SimTIO: A Simulation-Grounded Multi-Agent LLM Framework for Compositional Traffic Intervention Optimization

Shuyang Li; Ruimin Ke

cs.MASep 4, 2026

SimTIO: A Simulation-Grounded Multi-Agent LLM Framework for Compositional Traffic Intervention Optimization

Authors: Shuyang Li, Ruimin Ke

Organizations: Civil and Environmental Engineering, Rensselaer Polytechnic Institute Troy, New York, 12180

Abstract

Traffic analysts must translate diagnosed bottlenecks into executable interventions without allowing local improvements to degrade network-wide performance. This study presents SimTIO, a simulation-grounded multi-agent large language model framework for composing and selecting traffic interventions under explicit operational constraints. SimTIO first simulates an unmodified SUMO scenario to identify a baseline-frozen set of ten bottleneck edges. A grounded sampler then initializes signal-control, corridor-speed, and demand-preserving routing actions, while three specialist agents use measured simulation feedback to select one-parameter refinements from validator-confirmed mutation catalogs. Compatible actions are combined and re-simulated so that their interaction effects are measured rather than inferred. Final selection minimizes bottleneck time loss while constraining network-wide delay, neighboring-road spillover, throughput loss, and teleport events, with the unmodified scenario retained as a no-operation guard. Across 15 cases covering five U.S. urban networks, three synthetic-demand seeds, and 2,400 origin-destination trips per scenario, SimTIO reduced Top-10 bottleneck time loss by an average of 9.18 percent and network-wide delay by 2.78 percent. It found a feasible improving plan in 86.7 percent of cases, compared with 73.3 percent for grounded random search and 80.0 percent for a deterministic heuristic under the same seven-simulation budget, although the differences in Top-10 improvement were not statistically significant. These results support using LLMs as constrained, feedback-guided local search operators while reserving final decision authority for executable tools, microscopic simulation, and explicit safety constraints.

Figures & tables

Figure 1: Simulation-grounded multi-agent traffic optimization framework. Grounded sampling initializes diverse interventions; specialist LLM agents refine measured parents, after which compatible actions may be composed before constrained final selection.

Figure 2: Intervention mechanisms supported by the optimizer: fixed-time signal green-split adjustment, corridor-level speed control, and partial rerouting around a diagnosed bottleneck. Grounded initialization selects the initial targets and parameters, while an LLM feedback specialist selects a validator-confirmed one-parameter refinement. Deterministic utilities apply the corresponding SUMO modifications.

City	Edges	Junctions	TLS	Trips	Delay (s)
Albany	6,193	4,309	107	2,400	$112.80\pm 0.26$
Troy	6,107	4,355	57	2,400	$99.79\pm 0.84$
Ann Arbor	10,151	7,522	81	2,400	$138.58\pm 3.13$
Los Angeles	6,969	4,954	172	2,400	$175.76\pm 1.26$
Manhattan	5,993	4,065	256	2,400	$245.31\pm 13.03$

Table 1: Equal-demand SUMO scenarios. Delay is baseline mean vehicle time loss, reported as mean

\pm

sample standard deviation across three seeds.

Component	Setting
Network source	Five urban road networks derived from OpenStreetMap data (© OpenStreetMap contributors).
LLM	OpenAI Chat Completions API; gpt-5.6-luna ; model-default sampling; JSON response mode; 500 output tokens; 60-s timeout and at most two retries after the initial request, including one structured repair attempt for an invalid catalog selection.
Search budget	At most seven candidate SUMO evaluations per case. LLM search uses three grounded initial proposals, up to two feedback mutations, and up to two compatible bundles; each baseline uses five proposals and up to two bundles.
Candidate space	Signal timing, five-edge corridor speed control, single-edge rerouting, and validated topology actions. Numeric choices are discrete, and Top-10 ranking excludes edge fragments shorter than 10 m.
Signal timing	Fixed-time cycle and green-split plans derived using Webster’s method; original phase states and yellow/all-red transition intervals are preserved.
SUMO	Version 1.27.1; 1-s step; complete execution until all vehicles arrive; trip-level statistics and emissions plus 60-s edge measurements.

Table 2: Reproducibility settings for the main comparison.

Method	Valid (%)	Success (%)	Best nets.	Sim. eff. (%)	Top-10 (%)	Global (%)
LLM feedback	100.0	86.7	3/5	35.1	9.18	2.78
Grounded random	100.0	73.3	1/5	34.0	9.00	3.03
Heuristic	97.3	80.0	1/5	39.0	9.60	2.69

Table 3: Budget-matched proposal-source comparison over five networks and three demand seeds. Success denotes a feasible selected plan with at least 1% Top-10 reduction.

Figure 3: Proposal-source comparison over three demand seeds. Bars in (a) show mean Top-10 reduction and sample-standard-deviation error bars. Panel (b) reports case-level optimization success and candidate-level simulation efficiency under the common seven-evaluation cap.

Network	Top-10 (%)	Global delay (%)	Sim. eff. (%)	Success
Albany	2.25 $\pm$ 2.11	0.17 $\pm$ 0.52	21.1	2/3
Troy	8.55 $\pm$ 11.17	0.75 $\pm$ 1.27	28.6	2/3
Ann Arbor	8.25 $\pm$ 5.83	1.75 $\pm$ 0.46	30.0	3/3
Los Angeles	5.58 $\pm$ 6.84	0.51 $\pm$ 1.28	31.6	3/3
Manhattan	21.27 $\pm$ 14.23	10.71 $\pm$ 7.55	66.7	3/3
Overall	9.18 $\pm$ 10.18	2.78 $\pm$ 5.08	35.1	13/15

Table 4: Five-network performance of the LLM-feedback optimizer over three demand seeds. Reductions are mean

\pm

sample standard deviation in percentage points and include no-op outcomes.

Figure 4: Search outcomes over 15 city–seed cases. Panel (a) reports the intervention family selected by each proposal source. Panel (b) separates initial, feedback-refined, bundled, and no-op outcomes for the LLM search.

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Intelligent Transportation Systems apply sensing, communication, AI, and optimization to make transport networks safer and more efficient.

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Decoupled Intelligence: A Multi-Agent LLM Framework for Controllable Traffic Scenario Generation in SUMO

Shuyang Li, Ruimin Ke

Civil and Environmental Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, United States.

Multi-Agent LLM Systems Intelligent Transportation Systems LLM Agent Orchestration

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The integration of Large Language Models (LLMs) with microscopic traffic simulation offers a promising path toward autonomous urban planning and intelligent transportation analysis. However, existing monolithic agent architectures often struggle with the complexity of end-to-end simulation workflows, leading to reasoning failures, parameter inconsistency, and a lack of systematic state management. This paper proposes a novel multi-agent collaborative framework designed to automate the entire lifecycle of traffic simulation in SUMO (Simulation of Urban Mobility). Our approach decouples the simulation pipeline into specialized roles, including Planner, Builder, Demand, Runner, and Analyst, coordinated by a high-level reasoning engine. We introduce a state-persistent Orchestrator leveraging the Model Context Protocol (MCP) to ensure seamless data handover and environmental consistency across distributed agent actions. This architecture enables a robust closed-loop refinement process, where simulation outcomes are iteratively analyzed and optimized to satisfy user-defined Key Performance Indicators (KPIs). Experimental results through role ablation studies demonstrate that the proposed multi-agent framework significantly enhances task success rates and parameter accuracy compared to single-agent baselines. Furthermore, case studies on real-world network extraction and traffic optimization highlight the system's capability to bridge the gap between high-level natural language intent and low-level simulation execution.

Abstract

Intelligent Transportation Systems

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