Sweden/Michigan naturalistic field operational test - phase 1 : benefits of origin and destination information in IntelliDrive data sets

Dion, Francois; Robinson, R. · 2009 · ROSA P / University of Michigan. Transportation Research Institute

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Summary

This report, produced by the University of Michigan Transportation Research Institute, investigates the benefits and implications of collecting origin-destination (O-D) data from IntelliDrive (formerly Vehicle-Infrastructure Integration) probe vehicles. The research was motivated by the potential for wireless vehicle-infrastructure communication to transform vehicles into data collection instruments, offering a more reliable and frequent source of trip data than traditional methods. However, this capability raised significant concerns regarding driver privacy, prompting an examination of whether the benefits of O-D data collection outweigh the privacy risks and costs. The study employs a multi-faceted approach, beginning with a comprehensive review of current O-D data applications in transportation planning and system operations. It analyzes existing data collection methods, including direct traveler surveys and indirect generation through socioeconomic modeling, highlighting their limitations in cost, frequency, and accuracy. The report then evaluates how IntelliDrive-derived O-D data could enhance these applications, particularly in dynamic routing and traffic management. A central component of the methodology is a simulation case study using the Paramics microscopic traffic simulation model. This study modeled a major incident scenario on a network near Novi, Michigan, to assess the impact of dynamic route guidance provided by IntelliDrive vehicles reporting their O-D information. The simulation compared network performance with and without active navigation rerouting, analyzing specific vehicle groups based on their ability to reroute around the incident. The findings indicate that O-D data is fundamental to transportation planning, signal optimization, corridor management, and emergency evacuation procedures. The report concludes that IntelliDrive O-D data can significantly enhance these applications by providing real-time, high-resolution travel patterns. The simulation results demonstrated that dynamic routing based on O-D data effectively mitigates the propagation of traffic disruptions caused by incidents. Vehicles able to reroute experienced reduced delays, and the overall network performance improved as traffic was distributed away from the incident area. However, the study also identified that privacy concerns are a major barrier to implementation. It reviewed emerging policies and mitigation methods, noting that while privacy protection measures are necessary, they may impact the granularity and utility of the data for certain applications. The significance of this research lies in its balanced assessment of the trade-offs between operational efficiency and driver privacy. It provides a framework for evaluating the benefit-cost ratio of including O-D data in IntelliDrive datasets. The report suggests that while the technical benefits for network operations and planning are substantial, successful deployment depends on establishing robust privacy policies and mitigation strategies. This work supports the development of advanced traveler information systems and informs policy decisions regarding the scope of data collection in connected vehicle environments.

Key finding

Simulation results demonstrate that using O-D data from IntelliDrive vehicles to provide dynamic route guidance around incidents reduces travel times and improves overall network performance compared to scenarios without active routing.

Methodology

modeling

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 (45 acquisition events logged).

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

Topics

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• exposure measurement
• traffic density

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: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource