Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

Abstract EN

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.