Spatio-Temporal Digraph Convolutional Network-Based Taxi Pickup Location Recommendation

The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional network (STDCN) model. First, the pickup and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on the gray relational analysis. Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pickup locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects.

I. Introduction

As An important link in urban public transport, the taxi plays a significant role in urban transport development [1] and could act as a movement detector for urban population movements [2]. At the same time, the Internet of Vehicles (IoVs) technology provides support for taxis to obtain real-time and accurate information about surrounding vehicles and road conditions with its rapid development in recent years. It is noticed that there are many negative reports, such as taxi drivers parking illegally to carry passengers. This is mainly because there are currently no useful ways to integrate effective information into the road network, and drivers can only select “seemingly reasonable” pickup locations from limited traffic cognitive information. It not only tends to cause traffic congestion, but also poses a great safety hazard [3]. Therefore, it is important to recommend suitable pickup locations for taxi drivers.