Identifying and analyzing heavy truck stops from GPS data by grid processing

Truck transportation occupies a dominant position in road transportation, with the increase of freight demand and the number of trucks, the traffic congestion on the road is also become an urgent problem to be solved. Different to the family cars, the trucks in the process of driving the parking need and occupy a larger space, and loading and unloading time is longer, easy to have an impact on the surrounding traffic. Therefore, based on GPS track data, this paper adopts the grid processing method to identify the truck stopping points and compare it with its nearby POI data to know the POI information near the truck's stopping points. And then the parking time of the trucks is analyzed and it is found that most of the parking time of the trucks is within 100 minutes. Finally, the DBSCAN density clustering algorithm for truck hotspot area identification model is built for this paper, and the parking hotspots of trucks are analyzed and the results of clustering are derived. It can provide a basis for the road pipeline department to control the traffic in the internal area of the hotspot as well as the surrounding area, reducing congestion and reducing the hidden danger, and improving the efficiency of truck transportation.