REDUCING PERCEIVED URBAN RAIL TRANSFER TIME WITH ORDINAL LOGISTIC REGRESSIONS

XUESONG FENG; WEIXIN HUA; XUEPENG QIAN

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REDUCING PERCEIVED URBAN RAIL TRANSFER TIME WITH ORDINAL LOGISTIC REGRESSIONS

机译：使用常规Logistic回归减少可感知的城市轨道交通时间

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摘要

In order to improve the transfers inside an Urban Rail Transit (URT) station between different rail transit lines' this research newly develops two Ordinal Logistic Regression (OLR) models to explore effective ways for saving the Perceived Transfer Time (PTT) of URT passengers, taking into account the difficulty of improving the transfer infrastructure. It is validated that the new OLR models are able to rationally explain probabilistically the correlations between PTT and its determinants. Moreover, the modelling analyses in this work have found that PTT will be effectively decreased if the severe transfer walking congestion is released to be acceptable. Furthermore, the congestion on the platform should be completely eliminated for the evident reduction of PTT. In addition, decreasing the actual transfer waiting time of the URT passengers to less than 5 minutes will obviously decrease PTT.

机译：为了改善不同轨道交通线路之间的城市轨道交通（URT）站内的换乘，这项研究新开发了两种有序逻辑回归（OLR）模型，以探索节省URT乘客感知换乘时间（PTT）的有效方法，考虑到改善交通基础设施的困难。可以验证的是，新的OLR模型能够合理地概率解释PTT及其决定因素之间的相关性。此外，这项工作中的建模分析已经发现，如果严重的转移步行拥堵被释放为可接受的话，PTT将有效降低。此外，应完全消除平台上的拥塞，以明显减少PTT。此外，将URT乘客的实际中转等待时间减少到5分钟以内将明显减少PTT。

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来源
《Promet-traffic & transportation》 |2019年第5期|593-602|共10页
作者
XUESONG FENG; WEIXIN HUA; XUEPENG QIAN;
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作者单位

School of Traffic and Transportation Beijing Jiaotong University No.3 Shangyuancun Haidian District Beijing 100044 P.R. China;

College of Asia Pacific Studies Ritsumeikan Asia Pacific University 1-1 Jumonjibaru Beppu 874-8577 Japan;

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正文语种 eng
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关键词
perceived transfer time; perceived transfer waiting time; ordinal logistic regression model; urban rail transit service improvement;

机译：感知的传输时间;感知的转移等待时间;有序逻辑回归模型;城市轨道交通服务改善;

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1. Bayesian network modeling explorations of strategies on reducing perceived transfer time for urban rail transit service improvement in different seasons [J] . Feng Xuesong, Li Kemeng, Ding Chuanchen, Cities . 2019,第Deca期

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2. Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models [J] . Sebastian Tunny, Jeyaseelan Visalakshi, Jeyaseelan Lakshmanan, Statistical methods in medical research . 2019,第5期

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3. Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time [J] . Rose D Baker, Christian Weinand, James C Jeng, BMC Medical Research Methodology . 2009,第1期

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4. Ordinal Logistic Regression Modeling Research on Decreasing Perceived Metro Transfer Time [C] . Xuesong Feng, Weixin Hua, Xuejun Niu International Conference on Green Intelligent Transportation Systems and Safety . 2018

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5. A study of the behaviors of test statistics in the binary logistic regression model, the proportional odds ordinal logistic regression model, and the proportional hazards model [D] . McBride, Mark Leon 2000

机译：二元逻辑回归模型，比例赔率序数逻辑回归模型和比例风险模型中检验统计量行为的研究
6. Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time [O] . Rose D Baker, Christian Weinand, James C Jeng, 2009

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7. Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time [O] . Pape Sarah A, Monstrey Stan, Hoeksema Henk, 2009

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REDUCING PERCEIVED URBAN RAIL TRANSFER TIME WITH ORDINAL LOGISTIC REGRESSIONS

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