Alternative approach for vehicle trajectory reconstruction under spatiotemporal side friction using lopsided network

Abstract

Limitation of probe sensor-based technique to collect traffic data in the temporal domain tends to combine it with the fixed sensor. This combination technique involves extra equipment installation difficulties when the road segment becomes non-homogeneous due to traffic operations or lane configurations. Particularly, in developing countries, chaotic traffic pattern and the absence of a fixed sensor in the roadway create the trajectory reconstruction even more challenging. Video-sensor (also, other fixed-sensor) based trajectory reconstruction techniques require sensors for each non-homogeneous road segment and need separate fundamental diagram (FD) to estimate the required traffic parameters. These difficulties can be overcome by using probe sensors. Thus, this research proposes an approach of estimating traffic parameters from probe data considering variable road capacity. Those parameters are used as input to reconstruct the trajectories of all vehicles in the traffic stream (i.e. also non-probe vehicles, in particular) through the application of a lopsided network. The proposed approach improves the percent root mean square error of estimated travel time by around 38% compared with that which uses traffic parameters obtained from FD with respect to ground-truth travel time. This approach is very appropriate, economical and reliable, especially where the required number of fixed sensors is unavailable.