contributor author | Yang Shu;Chen Ming;Wu Yao-Jan;An Chengchuan | |
date accessioned | 2019-02-26T07:56:03Z | |
date available | 2019-02-26T07:56:03Z | |
date issued | 2018 | |
identifier other | %28ASCE%29CP.1943-5487.0000748.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4250368 | |
description abstract | Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposition (TTD) using complete travel times spanning successive links. Previous work has focused on TTD methodologies that use probe vehicle data. However, these approaches may be unsuitable for Bluetooth-based data. Therefore, this study proposes a machine learning–based approach to decomposing Bluetooth-based travel time. A modified hidden Markov model was developed to model travel-time distributions and traffic-state transitions. A genetic algorithm (GA) was applied to solve a numerical optimal decomposition based on maximum likelihood. Two real-world travel-time data sets were used for validation of the approach. The proposed hidden Markov chain with GA (HMMGA) approach and Gaussian mixture model with GA (GMMGA) were compared with a benchmark approach using distance-based allocation. The results showed that the HMMGA significantly outperformed both the GMMGA and benchmark approaches. Using the HMMGA, the average mean absolute percentage error was up to 72% lower compared to the benchmark approach. | |
publisher | American Society of Civil Engineers | |
title | Machine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm | |
type | Journal Paper | |
journal volume | 32 | |
journal issue | 3 | |
journal title | Journal of Computing in Civil Engineering | |
identifier doi | 10.1061/(ASCE)CP.1943-5487.0000748 | |
page | 4018005 | |
tree | Journal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 003 | |
contenttype | Fulltext | |