Real-Time Conflict Prediction for Large Truck Merging in Mixed Traffic at Work Zone Lane Closures

TL;DR

This paper presents an LSTM neural network approach to predict merging conflicts for large trucks in work zones, enabling safer and earlier merging maneuvers in mixed traffic conditions.

Contribution

The study introduces a novel LSTM-based conflict prediction method that outperforms baseline strategies in reducing merging conflicts and facilitating early merging for large trucks.

Findings

Lower conflict risk indicated by reduced TET and TIT values.

Large trucks can merge earlier and more safely using the proposed method.

Outperforms probabilistic and gap-based baseline approaches.

Abstract

Large trucks substantially contribute to work zone-related crashes, primarily due to their large size and blind spots. When approaching a work zone, large trucks often need to merge into an adjacent lane because of lane closures caused by construction activities. This study aims to enhance the safety of large truck merging maneuvers in work zones by evaluating the risk associated with merging conflicts and establishing a decision-making strategy for merging based on this risk assessment. To predict the risk of large trucks merging into a mixed traffic stream within a work zone, a Long Short-Term Memory (LSTM) neural network is employed. For a large truck intending to merge, it is critical that the immediate downstream vehicle in the target lane maintains a minimum safe gap to facilitate a safe merging process. Once a conflict-free merging opportunity is predicted, large trucks are instructed to merge in response to the lane closure. Our LSTM-based conflict prediction method is compared against baseline approaches, which include probabilistic risk-based merging, 50th percentile gap-based merging, and 85th percentile gap-based merging strategies. The results demonstrate that our method yields a lower conflict risk, as indicated by reduced Time Exposed Time-to-Collision (TET) and Time Integrated Time-to-Collision (TIT) values relative to the baseline models. Furthermore, the findings indicate that large trucks that use our method can perform early merging while still in motion, as opposed to coming to a complete stop at the end of the current lane prior to closure, which is commonly observed with the baseline approaches.