Affiliation:
1. School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, P. R. China
2. School of Economics, Ocean University of China, Qingdao 266100, P. R. China
Abstract
Travel time reliability plays a key role in bus scheduling and service quality. Owing to various stochastic factors, buses often suffer from traffic congestion, delay and bunching, which leads to disturbances of travel time. Automatic vehicle location (AVL) could record the spatiotemporal information of buses, making it possible to understand the status of bus service. In this paper, we specifically analyze the statistical characteristics of travel time based on historic AVL data. Moreover, a Kalman filter-LSTM deep learning is proposed to estimate bus travel time. Numerical tests indicate that the travel time of bus routes shows a left-skewed and right-tail pattern with a good fit of the lognormal distribution. The bus service reliability fluctuates largely in the peak hours, especially the morning peak. Bus bunching and large bus time headway easily occur, and once it occurs, it will continue until destination. The Kalman filter-LSTM model outperforms the ensemble learning methods to predict travel time. This study could provide implications for transit schedule optimization to improve the bus service quality.
Funder
National Natural Science Foundation of China
Youth Innovation Science and technology support project in Colleges and Universities of Shandong Province
Shandong Provincial Natural Science Foundation
Graduate Education Quality Improvement Plan program of Shandong Jianzhu University
Social Science Planning Foundation of Qingdao
Publisher
World Scientific Pub Co Pte Ltd
Subject
Condensed Matter Physics,Statistical and Nonlinear Physics
Cited by
4 articles.
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