Investigating Impact of the Heterogeneity of Trajectory Data Distribution on Origin-Destination Estimation: A Spatial Statistics Approach

Many studies have been conducted to estimate origin-destination (OD) demand based on vehicle trajectory data. However, the estimation accuracy heavily relies on the temporal-spatial distribution of trajectories, and its effect on OD estimation remains unrevealed and under-estimated. This study proposes a novel method for investigating the impact of the heterogeneity of trajectory data distribution on OD estimation at urban road networks. Synthetic scenarios are designed based on automatic license plate recognition data collected from a real-world traffic network in Kunshan, China. Four factors: test area, sampling rate, time period, and the sampling method are selected for scenario settings. Next, a particle filter-based method is implemented to reconstruct vehicle trajectories using the sampled trajectory data, and then the path flows and OD demands are extracted. Finally, a spatial statistics approach is introduced to reveal the spatial autocorrelation of trip generation/attraction variations, and the high-high clusters whose OD values are significantly affected are identified. Test results show that the heterogeneity effects of trajectory distribution on OD estimation can be effectively studied by the proposed method. Further investigation shows that the findings of spatial statistics can be applied for improving OD estimation accuracy.