Evaluating Impacts of Traffic Regulations in Complex Mobility Systems Using Scenario-Based Simulations
- URL: http://arxiv.org/abs/2601.07735v2
- Date: Thu, 15 Jan 2026 11:10:18 GMT
- Title: Evaluating Impacts of Traffic Regulations in Complex Mobility Systems Using Scenario-Based Simulations
- Authors: Arianna Burzacchi, Marco Pistore,
- Abstract summary: Urban traffic regulation policies are increasingly used to address congestion, emissions, and accessibility in cities.<n>Recent advances in data availability and computational power enable new forms of model-driven, simulation-based decision support for transportation policy design.<n>This paper proposes a novel simulation paradigm for the ex-ante evaluation of both direct impacts and indirect impacts spanning transportation-related effects and economic accessibility.
- Score: 0.3867363075280543
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Urban traffic regulation policies are increasingly used to address congestion, emissions, and accessibility in cities, yet their impacts are difficult to assess due to the socio-technical complexity of urban mobility systems. Recent advances in data availability and computational power enable new forms of model-driven, simulation-based decision support for transportation policy design. This paper proposes a novel simulation paradigm for the ex-ante evaluation of both direct impacts (e.g., traffic conditions, modal shift, emissions) and indirect impacts spanning transportation-related effects and economic accessibility. The approach integrates a multi-layer urban mobility model combining a physical layer of mobility flows and emissions with a social layer capturing behavioral responses and adaptation to policy changes. Real-world data are used to instantiate the current as-is scenario, while policy alternatives and behavioral assumptions are encoded as model parameters to generate multiple what-if scenarios. The framework supports systematic comparison across scenarios by analyzing variations in simulated outcomes induced by policy interventions. The proposed approach is illustrated through a case study that aims to assess the impacts of the introduction of broad urban traffic restriction schemes. Results demonstrate the framework's ability to explore alternative regulatory designs and user responses, supporting informed and anticipatory evaluation of urban traffic policies.
Related papers
- Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
arXiv Detail & Related papers (2025-11-26T17:01:41Z) - Reimagining Urban Science: Scaling Causal Inference with Large Language Models [39.231736674554995]
Urban causal research is essential for understanding the complex, dynamic processes that shape cities.<n>Current practices are often constrained by inefficient and biased hypothesis formulation.<n>We propose UrbanCIA, a conceptual framework composed of four distinct modular agents responsible for hypothesis generation, data engineering, experiment design and execution, and results interpretation with policy insights.
arXiv Detail & Related papers (2025-04-15T16:58:11Z) - The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation [1.4467930374568725]
Next-venue recommender systems are increasingly embedded in location-based services.<n>We introduce a simulation framework to model the human-AI feedback loop underpinning next-venue recommendation.<n>Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility.
arXiv Detail & Related papers (2025-04-10T17:15:50Z) - Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions [11.633051537198687]
IntSim is a strategy that explicitly decouples the driving intentions of surrounding actors from their motion planning.<n>IntSim achieves state-of-the-art performance in simulating realistic safety-critical scenarios.
arXiv Detail & Related papers (2025-03-07T06:59:27Z) - AI-Driven Scenarios for Urban Mobility: Quantifying the Role of ODE Models and Scenario Planning in Reducing Traffic Congestion [0.0]
This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic congestion dynamics.<n>We assess the role of AI innovations, such as autonomous vehicles and intelligent traffic management, in mitigating congestion under varying regulatory frameworks.
arXiv Detail & Related papers (2024-10-25T18:09:02Z) - Large-Scale Evaluation of Mobility, Technology and Demand Scenarios in the Chicago Region Using POLARIS [0.631976908971572]
Vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics.
Several of these areas of development may or may not have a synergistic outcome on the overall benefits such as reduction in congestion and travel times.
We found different combinations of strategies that can reduce overall travel times up to 7% and increase system efficiency up to 53% depending on how various metrics are prioritized.
arXiv Detail & Related papers (2024-03-04T21:37:29Z) - SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework.
Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations.
We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
arXiv Detail & Related papers (2023-12-31T04:14:43Z) - Urban traffic congestion control: a DeePC change [3.0023888160895473]
This paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights.
Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$$ emissions.
arXiv Detail & Related papers (2023-11-16T12:26:55Z) - Uncertainty Quantification for Image-based Traffic Prediction across
Cities [63.136794104678025]
Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning.
We investigate their application to a large-scale image-based traffic dataset spanning multiple cities.
We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow.
arXiv Detail & Related papers (2023-08-11T13:35:52Z) - Building a Foundation for Data-Driven, Interpretable, and Robust Policy
Design using the AI Economist [67.08543240320756]
We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations.
We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
arXiv Detail & Related papers (2021-08-06T01:30:41Z) - TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation.
In particular, we leverage an implicit latent variable model to parameterize a joint actor policy.
We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
arXiv Detail & Related papers (2021-01-17T00:29:30Z) - MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control [54.162449208797334]
Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city.
Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent.
We propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
arXiv Detail & Related papers (2021-01-04T03:06:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.