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Journal of Advanced Transportation
Volume 2018 (2018), Article ID 8645709, 10 pages
Research Article

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China

Correspondence should be addressed to Jingqiu Guo

Received 16 June 2017; Revised 14 October 2017; Accepted 19 November 2017; Published 14 January 2018

Academic Editor: Chunjiao Dong

Copyright © 2018 Lanfang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers’ natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies.