Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

Abstract

Due to the restriction of traffic management measure in large cities, large heavy-haul trucks can only travel on the circuits and expressways around the city, which often causes congestion in these areas. It is necessary to study the travel speed prediction of trucks on the urban ring road and provide special information services for trucks. Based on the data generated by the trucks driving on the Sixth Ring Road in Beijing, an optimized GRU algorithm is proposed to predict the travel speed of trucks driving on urban express roads under non-recurrent congested conditions. First, a GPS map-matching algorithm that can simultaneously meet the accuracy and efficiency requirements of matching is proposed. Then, the trucks' data traveling on the Sixth Ring Road in Beijing are extracted from the original data. Aiming at getting rid of the abnormal data in GPS data, the screening and processing rules of the abnormal data are made, and then, the traffic speed sequence is extracted. Aiming at the problem that the commonly used weight optimization algorithm SGD cannot adaptively adjust the learning rate, Adam, Adadelta, and Rmsprop are used to optimize the weights in the GRU model in this paper. Considering the four scenarios, including workday, weekend, rainy, and accident, the accuracies of the proposed methods are verified.