CHAPTER 1

Introduction

Background

Traffic simulation modeling is one tool used by many state departments of transportation (DOTs) to improve the operations of their transportation systems. Operational traffic simulation models can be used to facilitate the planning, design, and real-time operations of transportation systems. They can vary in temporal and spatial resolutions but typically focus on solving problems at a specific location in a near-term time horizon (e.g., improving the traffic signal timing along a corridor, identifying the causes of an existing bottleneck, assessing the mobility impacts of a planned construction project, or decision-making on alternative selection). Although the use of traffic simulation models is commonplace across many DOTs, there are differences in how DOTs implement these models. Therefore, there is a need to enhance the understanding of how DOTs create, calibrate, use, and report information from traffic simulation models.

Objectives and Scope

The objective of this synthesis is to review and document DOT practices for operational traffic simulation models. The scope of the synthesis includes the following topics:

• The extent to which DOTs use traffic simulation models
  • – Administrative procedures or protocols that mandate or restrict the use of simulation models (e.g., legislative code, agency policies)
  • – Use of traffic simulation models by staff or contractors
• The typical applications of specific traffic simulation models (e.g., design, corridor studies, work zone planning)
  • – Type of project
  • – Type of software used—including specialized features and companion tools
• Factors considered for model scoping (e.g., procedures and policies, funding and scheduling constraints, geographic extent, travel conditions, temporal extent of models)
• Methods and guidelines for the development, implementation, and quality control of simulation models (including calibration, validation, and review); maintenance; and archiving
• Data collection or acquisition practices
  • – Data sources used as inputs for operational models (e.g., probe data; regional travel demand forecasting model outputs)
  • – The age of data and the frequency of updating the data inputs
  • – Data fusion
• Model performance review practices
  • – Return on investment (qualitative and quantitative)
  • – Model reuse and adaptation
  • – Post-construction verification

• Skill set development within the agency (e.g., staff training) for policies and procedures as well as for simulation software

Definitions

For the purposes of this synthesis, the following definitions are used (Alexiadis et al. 2004; Zhou et al. 2021):

• Department of transportation (DOT): An agency from among the 50 states or the District of Columbia that may use operational traffic simulation models.
• Operational traffic simulation model: A mathematical representation of a transportation system that is developed using computer software; it simulates the movement of traffic over a user-defined transportation network and provides results through reports and animation. An operational simulation model is a tool used to facilitate the planning, design, and operations of transportation systems and may be macroscopic, mesoscopic, or microscopic.
• Macroscopic simulation model: A model that represents a higher-level approach, with large spatial and temporal resolutions that are used for applications that do not require detailed operational performance measures. These models use the deterministic relationships of flow, speed, and density. They require less computational effort than microsimulation models but also provide less detail than microscopic models.
• Microscopic simulation model: A model that mimics the movements of individual vehicles through a transportation network and tracks vehicle-to-vehicle interactions. Vehicles enter the transportation network through a stochastic process, are tracked in small units of time, and are given a destination, vehicle type, and driver type. Microsimulation models are used for detailed analyses with a smaller geographic footprint. They often require more modeling effort than other modeling resolutions (i.e., macroscopic and mesoscopic) to provide a higher level of detail.
• Mesoscopic simulation model: A model that acts as a hybrid between macroscopic and microscopic models. This model represents transportation networks with a higher level of resolution than that of macroscopic simulation models but with less fidelity for individual driver behavior and vehicle interactions than that of microscopic simulation models. Although mesoscopic simulation models are based on the estimated driving dynamics of individual vehicles, the movement of vehicles is represented on a segment or link basis as opposed to on the individual vehicle trajectories present in microsimulation.
• Multiresolution simulation model (MRM): A model based on an integrated approach that uses varying temporal and spatial resolutions (i.e., macroscopic, mesoscopic, and microscopic simulation) to answer a given question. As shown in Figure 1, MRM structures can be partial or full.

Additional details regarding the differences between modeling resolutions are provided in Figure 2.

Synthesis Methodology

The synthesis approach included a literature review, a survey, and case examples. The existing literature, including guidance documents (general and DOT-specific), research reports, journal articles, and other resources, was reviewed and synthesized. An online survey was distributed to the DOTs for all 50 states and the District of Columbia. Responses were received from 49 DOTs, a 96% response rate. Follow-up interviews were conducted with representatives from six DOTs in order to develop case examples of DOT experiences with operational traffic simulation modeling.

Synthesis Organization

The chapters of this synthesis are organized as follows:

• Chapter 2 describes the comprehensive literature review for operational traffic simulation models, including guidance documents (general and DOT-specific), research reports, journal articles, and other resources.
• Chapter 3 provides information on DOT practices based on the survey results.
• Chapter 4 contains case examples from six DOTs.
• Chapter 5 presents a summary of synthesis findings and recommendations for future research.
• Appendices (Table 1) provide supplemental information for the synthesis.

Table 1. Synthesis appendices.