Development of a binary logistic lane change model and its validation using empirical freeway data

Christina Ng, Susilawati Susilawati, Md Abdus Samad Kamal, Irene Mei Leng Chew

    Research output: Contribution to journalArticleResearchpeer-review

    3 Citations (Scopus)


    An effective macroscopic lane change (LC) model is required to facilitate active and dynamic lane management to develop cell-based multilane macroscopic traffic models. Existing logistic regression LC models do not undertake model classification of lane change; do not consider performance measures in the validation of field data and ignore movement between lanes during time-varying traffic. Models that consider the direction of LC are, however, biased in their prediction of left LC (LLC) and right LC (RLC) direction. This study proposed a simplified macroscopic cell-based binary logistic LC (BLLC) model describing the LC probability using fewer explanatory variables; in this model, the direction of LC is considered as a new variable. Considering the model performance measures, the results show that there exists substantial difference in LC behaviour in both directions. The present model also achieved a smaller difference in the percentage of accurate prediction (0.9%) between the LLC and RLC.

    Original languageEnglish
    Pages (from-to)49-71
    Number of pages23
    JournalTransportmetrica B: Transport Dynamics
    Issue number1
    Publication statusPublished - 2 Jan 2020


    • binary logistic regression
    • cell based
    • lane change direction
    • Macroscopic lane change model
    • Next Generation Simulation (NGSIM) data
    • probability of lane change

    Cite this