scholarly journals Automated Box-Jenkins forecasting tool with an application for passenger demand in urban rail systems

2015 ◽  
Vol 50 (1) ◽  
pp. 25-49 ◽  
Author(s):  
Saeedeh Anvari ◽  
Selcuk Tuna ◽  
Metin Canci ◽  
Metin Turkay
Keyword(s):  
Passenger Demand ◽  
Rail Systems ◽  
Urban Rail

Optimization for Urban Rail Timetable under Dynamic Passenger Demand on Different Days

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Yu Qiao ◽  
Chunfu Shao ◽  
Tianyu Zhang
Keyword(s):  
Passenger Demand ◽  
Urban Rail

scholarly journals Quantifying the effects of passenger-level heterogeneity on transit journey times

2020 ◽  
Vol 1 ◽  
Author(s):  
Ramandeep Singh ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Large Scale ◽  
Semiparametric Regression ◽  
Related Factors ◽  
Network Characteristics ◽  
Journey Time ◽  
Time Variance ◽  
Assignment Algorithm ◽  
Rail Systems ◽  
Urban Rail ◽  
Potential Applications

Abstract In this paper, we apply flexible data-driven analysis methods on large-scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems. Specifically, we use data from automated fare collection (AFC) and automated vehicle location (AVL) systems to obtain a more precise characterisation of the drivers of journey time variance on the London Underground, and thus an improved understanding of delay. Total journey times are decomposed via a probabilistic assignment algorithm, and semiparametric regression is undertaken to disentangle the effects of passenger-specific travel characteristics from network-related factors. For total journey times, we find that network characteristics, primarily train speeds and headways, represent the majority of journey time variance. However, within the typically twice as onerous access and egress time components, passenger-level heterogeneity is more influential. On average, we find that intra-passenger heterogeneity represents 6% and 19% of variance in access and egress times, respectively, and that inter-passenger effects have a similar or greater degree of influence than static network characteristics. The analysis shows that while network-specific characteristics are the primary drivers of journey time variance in absolute terms, a nontrivial proportion of passenger-perceived variance would be influenced by passenger-specific characteristics. The findings have potential applications related to improving the understanding of passenger movements within stations, for example, the analysis can be used to assess the relative way-finding complexity of stations, which can in turn guide transit operators in the targeting of potential interventions.

Urban rail systems: Analysis of the factors behind success

2002 ◽  
Vol 22 (4) ◽  
pp. 415-447 ◽  
Author(s):  
Ela Babalik-Sutcliffe
Keyword(s):  
Systems Analysis ◽  
Rail Systems ◽  
Urban Rail

scholarly journals Revealing Mobility Regularities in Urban Rail Systems

2020 ◽  
Vol 170 ◽  
pp. 219-226
Author(s):  
Wenhua Jiang ◽  
Zhenliang Ma ◽  
Inhi Kim ◽  
Seonha Lee
Keyword(s):  
Rail Systems ◽  
Urban Rail

scholarly journals Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 782
Author(s):  
Na Zhang ◽  
Zijia Wang ◽  
Feng Chen ◽  
Jingni Song ◽  
Jianpo Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Quantitative Research ◽  
Carbon Emission ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Low Carbon ◽  
Demand Forecast ◽  
Passenger Demand ◽  
Urban Rail

There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023.

scholarly journals Information as the First Attribute of Accessibility: A Method for Assessing the Information Provided by Urban Rail Systems to Tourists with Reduced Mobility

2020 ◽  
Vol 12 (23) ◽  
pp. 10185
Author(s):  
Amanda Fernandes Ferreira ◽  
Yuka Akasaka ◽  
Mirian Greiner de Oliveira Pinheiro ◽  
S. K. Jason Chang
Keyword(s):  
Public Transport ◽  
Information Provision ◽  
Transport Systems ◽  
Online Information ◽  
Insufficient Information ◽  
Rail Systems ◽  
Urban Rail ◽  
The World ◽  
Novel Method ◽  
Reduced Mobility

It is estimated that more than one billion people worldwide have some form of disability, and that number is expected to rise as the population ages. A lack of accessible transport can represent a challenge to commuting citizens and it can also inhibit tourists with reduced mobility. Online information about accessibility is the first point of contact that tourists have with their destination and it should therefore be considered an attribute of accessibility. In that context, this paper aims to: (a) propose a method for classifying the information about accessibility provided by the official websites of public transport systems; and (b) present and discuss the results of the application of the method to the official websites of 212 urban rail systems around the world. The results suggest that, despite it being the first indication of destination accessibility, many cities do not provide or provide unclear or insufficient information for tourists with reduced mobility on their official websites. Moreover, few official websites provide information on accessibility around stations or in the case of transfers. This novel method proved to be suitable for classifying the websites, as well as identifying aspects of the information provision that can be improved.

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