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A Driver Behavior-Based Lane-Changing Model for Urban Arterial Streets
Lane-changing algorithms have attracted increased attention during recent years. However, limited research has been conducted to address the probability of changing lanes as a function of driver characteristics and lane-changing scenarios. This study contributes to the development of a comprehensive...
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Published in: | Transportation science 2014-05, Vol.48 (2), p.184-205 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Lane-changing algorithms have attracted increased attention during recent years. However, limited research has been conducted to address the probability of changing lanes as a function of driver characteristics and lane-changing scenarios. This study contributes to the development of a comprehensive framework for modeling drivers' lane-changing maneuver on arterials by using driver behavior-related data. Focus group studies and "in-vehicle" driving tests were performed to investigate the effects of driver type under various lane changes on urban arterials and to collect microscopic vehicular data. With these field collected values, a model was developed to estimate the probability of changing lanes under various lane-changing scenarios and to estimate the corresponding gap acceptance characteristics. The lane-changing probability for each scenario was modeled as a function of the factors identified from the focus group discussions, as well as the driver types. In the gap acceptance modeling, a sequence of "hand-shaking negotiations" was introduced to describe vehicle interactions that may occur during lane-changing maneuvers. The proposed lane-changing model was implemented in the CORSIM (CORrider SIMulation) micro-simulator. The simulation capabilities of the newly developed model were compared to the original lane-changing algorithm in CORSIM and to the field observations. The validation results indicated that the new model better replicates the observed traffic under various levels of flow. |
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ISSN: | 0041-1655 1526-5447 |
DOI: | 10.1287/trsc.1120.0435 |