Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting
To improve bus passengers’ degree of comfort, it is necessary to determine the real-time crowd coefficient in the bus. With this concern, this paper employed the RBF Neural Networks approach to predict the number of passengers in the bus based on historical data. To minimize the impact of the random...
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Online Access: | http://dx.doi.org/10.1155/2019/2751916 |
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doaj-88a1779a06b54de6a8af5ea2cf14e9802020-11-24T21:44:22ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/27519162751916Determination of Bus Crowding Coefficient Based on Passenger Flow ForecastingZhongyi Zuo0Wei Yin1Guangchuan Yang2Yunqi Zhang3Jiawen Yin4Hongsheng Ge5School of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian 116028, ChinaDepartment of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USASchool of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian 116028, ChinaCollege of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaTo improve bus passengers’ degree of comfort, it is necessary to determine the real-time crowd coefficient in the bus. With this concern, this paper employed the RBF Neural Networks approach to predict the number of passengers in the bus based on historical data. To minimize the impact of the randomness of passenger flow on the determination of bus crowd coefficient, a cloud model-based bus crowd coefficient identification method was proposed. This paper first selected the performance measurements for determining bus crowd coefficient and calculated the digital characteristics of the cloud model based on the boundary values of the selected performance measures under six Levels-of-Service (LOSs). Then the subclouds obtained under the six LOSs were synthesized into a standard cloud. According to the predicted number of passengers in the bus, the passenger density and loading frequency were calculated, which were imported into the cloud generator to set up the bus crowd coefficient identification model. By calculating the crowd degrees of identification cloud and template cloud at each site, this paper determined the crowed coefficient of each bus station. Finally, this paper took the bus line No. 10 in Dalian city as case study to verify the proposed model. It was found that the crowd coefficients of the selected route ranged from 60.265 to 109.825, and the corresponding LOSs ranged between C and F. The method of discriminating bus crowding coefficient can not only effectively determine the congestion coefficient, but also effectively avoid the fuzziness and randomness of the crowding coefficient judgment in the bus, which has strong theoretical and practical significance.http://dx.doi.org/10.1155/2019/2751916 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhongyi Zuo Wei Yin Guangchuan Yang Yunqi Zhang Jiawen Yin Hongsheng Ge |
spellingShingle |
Zhongyi Zuo Wei Yin Guangchuan Yang Yunqi Zhang Jiawen Yin Hongsheng Ge Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting Journal of Advanced Transportation |
author_facet |
Zhongyi Zuo Wei Yin Guangchuan Yang Yunqi Zhang Jiawen Yin Hongsheng Ge |
author_sort |
Zhongyi Zuo |
title |
Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting |
title_short |
Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting |
title_full |
Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting |
title_fullStr |
Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting |
title_full_unstemmed |
Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting |
title_sort |
determination of bus crowding coefficient based on passenger flow forecasting |
publisher |
Hindawi-Wiley |
series |
Journal of Advanced Transportation |
issn |
0197-6729 2042-3195 |
publishDate |
2019-01-01 |
description |
To improve bus passengers’ degree of comfort, it is necessary to determine the real-time crowd coefficient in the bus. With this concern, this paper employed the RBF Neural Networks approach to predict the number of passengers in the bus based on historical data. To minimize the impact of the randomness of passenger flow on the determination of bus crowd coefficient, a cloud model-based bus crowd coefficient identification method was proposed. This paper first selected the performance measurements for determining bus crowd coefficient and calculated the digital characteristics of the cloud model based on the boundary values of the selected performance measures under six Levels-of-Service (LOSs). Then the subclouds obtained under the six LOSs were synthesized into a standard cloud. According to the predicted number of passengers in the bus, the passenger density and loading frequency were calculated, which were imported into the cloud generator to set up the bus crowd coefficient identification model. By calculating the crowd degrees of identification cloud and template cloud at each site, this paper determined the crowed coefficient of each bus station. Finally, this paper took the bus line No. 10 in Dalian city as case study to verify the proposed model. It was found that the crowd coefficients of the selected route ranged from 60.265 to 109.825, and the corresponding LOSs ranged between C and F. The method of discriminating bus crowding coefficient can not only effectively determine the congestion coefficient, but also effectively avoid the fuzziness and randomness of the crowding coefficient judgment in the bus, which has strong theoretical and practical significance. |
url |
http://dx.doi.org/10.1155/2019/2751916 |
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