from the California Department of Motor Vehicles and applied the decision tree and association rule methods to extract the pre-crash rules of AV-involved crashes. Extracted rules revealed that the most frequent types of AV crashes were rear-end crashes and predominantly occurred at intersections when AVs were stopped and engaged in the autonomous mode. AV and non-AV manufacturers and transportation agencies can use the findings of this study to minimize AV-related crashes. AV companies could install a distinct signal/display to inform the operational mode of the AVs (i.e., autonomous or non-autonomous) to human drivers around them. Moreover, the automatic emergency braking system in non-AVs could avoid a significant number of rear-end crashes as, often, rear-end crashes occurred as a result of the failure of following non-AVs to slow down in time behind AVs. Transportation agencies can consider separating AVs from non-AVs by assigning “AV Only” lanes to eliminate the excessive rear-end crashes resulting from the mistakes of human drivers in non-AVs at intersections.
He completed his Ph.D. in Civil Engineering from Clemson University in 2014 and M.Sc. in Civil Engineering from Wayne State University in 2010. Dr. Dey was the recipient of the Clemson University 2016 Distinguished Postdoctoral Award. His primary research area includes intelligent transportation systems, and traffic safety and operations. He has been very active in engineering education research as well.
Matching riders and drivers in ridesharing considering conflicting objectives of diverse stakeholders is challenging. The objective of this research is to formulate and evaluate the performance of four ridesharing matching-objectives (i.e. system-wide minimisation of passengers' wait time, minimisation of VMT, minimisation of detour distance, maximisation of drivers' profit) considering interests of diverse mobility stakeholders (i.e. drivers, riders, matching agencies, government transportation agencies). A grid roadway network was used to compare the performance of the four matching-objectives in serving a ridesharing demand scenario. Performance comparison of matching-objectives revealed that a systemwide VMT minimisation matching-objective performed best with least sacrifices on the other three matching-objectives from their respective best performance level. Also, systemwide VMT minimisation was the best matching-objective, when drivers' and government transportation agencies' expectations were prioritised. System-wide drivers' profit maximisation matching-objective provided the highest monetary incentives for drivers and riders in terms of maximising profit and travel cost savings, respectively. System-wide minimisation of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing matching system modelling and performance evaluation based on different matching-objectives and can be used in developing and implementing ridesharing service considering multiple stakeholders' concerns.
INTRODUCTIONAccording to the 2015 Urban Mobility Scorecard, United States (US) travellers lost 7 billion hours and wasted 3 billion gallons of fuel due to traffic congestion [1]. Shared transportation modes are the emerging transportation demand management (TDM) strategies to better utilize limited transportation infrastructures and improve transportation system performance. Ridesharing, a form of shared mobility service, has been growing in popularity and has the potential to reduce emissions, fuel consumption, system-level vehicle miles travelled (VMT), and most importantly, traffic congestion [2,3]. Modern-day ridesharing services, enabled by information technology (i.e. mobile apps) face several operational challenges (e.g. efficient drivers' and riders' matching, maintaining acceptable service reliability and flexibility, integration with multimodal options, and multi-institutional collaboration) [4]. Several studies haveThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.