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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/36819

Title: Evaluating the safety impact of connected and autonomous vehicles on motorways
Authors: Papadoulis, Alkis
Quddus, Mohammed A.
Imprialou, Marianna
Keywords: Connected and Autonomous vehicles
Traffic microsimulation
Road safety
Surrogate Safety Assessment Model
VISSIM
External Driver Model
Issue Date: 2019
Publisher: © Elsevier
Citation: PAPADOULIS, A., QUDDUS, M.A. and IMPRIALOU, M., 2019. Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis and Prevention, 124, pp.12-22.
Abstract: Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision-making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12–47%, 50–80%, 82–92% and 90–94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.
Description: This paper is closed access until 2 January 2020.
Version: Accepted for publication
DOI: 10.1016/j.aap.2018.12.019
URI: https://dspace.lboro.ac.uk/2134/36819
Publisher Link: https://doi.org/10.1016/j.aap.2018.12.019
ISSN: 0001-4575
Appears in Collections:Closed Access (Architecture, Building and Civil Engineering)

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