Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Abstract

This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles’ movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models.

KEYWORDS:

• Traffic flow modeling
• car following
• Intelligent driver model
• Gipps
• heterogonous traffic conditions

Acknowledgment

The authors would like to thank PTV Group for supporting the research work by proving PTV VISSIM 11.0 and external driving behavior API for modeling mixed traffic conditions.

Disclosure statement

No potential conflict of interest was reported by the author(s).