Field Deployment to Quantify the Value of Real-Time Information by Integrating Driver Routing Decisions and Route Assignment Strategies

Song, Dongyoon; He, Xiaozheng; Peeta, Srinivas · 2014 · ROSA P / NEXTRANS Center (U.S.)

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Summary

This research addresses the limitations of traditional Dynamic Traffic Assignment (DTA) models in predicting the value of Advanced Traveler Information Systems (ATIS). Existing models often rely on restrictive assumptions, such as treating drivers as behaviorally homogeneous or assuming travel time is the sole factor in route choice. These simplifications fail to capture the complex psychological and perceptual responses drivers exhibit when processing real-time information. The study aims to develop a comprehensive modeling framework that integrates driver behavioral responses with route assignment strategies, specifically accounting for the psychological effects of information provision. To achieve this, the authors designed an interactive driving simulator experiment using the Indianapolis road network. The experimental design combined revealed preference data from driving trajectories with stated preference data from a series of surveys. The simulator interface with microscopic traffic simulation software (AIMSUN) to create realistic, dynamic traffic conditions. Participants were exposed to various information scenarios, including generic Variable Message Signs (VMS) and personalized information via personal devices, allowing for the analysis of multiple information sources and potential inconsistencies. Data collection involved an online static survey to capture demographic and socioeconomic attributes, followed by pre-experiment, mid-run, and post-run surveys. These dynamic surveys measured real-time psychological states, such as information processing stress and perception of information accuracy, immediately after information provision to ensure accurate capture of causal responses. The study proposes an integrated route choice model that utilizes latent variable modeling to capture unobservable psychological factors. The model defines latent variables—such as information processing stress, information gap stress, and unfavorableness stress—through observable indicators derived from survey responses. These latent variables are structurally linked to observable explanatory variables, including driver characteristics, situational factors, route attributes, and information characteristics. The framework explicitly incorporates these psychological impacts into the utility function of the discrete choice model, thereby alleviating the restrictive behavioral assumptions found in traditional DTA models. The significance of this work lies in its demonstration of the effectiveness of using interactive driving simulator data to study driver behavior under real-time information provision. The authors argue that simulator-based experiments offer practical merits over field studies, including flexibility in scenario building, enhanced safety, and the ability to collect precise qualitative and psychological data. By moving beyond idealized simulation models, this approach provides a more reliable understanding of the potential costs and benefits of real-time traffic information systems. The findings support the development of more accurate behavioral models that account for human limitations in information processing and the psychological nuances of driver decision-making, which is critical for the future deployment of ATIS technologies.

Key finding

An integrated modeling framework combining interactive driving simulator experiments with latent variable analysis effectively captures the psychological and behavioral impacts of real-time travel information on driver route choice decisions.

Methodology

simulator

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• in vehicle coaching
• simulator validity fidelity
• situational awareness
• feedback modes
• mental model of traffic
• exposure measurement

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Methodological Resource: tool software, validation psychometrics
• Theoretical Contribution: computational model