Development and Evaluation of Intersection-Based Turning Movement Counts Framework Using Two Channel LiDAR Sensors

This paper presents vehicle localization and tracking methodology to utilize two-channel LiDAR data for turning movement counts. The proposed methodology uniquely integrates a K-means clustering technique, an inverse sensor model, and a Kalman filter to obtain the final trajectories of an individual vehicle. The objective of applying K-means clustering is to robustly differentiate LiDAR data generated by pedestrians and multiple vehicles to identify their presence in the LiDAR’s field of view (FOV). To localize the detected vehicle, an inverse sensor model was used to calculate the accurate location of the vehicles in the LiDAR’s FOV with a known LiDAR position. A constant velocity model based Kalman filter is defined to utilize the localized vehicle information to construct its trajectory by combining LiDAR data from the consecutive scanning cycles. To test the accuracy of the proposed methodology, the turning movement data was collected from busy intersections located in Newark, NJ. The results show that the proposed method can effectively develop the trajectories of the turning vehicles at the intersections and has an average accuracy of 83.8%. Obtained R-squared value for localizing the vehicles ranges from 0.87 to 0.89. To measure the accuracy of the proposed method, it is compared with previously developed methods that focused on the application of multiple-channel LiDARs. The comparison shows that the proposed methodology utilizes two-channel LiDAR data effectively which has a low resolution of data cluster and can achieve acceptable accuracy compared to multiple-channel LiDARs and therefore can be used as a cost-effective measure for large-scale data collection of smart cities.

Real-time intersection vehicle turning movement counts from live UAV video stream using multiple object tracking

The intelligent transportation system(ITS)is committed to ensuring safe and effective next-generation traffic throughout a city.However,such efficient operation on urban traffic networks needs the support of big traffic data,especially Turning Movement Counts(TMC)at intersections.Generally,TMC data are more challenging to collect due to labor cost and accuracy problems.In this paper,we leverage the capabilities of Unmanned Aerial Vehicles(UAV)to collect real-time TMC data in a cost-efficient way.We proposed a real-time TMC data collection framework based on a live video stream.The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection.In addition,a challenging case study was conducted,and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework.Specifically,with a GTX 1650 graphics card,about 10 FPS can be achieved in real-time for the TMC data collection.The overall accuracy is 91.93%,and the best case is over 98%accurate.In the context of miscounting,the major reason is due to ID switching caused by background occlusion.The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.