A comfortable environment in railway passenger coaches can be regarded as a resource for social consumption during the transport process. Railway passenger comfort benefits （RPCBs） can be regarded as a special gener...A comfortable environment in railway passenger coaches can be regarded as a resource for social consumption during the transport process. Railway passenger comfort benefits （RPCBs） can be regarded as a special generalized cost. In this paper, we select a series of objective and subjective indexes to formulate a quantitative method of calculating the RPCB with considering ticket fares. This method includes three steps： make the initial data dimensionless, calculate the weight of each index, and finally calculate the RPCBs. The proposed method was validated with the data collected from two types of trains： G13 from Beijing South to Shanghai Hongqiao and T109 from Beijing to Shanghai. Also, questionnaire survey was conducted in both trains. After data processing, the results show that there is a linear relationship between the RPCB and ticket fare with a correlation coefficient of 0.9616.展开更多
The smart card-based automated fare collection （AFC） system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities...The smart card-based automated fare collection （AFC） system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices. Boarding location detection is an important step particularly when there is no automatic vehicle location （AVL） system or GPS information in the database in some cases. With the analysis of raw data without AVL information in this paper, an algorithm for trip direction detection is built and the directions for any bus in operation can be confirmed. The transaction interval between each adjacent record will also be analyzed to detect the boarding clusters for all trips in sequence. Boarding stops will then be distributed with the help of route information and operation schedules. Finally, the feasibility and practicality of the methodology are tested using the bus transit smart card data collected in Guangzhou, China.展开更多
基金jointly supported by the Traffic and Transportation Engineering Experiment and Comprehensive Innovation Center, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuansubsidized by National Natural Science Foundation of China (71173177)+1 种基金China State Railway Administration of Science and Technology Legal Division (KF2013-020)2015 Graduate Innovative Experimental and Practice Program (YC201507103), Southwest Jiaotong University
文摘A comfortable environment in railway passenger coaches can be regarded as a resource for social consumption during the transport process. Railway passenger comfort benefits （RPCBs） can be regarded as a special generalized cost. In this paper, we select a series of objective and subjective indexes to formulate a quantitative method of calculating the RPCB with considering ticket fares. This method includes three steps： make the initial data dimensionless, calculate the weight of each index, and finally calculate the RPCBs. The proposed method was validated with the data collected from two types of trains： G13 from Beijing South to Shanghai Hongqiao and T109 from Beijing to Shanghai. Also, questionnaire survey was conducted in both trains. After data processing, the results show that there is a linear relationship between the RPCB and ticket fare with a correlation coefficient of 0.9616.
基金The United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte (Grant Number: 69A3551747133) for sponsoring this research project entitled ‘estimation of origin–destination matrix and identification of user activities using public transit smart card data’
文摘The smart card-based automated fare collection （AFC） system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices. Boarding location detection is an important step particularly when there is no automatic vehicle location （AVL） system or GPS information in the database in some cases. With the analysis of raw data without AVL information in this paper, an algorithm for trip direction detection is built and the directions for any bus in operation can be confirmed. The transaction interval between each adjacent record will also be analyzed to detect the boarding clusters for all trips in sequence. Boarding stops will then be distributed with the help of route information and operation schedules. Finally, the feasibility and practicality of the methodology are tested using the bus transit smart card data collected in Guangzhou, China.
