Let Trajectories Speak Out the Traffic Bottlenecks

TL;DR

This paper introduces a trajectory-driven framework for identifying traffic bottlenecks in road networks, utilizing real-time trajectory data to efficiently find influential road segments causing congestion.

Contribution

It formulates the traffic bottleneck identification as an NP-hard problem and proposes two novel algorithms, including a sampling-based greedy method, to solve it effectively.

Findings

The proposed algorithms outperform baseline methods in accuracy and efficiency.

The traffic spread model accurately captures real-world traffic dynamics.

Sampling-based greedy algorithm accelerates bottleneck detection in large datasets.

Abstract

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures real-time traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification: Given a road network R, a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF, with an approximation ratio of 1-1/e. To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG. Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.