Detecting Loaded Trajectories for Hazardous Chemicals Transportation

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Abstract
Hazardous chemicals transportation (HCT) brings significant financial, environmental, and health-related risks. It is imperative that a robust regulatory system is in place to reduce the risk of accidents occurring while such hazardous chemicals are being transported. Governments around the world use GPS sensors to monitor the raw trajectories of HCT trucks, but they have difficulty detecting the loaded trajectories, which is of utmost importance for the management of H CT processes. The loaded trajectory refers to the subtrajectory generated by tracking an HCT truck when it is loaded with hazardous chemical in an HCT process. The stay points in the raw trajectory provide some feasibility to detect the loaded trajectory as they reflect the potential loading and unloading actions of the HCT truck. However, directly using the stay points to detect the loaded trajectory usually leads to unsatisfactory results due to two chal-lenges: (1) complex staying scenarios, and (2) numerous loading and unloading locations. To tackle the challenges, we propose a LoadEd trAjectory Detectlon framework, called LEAD, to detect the loaded trajectory from the raw HCT trajectory accurately and efficiently. LEAD processes a raw trajectory into a set of candidate trajectories, encodes each candidate trajectory into a latent representation, and detects the loaded trajectory using the latent representations of candidate trajectories. Extensive experiments based on a real-world dataset from Nantong, China confirm the effectiveness of our framework. The results show that the detection accuracy of LEAD exceeds 83 % which outperforms competing baselines by over 42 %.
Funding Information
  • NSFC (61972069,61836007,61832017)
  • National Key R&D Program of China (2019YFB2101801)

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