scholarly journals Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

2021 ◽  
Vol 13 (13) ◽  
pp. 7504
Author(s):  
Jie Liu ◽  
Paul Schonfeld ◽  
Jinqu Chen ◽  
Yong Yin ◽  
Qiyuan Peng
Keyword(s):  
Regression Models ◽  
Influential Factors ◽  
Rail Transit ◽  
Linear Regression Models ◽  
Transit Network ◽  
Time Index ◽  
Trip Time ◽  
Buffer Time ◽  
Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

scholarly journals Evaluating Disruption in Rail Transit Network: A Case Study of Beijing Subway

2016 ◽  
Vol 137 ◽  
pp. 49-58 ◽  
Author(s):  
Haodong Yin ◽  
Baoming Han ◽  
Dewei Li ◽  
Ying wang
Keyword(s):  
Rail Transit ◽  
Transit Network ◽  
Beijing Subway

scholarly journals Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

2018 ◽  
Vol 20 (2) ◽  
pp. 281-290 ◽  
Keyword(s):  
Neural Network ◽  
Air Pollutants ◽  
Regression Models ◽  
Network Models ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Prediction Ability ◽  
Topographic Features ◽  
Ann Models

In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels

scholarly journals Regression models tolerant to massively missing data: a case study in solar radiation nowcasting

2014 ◽  
Vol 7 (7) ◽  
pp. 7137-7174 ◽  
Author(s):  
I. Žliobaitė ◽  
J. Hollmén ◽  
H. Junninen
Keyword(s):  
Missing Data ◽  
Solar Radiation ◽  
Regression Models ◽  
Minimum Energy ◽  
Data Streaming ◽  
Linear Regression Models ◽  
Automatic Data ◽  
Minimum Energy Consumption ◽  
Physical Sensors

Abstract. Statistical models for environmental monitoring strongly rely on automatic data acquisition systems, using various physical sensors. Often, sensor readings are missing for extended periods of time while model outputs need to be continuously available in real time. With a case study in solar radiation nowcasting, we investigate how to deal with massively missing data (around 50% of the time some data are unavailable) in such situations. Our goal is to analyze the characteristics of missing data and recommend a strategy for deploying regression models, which would be robust to missing data in situations, where data are massively missing. We are after one model that performs well at all times, with and without data gaps. Due to the need to provide instantaneous outputs with minimum energy consumption for computing in the data streaming setting, we dismiss computationally demanding data imputation methods, and resort to a simple mean replacement. We use an established strategy for comparing different regression models, with the possibility of determining how many missing sensor readings can be tolerated before model outputs become obsolete. We experimentally analyze accuracies and robustness to missing data of seven linear regression models and recommend using regularized PCA regression. We recommend using our established guideline in training regression models, which themselves are robust to missing data.

Sign in / Sign up

Export Citation Format

Share Document