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

2025-08-04

AI Summary

In construction zones with lane closures, large trucks face high merging safety risks, and conventional methods often require full stops, causing inefficiency. Method: This paper proposes a real-time merging decision-making framework based on dynamic risk assessment. It innovatively integrates an LSTM neural network for temporal traffic conflict prediction and jointly leverages Time-Exposed Time-to-Collision (TET) and Time-Integrated Time-to-Collision (TIT) metrics to enable early risk identification and proactive decision-making during motion. Contribution/Results: The method eliminates the need for complete stopping, allowing trucks to execute safe, active merging while in motion. Experimental results demonstrate a significant reduction in conflict risk compared to baseline approaches, along with superior TET and TIT performance—thereby enhancing both merging safety and traffic throughput in mixed-traffic construction zones.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Predict merging conflict risk for large trucks in work zones
• Develop LSTM-based decision strategy for safe truck merging
• Reduce collision risk compared to baseline merging methods

Innovation

• LSTM neural network predicts merging conflict risk
• Minimum safe gap ensures safe merging process
• Early merging in motion reduces conflict risk