Multivariate Analysis on Dynamic Car-Following Data of Non-lane-Based Traffic Environments

The difficulties of the microscopic models in accurate representation of the real traffic phenomena stem from its complexities in the collection and processing of reliable time-series car-following data of non-lane-based traffic environments. Proper estimation of car-following data can suitably ameliorate the realism of traffic sub-models and is still a demanding task. This study describes an image-based in-vehicle trajectory data collection system for the estimation of reliable dynamic time-series data, using camera calibration and in-vehicle global positioning system (GPS) information. A copula-based methodological framework is also investigated in this study for evaluating safety in the car-following processes, by accommodating the dependence structure of longitudinal gap, centerline separation and vehicle speeds. Results of the study demonstrated the importance of centerline separation in apprehending the car-following processes. In particular, the probability of maintaining lower gaps increases with the decrease in speed and increase in centerline separation. A 15–20% reduction in the longitudinal gaps is observed for speeds greater than 60 kmph. As importantly, the study recommends the applicability of tri-variate Gaussian copula in assessing the safety or ‘safe distance-keeping’ criteria of drivers in the car-following processes, which can indeed augment the accurate representation of drivers’ behavior and development of the car-following models, advanced driver assistance systems and for safety evaluation in the car-following process of non-lane-based traffic environments.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01717581
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 30 2019 3:06PM