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Temporary debris management sites (TDMSs) are often established after large‐scale natural disaster to expedite waste removal. However, uses of these sites inevitably increase debris removal cost due to double handling of waste. How to select the quantity of TDMSs to optimize the trade‐off between the recycling benefits and debris removal cost is a critical issue in debris management. Previous studies focused primarily on initial geographical analyses of TDMSs or the debris removal optimization given known TDMS locations and quantities. This article proposes a multiobjective mixed integer linear optimization method to optimize debris removal with a particular focus on TDMS selection and to pursue the minimum debris removal cost, total processing time, and environmental influence. A case study with four scenarios was conducted to test the validity of the proposed approach. Numerical results indicate that a maximum reduction of 33.27% in removal time, a maximum reduction of 37.07% in cost, and a maximum increase of 33.60% in recycle profits can be achieved when the time, cost, and recycle profit objective are solely pursued compared to other studied scenarios, respectively. The findings of this study contribute to better decision making in debris removal operations after major natural disasters.

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High-resolution vehicle trajectory data can be used to generate a wide range of performance measures and facilitate many smart mobility applications for traffic operations and management. In this paper, a Longitudinal Scanline LiDAR-Camera model is explored for trajectory extraction at urban arterial intersections. The proposed model can efficiently detect vehicle trajectories under the complex, noisy conditions (e.g., hanging cables, lane markings, crossing traffic) typical of an arterial intersection environment. Traces within video footage are then converted into trajectories in world coordinates by matching a video image with a 3D LiDAR (Light Detection and Ranging) model through key infrastructure points. Using 3D LiDAR data will significantly improve the camera calibration process for real-world trajectory extraction. The pan-tilt-zoom effects of the traffic camera can be handled automatically by a proposed motion estimation algorithm. The results demonstrate the potential of integrating longitudinal-scanline-based vehicle trajectory detection and the 3D LiDAR point cloud to provide lane-by-lane high-resolution trajectory data. The resulting system has the potential to become a low-cost but reliable measure for future smart mobility systems.