Modelling passenger waiting time using large-scale automatic fare collection data: An Australian case study

Tavassoli, Ahmad; Mesbah, Mahmoud; Shobeirinejad, Ameneh

doi:10.1016/j.trf.2018.06.037

Cited by 25 publications

(17 citation statements)

References 14 publications

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“…However, transfer demand was not considered. Tavassoli et al [35] estimated passenger wait time for nontransfer and transfer passengers, while assuming the travel path is given. However, the paths with more than one transfer were not considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Xiaoyue

Chien

2020

Journal of Advanced Transportation

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The service quality of public transit, such as comfort and convenience, is an important factor influencing ridership and fare revenue, which also reflects the passengers’ perception to the transit performance. Passengers are frustrated while waiting to board a crowded train especially during the peak hours, while the fail-to-board (FtB) situation commonly exists. The service performance measures determined by deterministic passenger demand and service frequency cannot reflect the perceived service of passengers. With the automatic fare collection system data provided by Chengdu Metro, we develop a data-driven approach considering the joint probability of spatiotemporal passenger demand at stations based on posted train schedule to approximate passenger travel time (e.g., in-vehicle and out-of-vehicle times). It was found that the estimated wait time can reflect the actual situation as passengers FtB. The proposed modeling approach and analysis results would be useful and beneficial for transit providers to improve system performance and service planning.

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Section: Literature Reviewmentioning

confidence: 99%

Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Xiaoyue

Chien

2020

Journal of Advanced Transportation

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“…The majority of the existing contributions focused on developing methodologies for PT performance assessment. In the reviewed articles, big data was used to estimate regular performance measures such as quality of PT service using GSM data 1 , physical and schedule-based connections of metro user using quadruple 33 , bus arrival time using smart card data 79 , left behind passenger using smart card data and AVL data 80 , accessibility to PT service using mobile phone data 43 , passenger waiting time using smart card data 64 , and spatial variations of urban PT ridership using GPS trajectories and smart card data 66 . Further, Min et al 50 proposed a method to recover the arrival times of trains from the exit times of metro passengers.…”

Section: Measurement Of Performance Assessment Indicatorsmentioning

confidence: 99%

“…Trip-chaining and linked trip analysis can be further extended for inferring trip purpose, analyzing spatial and temporal travel pattern, and analyzing route choice behavior of passenger 32,39 . To improve the performance of PT service, research is needed to determine the relationship between passenger travel demand and performance indicator such as speed of vehicle, quality of service, accessibility to PT, and passenger waiting time 22,43,64 .…”

Section: Performance Measuresmentioning

confidence: 99%

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Zannat

Choudhury

2019

J Indian Inst Sci

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Introduction Rapid urbanization and associated increase in population are resulting in a higher growth of motorized traffic flow in urban areas. As a consequence, cities are experiencing different problems such as air pollution, road accidents, and congestions. In response to these problems, public transportation (PT) could help to reduce air pollution, road congestion and travel time, and dependency on non-renewable energy, which benefit both

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“…), the AFC data has been used in the researches of transportation engineering. These studies are mainly focused on four fields: prediction of passenger flow [ 2 , 7 , 10 , 11 , 12 ], analysis of passenger flow patterns [ 13 ], investigation of passenger behaviors [ 14 , 15 ], and evaluation of metro networks [ 3 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Zhou

Wang

Zhao

2020

Sensors

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The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.

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Modelling passenger waiting time using large-scale automatic fare collection data: An Australian case study

Cited by 25 publications

References 14 publications

Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

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