The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

Abstract

A real-time, effective, and dynamic taxi cruising recommendation strategy is essential to solving the problem of taxi cruising passenger difficulty and urban road traffic congestion. This study focuses on two aspects of the real-time accessible range and pick-up ratio (PR) and proposes a real-time dynamic identification method for taxi optimal cruise-seeking area. Firstly, based on the cumulative opportunity method, a univariate temporal convolutional network (UTCN) accessible range dynamic prediction model is proposed to predict the real-time accessible range of taxis. Secondly, based on the gradient boosting decision tree (GBDT) model, the influencing factors with a high correlation with the PR are selected from the four dimensions of traffic characteristics, environmental meteorology, and time and space variables. Then, a multivariate univariate temporal convolutional network (MTCN) global grid PR prediction model is constructed, and the optimal taxi cruising area is identified based on the maximum PR. The results show that the taxi accessible range and PR of the same grid in different periods change with time, and based on the model comparison, the accessible range and PR prediction results of UTCN and MTCN algorithms in different periods are the best to identify the optimal cruising area of taxis in different periods. The main contribution of this study is that the proposed optimal cruising area prediction model has timeliness, accessibility, and dynamics. It can not only improve the probability of taxis receiving passengers and avoid taxis cruising aimlessly, but also solve the shortage of taxis in hotspots, thus shortening the waiting time of passengers. This provides a scientific basis for improving taxi cruising efficiency and the government’s formulation of taxi operation management policies, which can effectively promote the sustainable development of urban traffic.