Abstract
Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.
Highlights
Traffic operational condition, measuring with traffic flow, travel time, and vehicle speed, is an important indicator to reflect the level of service of urban roads network
In order to solve this question, we introduced the graph neural network (GNN), an extended deep learning model to deal with graph data [17]
Our main contributions are as follows: (1) According to the application restriction, traffic information is expected to collect on segments as less as possible in order to reduce acquisition cost; we put forward a new research issue: how to identify the critical segments that contribute to guaranteeing traffic state prediction accuracy for all segments in road network in the most effective way
Summary
Traffic operational condition, measuring with traffic flow, travel time, and vehicle speed, is an important indicator to reflect the level of service of urban roads network. Combined with attention mechanism [21], we construct an GNN-based machine learning model that takes the historical traffic information of critical segments as input and predicts the link travel speed for each segment in the time interval. E attention mechanism quantifies the contribution of each segment’s traffic information to travel speed prediction of each link in the road network when the model achieves the downstream objective of minimizing the prediction error. (1) According to the application restriction, traffic information is expected to collect on segments as less as possible in order to reduce acquisition cost; we put forward a new research issue: how to identify the critical segments that contribute to guaranteeing traffic state prediction accuracy for all segments in road network in the most effective way. We have to design a heuristics algorithm for this NP-hard problem. (b) Prediction relation f(·) establishment by machine learning model: it is a typical nonlinear regression problem
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