Mining Public Transport User Behaviour From Smart Card Data

Agard, Bruno; Morency, Catherine; Trépanier, Martin

doi:10.3182/20060517-3-fr-2903.00211

Cited by 146 publications

(89 citation statements)

References 5 publications

(3 reference statements)

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“…Jang [5] has studied the travel time and transfer activities in Seoul, South Korea using smart card data, which provides a comprehensive travel time map and basic understanding of transit services. By analyzing smart card data collected in Outaouis, Canada, Agard et al [1] have identified different trip habits based on the predefined user types and variabilities of trips against time. Utsunomiya et al [12] pointed out that demand pattern varies with day in week, therefore, different operation schedules should be provided for each day.…”

Section: Introductionmentioning

confidence: 99%

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Sun

Lee

Erath³

et al. 2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

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Rapid tranit systems are the most important public transportation service modes in many large cities around the world. Hence, its service reliability is of high importance for government and transit agencies. Despite taking all the necessary precautions, disruptions cannot be entirely prevented but what transit agencies can do is to prepare to respond to failure in a timely and effective manner. To this end, information about daily travel demand patterns are crucial to develop efficient failure response strategies. To the extent of urban computing, smart card data offers us the opportunity to investigate and understand the demand pattern of passengers and service level from transit operators.In this present study, we present a methodology to analyze smart card data collected in Singapore, to describe dynamic demand characteristics of one case mass rapid transit (MRT) service. The smart card reader registers passengers when they enter and leave an MRT station. Between tapping in and out of MRT stations, passengers are either walking to and fro the platform as they alight and board on the trains or they are traveling in the train. To reveal the effective position of the passengers, a regression model based on the observations from the fastest passengers for each origin destination pair has been developed. By applying this model to all other observations, the model allows us to divide passengers in the MRT system into two groups, passengers on the trains and passengers waiting in the stations. The estimation model provides the spatio-temporal density of passengers. From the density plots, trains' trajectories can be identified and passengers can be assigned to single trains according to the estimated location.

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Section: Introductionmentioning

confidence: 99%

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Sun

Lee

Erath³

et al. 2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

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show abstract

“…Agard et al [3] presented a typical transport planning/data mining approach for travel behavior analysis. Different measures regarding the variability of travel behaviors of transit users were proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this research proposal, a strategic-level analysis of travel behaviours indicators, public transport policies, operational performance, and fare policies was conducted. Agard et al [3] presented that analyzing smart card could aim to better understanding of user behavior, since every single passenger could be followed during his/her journey.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Large-Scale Smart Card Data to Investigate Public Transport Travel Behaviour Using Big Data Analytics

Maktoubian¹

2017

J Inform Tech Softw Eng

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In urban public transport, Smart card data have been used more and more in order to collect fare automatically. They allowed passengers to access almost all type of public transportation system modes (bus, train, tram, funiculars, LRT, metro, and ferryboats) with a single card that is valid for the complete journey. Although Smart card major concentration is in revenue collection, they also generate massive amounts of passive data from the technological devices installed to control the operation of them. Generated data could be beneficial to transit planners which could rise the better understanding of passengers' behavioral patterns for short and long term service planning. However, one of the major challenges is the fact that traditional infrastructures and methods are inefficient when processing or analyzing a large volume of data. Thus, as an alternative, big data technology could be employed to enhance collecting, storing, processing, and analyzing the data. Moreover, the main motivation would be cost-efficiency of this methodology as the cost of processing and analyzing large-scale data is huge. This experience demonstrates that a combination of planning knowledge, big data, and data mining tool allows to produce travel behaviors indicators, public transport policies, operational performance, and fare policies.

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“…This enables more flexible bidirectional operation that can lead to more efficient line utilization. For the third challenge, recent research has shown that transit smart card data can be utilized to better interpret the temporal and spatial dynamics of passenger demand [20,21]. Niu et al [19] used time-varying passenger demand obtained from the high-speed rail system.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Peitong¹,

Sun²,

Liu³

2017

Journal of Advanced Transportation

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Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional "A/B" scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train.

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Mining Public Transport User Behaviour From Smart Card Data

Cited by 146 publications

References 5 publications

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Analyzing Large-Scale Smart Card Data to Investigate Public Transport Travel Behaviour Using Big Data Analytics

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

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