Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

Chaoyang Shi,Qingquan Li,William H K Lam,Bi Yu Chen

doi:10.3390/s17122822

Abstract

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

Highlights

Accurate and robust estimation of travel time distribution, including mean and variance, is a crucial requirement for advanced traveler information systems (ATIS)
The probability of the unknown state for both interval and point detectors was set as αΘ = mint (Θ) = m pos (Θ) = 0.05, and sensitivity parameters in Equations (15) and (16) were set as β int = 0.2 and β poi = 0.8, according to the sensitive analysis results obtained from Dion and
Tint poi od was estimated from interval detector data, whereas T od was estimated from was expected, since Tint poi point detector data through spatial interpolation

Summary

Introduction

Accurate and robust estimation of travel time distribution, including mean and variance, is a crucial requirement for advanced traveler information systems (ATIS). Provision of travel time distribution information through ATIS enables travelers to make reliable path choice decisions, ensuring a higher chance of on-time arrival [1,2,3]. The provided distribution information allows operators to evaluate network performance and reliability, and identify bottlenecks for proactively deploying effective controls to improve overall traffic conditions [4,5]. Recent advances in information and communication technologies (ICTs) have produced a variety of spatiotemporal big data for travel time estimation [6]. Interval detectors consist of a pair of devices deployed in road networks to directly calculate travel times between the device pair.

Methods

Results

Conclusion