Source-linked AI summary
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
Maonan Wang, Aoyu Pang, Yuheng Kan, Man-On Pun, Chung Shue Chen, Bo Huang
TL;DR
Urban traffic control remains challenging because conventional and RL-based methods can be limited in adapting to changing or unusual conditions. LA-Light places an LLM within a tool-supported TSC framework that combines traffic information with established methods. Simulations across multiple networks and scenarios report reductions in average travel and waiting times without further training.
Problem
Rule-based and RL-based TSC systems have limited ability to adapt to unfamiliar or changing urban traffic, and RL may not handle infrequent critical events effectively.
Source: §1 · p. 2Method
LA-Light integrates an LLM with interoperable tools and established TSC methods to gather traffic information and guide signal-control decisions.
Source: §1 · pp. 2–3Results
Across three traffic networks and nine scenarios, LA-Light required no further training and consistently reduced ATT and AWT compared with traditional methods, while improving emergency-response metrics AETT and AEWT.
Source: §6 · p. 17Takeaways & Limitations
The simulations indicate that LA-Light can respond to varied environmental changes, including rare or unexpected events, supporting its stated practical applicability.
Source: §1 · p. 3Frequent LLM interactions may delay signal adjustments, and textual traffic descriptions may omit details useful for decision-making.
Source: §6 · p. 17
Abstract
from arXiv · showhide
Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion management is imperative, with traffic signal control (TSC) systems being pivotal in this endeavor. Conventional TSC systems, designed upon rule-based algorithms or reinforcement learning (RL), frequently exhibit deficiencies in managing the complexities and variabilities of urban traffic flows, constrained by their limited capacity for adaptation to unfamiliar scenarios. In response to these limitations, this work introduces an innovative approach that integrates Large Language Models (LLMs) into TSC, harnessing their advanced reasoning and decision-making faculties. Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information. This design places the LLM at the center of the decision-making process, combining external traffic data with established TSC methods. Moreover, a simulation platform is developed to corroborate the efficacy of the proposed framework. The findings from our simulations attest to the system's adeptness in adjusting to a multiplicity of traffic environments without the need for additional training. Notably, in cases of Sensor Outage (SO), our approach surpasses conventional RL-based systems by reducing the average waiting time by $20.4\%$. This research signifies a notable advance in TSC strategies and paves the way for the integration of LLMs into real-world, dynamic scenarios, highlighting their potential to revolutionize traffic management. The related code is available at https://github.com/Traffic-Alpha/LLM-Assisted-Light.
1 Introduction
The introduction frames urban traffic control as difficult because conventional and RL-based systems may struggle to adapt to changing or unusual conditions. It presents LA-Light as an LLM-assisted framework that combines traffic information and existing control methods, with simulations reporting performance in typical and rare-event scenarios.
1 Introduction
- Rule-based TSC methods are described as static, while RL systems may overfit traffic patterns or fail to account for infrequent critical events.Examples include sudden emergency-vehicle arrivals and unexpected road blockages.Source: §1 · p. 2
- Simulations report good performance in typical and rare-event scenarios, with qualitative examples of LA-Light analyzing complex conditions and making more reasonable decisions than conventional methods.The authors also describe the system as providing clear explanations for its actions.Source: §1 · p. 3
- LA-Light integrates LLM reasoning into traffic signal control to interpret complex scenarios and respond to unpredictable or rare events conventional systems may overlook.The framework is presented as using nuanced, human-mimetic judgment.Source: §1 · pp. 2–3
- A suite of tools collects environmental information and connects the LLM with the traffic control system through a standardized interface compatible with existing TSC methods.The authors describe the system as a closed-loop process that supports real-time analysis and decision-making.Source: §1 · pp. 2–3
2 Related Work
The related work reviews conventional and RL-based traffic signal control alongside LLM applications, then positions LA-Light as an LLM-centered approach for normal and abnormal traffic conditions that is compatible with established TSC methods.
2.1 Traffic Signal Control Methods
- Conventional methods include fixed-rule approaches and adaptive systems using live sensor data, but may struggle with real-time data use and rapidly changing or complex traffic.The review names Webster, SOTL, SCOOT, and SCATS as examples.Source: §2.1 · p. 3
- RL-based TSC commonly uses queue length, waiting time, intersection pressure, and signal-switching frequency in reward design, while balancing these factors and handling rare events remain challenges.The review notes that an incomplete reward may not direct an agent effectively in unexpected conditions.Source: §2.1 · p. 3
2.2 Large Language Models
- Prior LLM work spans transportation planning, decision support, autonomous vehicles, and traffic analysis, while cited traffic-signal studies focus mainly on standard scenarios and lack compatibility with existing TSC systems.LA-Light is positioned as addressing normal and abnormal conditions with the LLM as the central decision-maker invoking tools.Source: §2.2 · p. 4
3 Preliminaries
The preliminaries define traffic-control concepts using a typical four-legged intersection as their reference. They describe its movements and signals and give an example of movements operating together in a phase.
3 Preliminaries
- The study models a four-way intersection with eight movements, comprising left-turn and straight-ahead paths from four directions, and excludes right turns under its stated assumption.The movements are labeled m1 through m8.Source: §3 · p. 5
- Movement signals indicate whether traffic in a given direction may proceed or must stop; the example has m4 and m8 green for westbound and eastbound left turns.The figure depicts a standard intersection with eight movements and signal phases.Source: Figure 2 · p. 4, §3 · p. 5
- A phase groups movement signals displayed simultaneously so multiple traffic flows can proceed without interference; P4 activates m4 and m8 for opposing left turns.The intersection is described as having four phases, P1 through P4.Source: §3 · p. 5
4 LLM-Assisted Light
LA-Light places an LLM in a hybrid traffic-signal-control process, using perception and decision tools to gather traffic information, inform decisions, and implement signal adjustments.
4.1 Overview of LA-Light
- LA-Light cycles from assigning the LLM’s role and choosing tools through data gathering and analysis to phase implementation with explanatory feedback.Source: Figure 3 · p. 5, §4.1 · pp. 5–6
4.2 Toolkit for TSC
- Its toolkit pairs static intersection and phase descriptions and live occupancy, queue, phase, and unusual-situation data with decision-support and decision-evaluation tools.Source: §4.2 · p. 7
- Evaluate Action Feasibility checks output format and prompts revision when invalid, while Justify Decision Logic explains the LLM’s reasoning to traffic managers.Source: §4.2 · p. 8
4.3 Prompt Design
- The system prompt combines task, tool, and prior-observation context with regulation and safety guidance and an output protocol; per-tool prompts add capability, input, output, and an example.Source: Figure 5 · p. 8, §4.3 · p. 9
5 Experiments
Experiments evaluate LA-Light in simulated synthetic intersections and a Shanghai road network across ordinary and disrupted traffic conditions. Performance comparisons and case studies examine its traffic-control outcomes and decisions.
5.1 Experiment Setting
- GPT-4 Turbo was evaluated without TSC-specific fine-tuning, using SUMO lane detectors that observe traffic up to 150 meters from intersections.The paper notes that this sensing limit may truncate actual queue lengths beyond that distance.Source: §5.1 · pp. 9–10
5.2 Datasets and Scenarios
- The evaluation used three-way and four-way synthetic intersections and an 18-intersection Shanghai network, each tested with emergency vehicles, roadblocks, and sensor outages.The disruption scenarios included ambulances at 1% of traffic, roadblocks during 10% of simulation time, and a 10% chance of sensor failure at any moment.Source: §5.2 · p. 10, Figure 6 · p. 10, Figure 7 · p. 11
5.3 Metrics
- Traffic outcomes were measured using Average Travel Time (ATT) and Average Waiting Time (AWT), with Average Emergency Travel Time (AETT) and Average Emergency Waiting Time (AEWT) tracking emergency vehicles separately.Source: §5.3 · p. 11
5.4 Compared Methods
- Benchmarks included traditional and RL-based TSC methods, plus Vanilla-LLM, which makes decisions directly without Chain-of-Thought reasoning or supplemental decision-support tools.Source: §5.4 · pp. 11–12
5.5 Performance Analysis
- In Shanghai’s Roadblock Incident (RBI) scenario, LA-Light improved ATT by 6.8% and AWT by 11.3% versus UniTSA, the best RL-based approach.Under EMV in Shanghai, LA-Light reduced emergency-vehicle AETT by 15.3% versus Maxpressure; shifting from EMV to SO increased its ATT by 6.9% and AWT by 0.2%.Source: §5.5 · pp. 12, 14
- 7.7% improvement in ATT and 20.4% reduction in AWT over UniTSA were reported for LA-Light in the Shanghai Sensor Outage scenario.In that scenario, LA-Light also reduced ATT and AWT by 20.0% and 35.9%, respectively, compared with Maxpressure.Source: Table 5 · p. 13, §5.5 · p. 14
5.6 Case Study Insights
- Case studies show LA-Light prioritizing emergency vehicles, detecting faulty sensor data, recommending on-site inspection, and avoiding blocked movements when selecting signal phases.In the SO example it selects P1 despite the auxiliary tool's P4 suggestion; in the RBI example it chooses an unobstructed phase after excluding those affected by the blockage.Source: §5.6 · pp. 15–16
6 Conclusion
Across three networks and nine scenarios, LA-Light improves traffic and emergency-vehicle metrics without further training. Its current design is limited by decision delays from frequent LLM interactions and reliance on textual traffic descriptions.
- Across three distinct traffic networks and nine scenarios, LA-Light required no further training and consistently reduced ATT and AWT compared with traditional methods.Source: §6 · p. 17
- LA-Light also improved emergency-response metrics AETT and AEWT.Source: §6 · p. 17
- Its LLM-based decision process identifies suitable tools for each traffic situation and provides clear insights into its rationale.Source: §6 · p. 17
- Frequent LLM interactions introduce delay that could slow signal adjustments, while textual traffic descriptions may omit details needed for effective decisions.Source: §6 · p. 17
- Future work will refine interactions to expedite responses and incorporate vision-based models that process visual information directly.Source: §6 · p. 18