Analyzing the Spatial and Temporal Characteristics of Subway Passenger Flow Based on Smart Card Data

2018 ◽  
pp. 121-151
Author(s):  
Xiaolei Ma ◽  
Jiyu Zhang ◽  
Chuan Ding
Keyword(s):  
Smart Card ◽  
Temporal Characteristics ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Spatial And Temporal Characteristics

Analysis of subway passenger flow based on smart card data

2020 ◽  
Author(s):  
Yi Wang ◽  
Weilin Zhang ◽  
Fan Zhang ◽  
Ling Yin ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Smart Card ◽  
Passenger Flow ◽  
Smart Card Data

scholarly journals Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoqing Dai ◽  
Lijun Sun ◽  
Yanyan Xu
Keyword(s):  
Model Selection ◽  
Smart Card ◽  
Probabilistic Model ◽  
Travel Demand ◽  
Combination Method ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Smart Card Data

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Exploring Temporal Characteristics of Bus Trips in Tian’anmen Area Using Smart Card Data

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Ying Xiong ◽  
Guangquan Lu ◽  
Xiaolei Ma ◽  
Haode Liu ◽  
Xianglong Liu
Keyword(s):  
Smart Card ◽  
Temporal Characteristics ◽  
Smart Card Data

scholarly journals Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Mobility Patterns ◽  
Transit Planning ◽  
Transit Systems ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Depth Analysis ◽  
Spatio Temporal ◽  
The Impact

AbstractModern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

scholarly journals A data-driven estimation method for potential passenger demand of last trains in metro based on external traffic data

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989835
Author(s):  
Wei Li ◽  
Qin Luo
Keyword(s):  
Smart Card ◽  
Real World ◽  
Estimation Method ◽  
Data Driven ◽  
Traffic Data ◽  
Passenger Flow ◽  
Passenger Demand ◽  
Total Potential ◽  
Input Condition ◽  
Smart Card Data

The last train problem for metro is especially important because the last trains are the last chances for many passengers to travel by metro; otherwise, they have to choose other traffic modes like taxis or buses. Among the problems, the passenger demand is a vital input condition for the optimization of last train transfers. This study proposes a data-driven estimation method for the potential passenger demand of last trains. Through the geographic information, external traffic data including taxi and bus are first analyzed separately to match the origin–destination passenger flow during the last train period. A solving solution for taxi and bus is then developed to estimate the potential passenger flow for all the transfer directions of the target stations. Combining the estimated potential passenger flow and the actual passenger flow obtained by metro smart card data, the total potential passenger demand of last trains is obtained. The effectiveness of the proposed method is evaluated using a real-world metro network. This research can provide important guidance and act as a technical reference for the metro operations on when to optimize the last train transfers.

scholarly journals Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao Fu ◽  
Yu Gu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Rapid Development ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Mobility Patterns ◽  
Passenger Flow ◽  
Smart Card Data ◽  
The Impact ◽  
Metro Network

Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. The appearance of new metro lines may significantly affect passenger flow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger flow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). The impact of the new metro line on passenger flow distribution and travel time in the metro network is first analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. The results show that the new metro line influences passenger flow, travel time, and travel time reliability in the metro network and has different impacts on different categories of commuters.

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