Citation: | SUN Lishan, JIA Lin, WEI Zhonghua, LI Junfeng. Demand Forecasting of Taxi Travel Based on GPS Data[J]. Journal of Transport Information and Safety, 2021, 39(5): 128-136. doi: 10.3963/j.jssn.1674-4861.2021.05.016 |
In recent years, the number of "car-hailing" keeps increasing, which leads to several problems gradually, such as long waiting time of "car-hailing"and large demands for hot spot areas. The experiences of "car-hailing" should be improved urgently. Based on GPS data of taxis in Chengdu, the distribution characteristics of taxi trips are studied by dividing working days into morning, evening, and night peak periods. A k-distance curve is used to improve the density-based spatial clustering of applications with noise(DBSCAN)algorithm. Cluster analysis is carried out on taxi pick-up and drop-down points, and the hot spot areas are obtained by data mining. The BP neural network is used to predict the travel demands in hot spot areas. The prediction results show that compared with the random forest model and ridge regression model, the MAPE of the BP neural network model increases by 3.25% and 5.87% in the morning peak, 2.98% and 4.32% in the evening peak, and 1.44% and 2.58% in the night peak, respectively, which verifies the feasibility of the BP neural network in demand forecasting of taxi travel.
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