Nirmale, SK and Pinjari, AR and Sharma, A (2022) A panel data-based discrete-continuous modelling framework to analyze longitudinal driver behavior in homogeneous and heterogeneous disordered traffic conditions. In: Transportation Letters .
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Abstract
We propose a panel data-based discrete-continuous modeling framework to analyze driver behavior in two disparate trajectory datasets–one from a heterogeneous disorderly (HD) traffic stream in India and another from a homogeneous traffic stream in the United States. The panel data-based framework allows the analyst to isolate the subject vehicle- and driver-specific unobserved factors that influence driver behavior. Doing so helps reduce the confounding effects of such unobserved factors on analyzing the influence of observed factors, such as relative speeds and spacing between the subject vehicle and other vehicles, on driver behavior. The empirical results reveal both similarities and differences in driver behavior between the two trajectory datasets. In addition, the analysis sheds light on the suitability of different lengths of influence zones on driver behavior in the two datasets. The insights from this study can help improve driver behavior models and traffic simulation frameworks for both traffic conditions.
Item Type: | Journal Article |
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Publication: | Transportation Letters |
Publisher: | Taylor and Francis Ltd. |
Additional Information: | The copyright of the article belongs to the Taylor and Francis Ltd. |
Keywords: | Behavioral research, Discrete/continuous; Driver's behavior; Heterogeneous disorderly traffic condition; Homogeneous traffic condition; Multi-vehicle anticipation; Multi-vehicles; Panel data; Panel data models; Traffic conditions, Vehicles |
Department/Centre: | Division of Mechanical Sciences > Civil Engineering |
Date Deposited: | 04 Nov 2022 09:11 |
Last Modified: | 16 Nov 2022 06:25 |
URI: | https://eprints.iisc.ac.in/id/eprint/77793 |
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