Kamrani, M, Srinivasan, AR orcid.org/0000-0001-9280-7837, Chakraborty, S et al. (1 more author) (2020) Applying Markov decision process to understand driving decisions using basic safety messages data. Transportation Research Part C: Emerging Technologies, 115. 102642. ISSN 0968-090X
Abstract
While a number of studies have investigated driving behaviors, detailed microscopic driving data has only recently become available for analysis. Through Basic Safety Message (BSM) data from the Michigan Safety Pilot Program, this study applies a Markov Decision Process (MDP) framework to understand driving behavior in terms of acceleration, deceleration and maintaining speed decisions. Personally Revealed Choices (PRC) that maximize the expected sum of rewards for individual drivers are obtained by analyzing detailed data from 120 trips and the application of MDP. Specifically, this paper defines states based on the number of objects around the host vehicle and the distance to the front object. Given the states, individual drivers’ reward functions are estimated using the multinomial logit model and used in the MDP framework. Optimal policies (i.e. PRC) are obtained through a value iteration algorithm. The results show that as the number of objects increases around a host vehicle, the driver prefer to accelerate in order to escape the crowdedness around them. In addition, when trips are segmented based on the level of crowdedness, increased levels of trip crowdedness results in a fewer number of drivers accelerating because the traffic conditions constrain them to maintaining constant speed or deceleration. One potential application of this study is to generate short-term predictive driver decision information through historical driving performance, which can be used to warn a host vehicle driver when the person substantially deviates from their own historical PRC. This information could also be disseminated to surrounding vehicles as well, enabling them to foresee the states and actions of other drivers and potentially avoid collisions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Transportation Research Part C: Emerging Technologies. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Driving behavior; Markov decision processes; Basic safety messages; Multinomial logit model; Instrumented vehicle data; Automation; Connected vehicle data |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Jun 2020 10:47 |
Last Modified: | 22 Apr 2021 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trc.2020.102642 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162137 |
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Licence: CC-BY-NC-ND 4.0