Discretionary lane changing (DLC) decision on freeways is influenced by several factors and varies across vehicle classes. This research aims to investigate the role of vehicle attributes (e.g., length, width) and flow characteristics (e.g., headways and lead-lag gaps) on the DLC decision making process. We analyzed the vehicle trajectory data extracted from the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) program for the Interstate 80. The data were collected during rush hours (between 4:00 p.m. and 4:15 p.m. on April 13, 2005) on a segment of Interstate 80 in Emeryville, San Francisco, California. Our data set contains only the discretionary lane change that made by autos and trucks only. As a result, two lanes out of six lanes were used for the purpose of this research. The logistic stepwise selection procedure was applied to estimate statistically significant predictor variables that contribute to increasing the likelihood of DLC. The dependent variable was whether discretionary lane change was executed or not. Nine explanatory variables were included to find out the associated factors for DLC. The logistic regression model identified three statistically significant predictor variables out of seven independent variables considered in the current research. The predictors that increase the probability of discretionary lane change are distance between vehicles (space headway) on the original lane, distance between subject vehicle and vehicle on the target lane (lead gap), vehicle class (auto and truck). Overall, the model results provide an overview of factors associated with discretionary lane change that could be addressed when providing highway safety improvement.


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    Titel :

    Analysis of Factors Affecting Discretionary Lane Change


    Beteiligte:

    Kongress:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Erscheinungsdatum :

    31.08.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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