Traffic parameters estimation for signalized intersections based on combined shockwave analysis and Bayesian Network. (July 2019)

• Record Type: Journal Article
• Authors: Wang, Shuling; Huang, Wei; Lo, Hong K.
• Is Part Of: Transportation research. Volume 104(2019)
• Page Start: 22
• Page End: 37
• Publication Date: 2019-07
• Subjects: Signalized intersection -- Shockwave analysis -- Bayesian network -- Expectation maximization -- Vehicle trajectory data; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.04.023
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Combined shockwave analysis and Bayesian Network for traffic parameter estimation. Traffic volume and traffic model parameter estimation using sample vehicle trajectory data. Expectation maximization is introduced to solve the maximum likelihood problem. Sensitivity analysis under different penetration rates and population sizes. Estimation results with mean absolute percentage error bounded by 15% Abstract: This paper proposes a framework to combine shockwave analysis (SA) with Bayesian Network (BN) for traffic flow parameters estimation at signalized intersections using vehicle trajectory data. Traffic SA is applied as the basis to derive the analytical expression of individual vehicle trajectory given the fundamental diagram (FD) parameters (e.g., capacity, jam density, free flow speed) as well as the traffic volume. According to SA, the analytical probability distribution of each vehicle's travel time (defined as the time of a vehicle travelling between an upstream and a downstream location at a signalized intersection) is derived. This probability distribution is parameterized by traffic volume and FD parameters. We then apply a three-layer BN model to construct the relationship between traffic volume, FD parameters, sampled vehicles' arrival times and intersection travel times. Since traffic volume cannot be measured directly from sampled vehicle trajectories, an expectation maximization (EM) algorithm is introduced to perform learning for estimating the cycle-by-cycle volume (or arrivals) and FD parameters. Such estimated parameters are then used, via SA, to estimate vehicles' intersection travel times. The proposed combined SA-BN model is evaluated using microscopic traffic simulation data and the NGSIM dataset. Sensitivity analysis of the estimation performance with respect to penetration rates is conducted. In general, the proposed method yields promising estimation accuracy, with the mean absolute percentage error (MAPE) of the estimation bounded by 15%, generally around 5%, as compared with the true value, even under low penetration rates.