Second-order models and traffic data from mobile sensors

TL;DR

This paper investigates how sampling frequency and vehicle penetration rate affect the accuracy of mobile sensing in highway traffic, especially for higher-order quantities like acceleration and emissions, using a second-order traffic model and data fusion techniques.

Contribution

It introduces a second-order phase transition model for traffic data analysis and proposes data fusion schemes to improve estimation accuracy of higher-order traffic quantities.

Findings

First-order quantities are accurately estimated at low sampling frequencies.

Higher-order quantities are misinterpreted with insufficient sampling.

A correction factor can improve estimation robustness against low sampling frequency.

Abstract

Mobile sensing enabled by GPS or smart phones has become an increasingly important source of traffic data. For sufficient coverage of the traffic stream, it is important to maintain a reasonable penetration rate of probe vehicles. From the standpoint of capturing higher-order traffic quantities such as acceleration/deceleration, emission and fuel consumption rates, it is desirable to examine the impact on the estimation accuracy of sampling frequency on vehicle position. Of the two issues raised above, the latter is rarely studied in the literature. This paper addresses the impact of both sampling frequency and penetration rate on mobile sensing of highway traffic. To capture inhomogeneous driving conditions and deviation of traffic from the equilibrium state, we employ the second-order phase transition model (PTM). Several data fusion schemes that incorporate vehicle trajectory data into the PTM are proposed. And, a case study of the NGSIM dataset is presented which shows the estimation results of various Eulerian and Lagrangian traffic quantities. The findings show that while first-order traffic quantities can be accurately estimated even with a low sampling frequency, higher-order traffic quantities, such as acceleration, deviation, and emission rate, tend to be misinterpreted due to insufficiently sampled vehicle locations. We also show that a correction factor approach has the potential to reduce the sensing error arising from low sampling frequency and penetration rate, making the estimation of higher-order quantities more robust against insufficient data coverage of the highway traffic.