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Multi-vehicle anticipation-based driver behavior models: a synthesis of existing research and future research directions

Nirmale, S and Sharma, A and Pinjari, AR (2023) Multi-vehicle anticipation-based driver behavior models: a synthesis of existing research and future research directions. In: Transportation Letters .

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Official URL: https://doi.org/10.1080/19427867.2023.2231212


Multi-vehicle anticipation (MVA) refers to drivers’ ability to consider stimuli from several vehicles ahead in their maneuvering decisions, such as longitudinal, lateral, and a combination of longitudinal and lateral movements. This paper provides a comprehensive review of MVA-based driver behavior models developed for both homogeneous and heterogeneous disordered (HD) traffic streams. Studies on MVA identify various advantages of incorporating MVA in driver behavior models, such as superior numerical and behavioral soundness, plausible parameter estimates, and model outputs, and improved model realism. In addition, our findings indicate that MVA-based driver behavior models follow a similar pattern of extending the established single-leader car-following models, considering vehicles that are directly ahead (in the same lane), and focussing on a fixed number of vehicles ahead. For HD traffic streams, drivers’ also consider stimuli from vehicles obliquely placed or on either side. Furthermore, this review discusses issues with the current modeling approaches and suggests future research directions

Item Type: Journal Article
Publication: Transportation Letters
Publisher: Taylor and Francis Ltd.
Additional Information: The copyright for this article belongs to the Taylor and Francis Ltd.
Keywords: driver behavior; homogeneous traffic conditions, heterogeneous traffic conditions; human factors; Multi-vehicle anticipation
Department/Centre: Division of Interdisciplinary Sciences > Center for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP)
Division of Mechanical Sciences > Civil Engineering
Date Deposited: 31 Jul 2023 11:32
Last Modified: 31 Jul 2023 11:32
URI: https://eprints.iisc.ac.in/id/eprint/82754

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