Framework for Highway Traffic Profiling using Connected Vehicle Data

TL;DR

This paper introduces a scalable framework utilizing connected vehicle data for detailed highway traffic profiling, enabling advanced analysis beyond traditional metrics with potential for real-time monitoring.

Contribution

It presents a novel traffic profiling framework leveraging high-quality connected vehicle data, extending traffic analysis capabilities without additional infrastructure costs.

Findings

Feasibility demonstrated on I-280 NJ highway

Framework enables detailed vehicle-level performance analysis

Potential for real-time traffic monitoring

Abstract

The connected vehicle (CV) data could potentially revolutionize the traffic monitoring landscape as a new source of CV data that are collected exclusively from original equipment manufactures (OEMs) have emerged in the commercial market in recent years. Compared to existing CV data that are used by agencies, the new-generation of CV data have certain advantages including nearly ubiquitous coverage, high temporal resolution, high spatial accuracy, and enriched vehicle telematics data (e.g., hard braking events). This paper proposed a traffic profiling framework that target vehicle-level performance indexes across mobility, safety, riding comfort, traffic flow stability, and fuel consumption. The proof-of-concept study of a major interstate highway (i.e., I-280 NJ), using the CV data, illustrates the feasibility of going beyond traditional aggregated traffic metrics. Lastly, potential applications for either historical analysis and even near real-time monitoring are discussed. The proposed framework can be easily scaled and is particularly valuable for agencies that wish to systemically monitoring regional or statewide roadways without substantial investment on infrastructure-based sensing (and the associated on-going maintenance costs)