Abstract
Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.
Highlights
Taxi is a comprehensive reflection of urban traffic
Balan designed a trip information system to predict the fare and trip duration of the taxi ride the passengers were planning to take. e authors claimed that the accuracy and the real-time performance were validated by large scale evaluation [2]
Li conducted a similar work. e authors proposed a hybrid model coupling the deep learning model and the quantile regression aiming at the travel time prediction [3]
Summary
Taxi is a comprehensive reflection of urban traffic. It provides information regarding the traffic situation and the trend of crowd activities. e accurate prediction of taxi flow helps to find the potential traffic hotspots in the city to take effective measures to avoid the coming traffic congestions. If some of the trajectory data are lost, they can be reconstructed through the nearest valid track points Due to this attractive advantage, a variety of trajectory-based models were proposed for the traffic flow prediction. En, a GNN model and a time series network are created to capture the spatiotemporal information of intersection traffic flow. The overall framework of TaxiInt consists of three parts: the data sources, the time-based road network traffic information change graph, and neural network structure. By using these components, we can capture the dynamic information, like spatial and temporal correlations, from the road information stream. In order to increase the sensitivity of the network model to the traffic data in the spatial structure of the road network, we introduce the at-
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