Decentralized Traffic Management Strategies for Sensor-Enabled Cars

TL;DR

This paper presents proactive merging algorithms for sensor-enabled cars that improve traffic flow and reduce delays at highway merge points by enabling early decision-making based on local sensor communication.

Contribution

It introduces novel proactive merging strategies utilizing sensor communication, decoupling decision points from merging points to optimize traffic flow.

Findings

Proactive merging outperforms priority-based merging in simulations.

Sensor-enabled cars enable earlier and better merging decisions.

Significant improvements in traffic flow and delay reduction observed.

Abstract

Traffic Congestions and accidents are major concerns in today's transportation systems. This thesis investigates how to optimize traffic flow on highways, in particular for merging situations such as intersections where a ramp leads onto the highway. In our work, cars are equipped with sensors that can detect distance to neighboring cars, and communicate their velocity and acceleration readings with one another. Sensor-enabled cars can locally exchange sensed information about the traffic and adapt their behavior much earlier than regular cars. We propose proactive algorithms for merging different streams of sensor-enabled cars into a single stream. A proactive merging algorithm decouples the decision point from the actual merging point. Sensor-enabled cars allow us to decide where and when a car merges before it arrives at the actual merging point. This leads to a significant improvement in traffic flow as velocities can be adjusted appropriately. We compare proactive merging algorithms against the conventional priority-based merging algorithm in a controlled simulation environment. Experiment results show that proactive merging algorithms outperform the priority-based merging algorithm in terms of flow and delay.