Open AccessFreeway travel time estimation based on the general motors model: a genetic algorithm calibration framework
Author(s) -
Yang Shu,
Ou Jishun,
Feng Yiheng,
Wang Yuan
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5540
Subject(s) - calibration , genetic algorithm , reliability (semiconductor) , travel time , traffic flow (computer networking) , interval (graph theory) , algorithm , computer science , estimation , simulation , traffic congestion , engineering , statistics , mathematics , transport engineering , machine learning , power (physics) , physics , computer security , quantum mechanics , combinatorics , systems engineering
Travel time estimation plays an important role in freeway performance assessment and reliability management. Conventional estimation methods based on speed information do not consider the level of congestion during modelling, leading to unreliable estimations under congested conditions. This study presents a freeway travel time estimation method based on the general motors (GM) model, where the concept ‘virtual vehicle’ was proposed to apply macroscopic traffic flow data to the microscopic GM model. First, a GM‐based travel time estimation (GMTTE) model was developed to estimate link travel time. Three sets of parameters l , m and α were separately used under free‐flowing, congestion and transition conditions. Next, two corridor estimation models, namely GMTTE time‐slice‐based (GMTTE‐TSB) model and GMTTE continuous‐speed (GMTTE‐CS) model, were proposed. To calibrate the three parameters, a genetic algorithm framework was developed. The proposed method was calibrated and evaluated using traffic data collected from seven freeway segments in the greater St. Louis, Missouri region. Results show that (i) the GMTTE‐CS model outperformed the GMTTE‐TSB model; (ii) the optimal parameters of the GMTTE‐CS model under free‐flowing and congested conditions arel = 1.1 ,m = 2.0 ,α = 8.0andl = 1 ,m = 0.1 ,α = 8.0, respectively; (iii) the GMTTE‐CS model outperformed the instantaneous and time‐slice models under congested conditions, while showing similar accuracies with the two models under free‐flowing and status‐transition conditions; and (iv) the optimal time interval was found to be 9–10 min for free‐flowing conditions and 6–7 min for congested conditions.