Framework for Highway Traffic Profiling Using Connected Vehicle Data
DOI: 10.1109/icite56321.2022.10101480
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the potential of commercial connected vehicle (CV) data to revolutionize traffic monitoring by offering high-resolution, vehicle-level insights that surpass traditional aggregated metrics. The authors propose a traffic profiling framework designed to ingest massive datasets from original equipment manufacturers (OEMs), such as Wejo, which provide nearly ubiquitous coverage, high temporal resolution, and enriched telematics data including waypoints, speed, heading, and acceleration events. The motivation stems from the limitations of existing sensing infrastructure and the emergence of large-scale CV data that can support transportation planning, incident management, and systemic roadway monitoring without significant capital investment. The methodology involves a monitoring framework that categorizes traffic performance into five indices: mobility, safety, riding comfort, traffic flow stability, and fuel consumption. These metrics are calculated for 0.5-mile roadway segments over 30-minute intervals. The safety index combines speed coefficient of variation, speed drop relative to the limit, and heading changes. The comfort index is derived from the percentage of braking events and high jerks (acceleration derivatives). The stability index counts large acceleration and braking events, while fuel consumption is estimated using a polynomial model based on instantaneous speed and acceleration. A proof-of-concept study was conducted on Interstate 280 in New Jersey using data from May and June 2021, a period that included a fatal crash causing lane closures. The results demonstrate the framework’s ability to detect and quantify traffic disruptions. During the fatal crash event, the system identified significant congestion upstream, with speeds dropping below 10 mph and a corresponding spike in the number of waypoints per vehicle, indicating slowed travel. The safety index highlighted areas of high vulnerability, such as the toll plaza and segments near dense ramps, where abrupt heading changes and speed variations were prevalent. The comfort and stability indices revealed higher scores in westbound traffic and near interchanges, correlating with merging activities and hard braking/acceleration events. Additionally, the fuel consumption analysis showed increased usage during nighttime hours due to higher speeds and on specific segments with grade inclines. The data also captured the cross-directional impact of the crash, showing disruptions in westbound traffic corresponding to the eastbound incident. The significance of this work lies in its demonstration that commercial CV data can provide a comprehensive, granular picture of traffic conditions beyond traditional volume and speed metrics. The proposed framework enables agencies to monitor mobility, safety, comfort, stability, and environmental impact simultaneously. This approach supports proactive traffic management and historical analysis, offering a cost-effective alternative to infrastructure-based sensing. The study confirms that CV data can effectively identify traffic vulnerabilities and disruptions, paving the way for scalable, real-time monitoring systems that enhance transportation operations and planning.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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Information type
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- Empirical Findings: observational prevalence, crash risk outcomes
- Methodological Resource: dataset resource