Evaluating Impacts of Traffic Regulations in Complex Mobility Systems Using Scenario-Based Simulations

TL;DR

A novel simulation paradigm for the ex-ante evaluation of both direct impacts spanning transportation-related effects and economic accessibility is proposed, supporting informed and anticipatory evaluation of urban traffic policies.

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, social equity, and economic accessibility. The approach integrates a multi-layer urban mobility model combining a physical layer of networks, 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 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.

Figures (6)

• Figure 1: Schematic representation of the architectural framework for what-if scenario analysis
• Figure 2: Conceptual diagram of the case study, showing the main quantities of interest (i.e., inflow, traffic, and emissions) in the current and modified scenarios. Blue squares represent modeling steps that map inputs to outputs, allowing for the assessment of the modified quantities
• Figure 3: Left: the city of Bologna is highlighted in yellow, while the selected area, subject to the policy, is colored in orange. Center: population distribution in Bologna; the boundary of the selected area is highlighted in black. Right: road network and public transport stops in Bologna; the boundary of the selected area is highlighted in black
• Figure 4: Inflow [veh], traffic [veh], and emissions [g NOx] varying over time for the scenarios A1--A4 of Table \ref{['tab:global']}
• Figure 5: Inflow [veh], traffic [veh], and emissions [g NOx] varying over time for the scenarios A1, A5 and A6 of Table \ref{['tab:global']}