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 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.