Arrival flow profile estimation and predication for urban arterials using license plate recognition data

TL;DR

This paper presents a novel method for estimating and predicting urban arterial arrival flow profiles using license plate recognition data, addressing limitations of traditional methods by incorporating traffic wave features and signal timing effects.

Contribution

It introduces a new approach employing a time partition algorithm and platoon dispersion model to accurately estimate and predict arrival flow profiles from boundary data.

Findings

Method accurately estimates arrival flow profiles in simulations.

Approach effectively predicts profiles under various signal timings.

Validation shows robustness and promising performance.

Abstract

Arrival flow profiles enable precise assessment of urban arterial dynamics, aiding signal control optimization. License Plate Recognition (LPR) data, with its comprehensive coverage and event-based detection, is promising for reconstructing arrival flow profiles. This paper introduces an arrival flow profile estimation and prediction method for urban arterials using LPR data. Unlike conventional methods that assume traffic homogeneity and overlook detailed traffic wave features and signal timing impacts, our approach employs a time partition algorithm and platoon dispersion model to calculate arrival flow, considering traffic variations and driving behaviors using only boundary data. Shockwave theory quantifies the piecewise function between arrival flow and profile. We derive the relationship between arrival flow profiles and traffic dissipation at downstream intersections, enabling recursive calculations for all intersections. This approach allows prediction of arrival flow profiles under any signal timing schemes. Validation through simulation and empirical cases demonstrates promising performance and robustness under various conditions.