Modeling Drivers in Route Diversion Behavior During Congestion: A Pilot Study

Fuller, James David; Cole, Grace; Bian, Ruijie; Wolshon, Brian · 2025 · ROSA P / Maritime Transportation Research and Education Center (MarTREC)

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Summary

This pilot study investigates the factors influencing drivers’ decisions to divert from their normal routes during congestion on coastal road networks. Motivated by the high economic costs of traffic delays and the unique vulnerabilities of coastal infrastructure to hazards like hurricanes, the research aims to improve the understanding of diversion behavior in disrupted networks. While route choice in normal conditions is well-documented, re-routing decisions during disruptions remain complex and poorly understood. The study specifically examines how information-seeking behavior, disruption types, time-related factors, trip characteristics, and demographics affect diversion likelihood during morning and afternoon commutes. The researchers employed a stated preference survey distributed via Prolific to U.S. drivers, collecting 1,051 responses, of which 810 were eligible for analysis. The survey instrument included 58 questions covering routine travel habits, information sources, intended diversion actions under various scenarios (e.g., work zones, adverse weather, incidents), and demographic data. Responses were largely structured on 5-point Likert scales. To analyze the data, the authors developed multinomial logit (MNL) and ordinal logit (OL) models using SPSS and NLOGIT. Due to sample size constraints, the 5-point response scale was simplified to a 3-point scale (No Divert, Unsure, Divert). The MNL models, which demonstrated a better fit, included up to 33 predictor variables for morning commutes and 29 for afternoon commutes after removing multicollinear variables. The results indicate that receiving real-time traffic information significantly increases the likelihood of diversion, supporting the hypothesis that informed drivers are more likely to reroute. Disruption type also played a critical role; drivers unlikely to divert during work zones or adverse weather events were generally less likely to divert during commute trips. Travel time and delay tolerance emerged as strong motivators, with drivers experiencing longer delays being significantly more likely to divert. Demographic factors revealed distinct patterns: drivers aged 18–30 tended to choose "No Divert" during morning commutes, while those with incomes below $100,000 were more likely to be "Unsure" in the morning and less likely to choose "No Divert" in the afternoon. Contrary to initial hypotheses, older drivers and lower-income earners were not uniformly less likely to divert, suggesting more complex behavioral interactions. These findings underscore the importance of timely and reliable traffic information systems, particularly during natural disasters and congestion events. The study highlights that transportation agencies should tailor traffic management strategies and driver information systems to account for demographic differences and varying disruption scenarios. By understanding these behavioral drivers, agencies can better manage traffic flow, reduce congestion, and enhance safety on coastal networks. Additionally, the insights contribute to modeling human routing decisions, which is increasingly relevant as autonomous and connected vehicles integrate into transportation systems.

Key finding

Receiving real-time traffic information and experiencing longer delays significantly increase the likelihood of route diversion, with demographic characteristics further influencing driver uncertainty and decision-making.

Methodology

survey

Sample size: 810

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

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.

• traffic density
• speed choice
• work zones

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).

• Empirical Findings: behavioral performance data, observational prevalence
• Theoretical Contribution: theory or model