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原文传递 Evaluation of Effects from Sample-Size Origin-Destination Estimation Using Smart Card Fare Data

题名：	Evaluation of Effects from Sample-Size Origin-Destination Estimation Using Smart Card Fare Data
其他题名：	Alsger,A.,Mesbah,M.,Ferreira,L.,and Safi,H.(2015)."Use of smart card fare data to estimate public transport origin-destination matrix."Transp.Res.Rec.,2535,88–96.
正文语种：	英文
作者：	Azalden Alsger
关键词：	Origin-destination (O-D) estimation;Public transport demand;Sample size;Public transport smart card fare data;Associated errors
摘要：	Public transport planners are required to make decisions on transport infrastructure and services worth billions of dollars. The decision-making process for transport planning needs to be informed, accountable, and founded on comprehensive, current, and reliable data. One of the major issues affecting the accuracy of the estimated origin-destination (O-D) matrices is sample size. Cost, time, precision, and biases are some issues associated with sample size. Smart card data can potentially provide much information based on better understanding and assessment of the sample size impact on the estimated O-D matrices. This paper uses South East Queensland (SEQ) data to study the effect of different data sample sizes on the accuracy level of the generated public transport O-D matrices and to quantify the sample size required for a certain level of accuracy. As a result, the total number of O-D trips for the whole network can be accurately estimated at all levels of sample sizes. However, a wide distribution of O-D trips appeared at different sample sizes. The large difference from the actual distribution at 100％ sample size was readily captured at small sample sizes where more O-D pairs were not representative. The wide distribution of O-D trips at different levels of sample sizes caused significant errors even at large sample sizes. The variation of the errors within the same sample was also captured as a result of the 80 iterations for each sample size. It is concluded that three major parameters (distribution, number, and sample size of selected stations) have a significant impact on the estimated O-D matrices. These results can be also reflected on the sample size of the traditional O-D estimation methods, such household travel surveys.
出版年：	2017
论文唯一标识：	P-72Y2017V143N04006
英文栏目名称：	TECHNICAL PAPERS
doi：	10.1061/JTEPBS.0000016
期刊名称：	Journal of Transportation Engineering
拼音刊名(出版物代码)：	P-72
卷：	143
期：	04
页码：	41-50