Public Transportation Service Evaluations Utilizing Seoul Transportation Card Data

Song, Junyoung; Eom, Jin Ki; Lee, Kwang Sub; Min, Jae Hong; Yang, Keun Yul

doi:10.1016/j.procs.2015.05.053

Cited by 8 publications

(3 citation statements)

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“…The smartcard data of Seoul contained individual information for about 14.5 million trips per day, providing 99% of individual transit passenger trip information. Since the smartcards used in Seoul include traffic information for all passengers of public transit, the smartcard data were suitable for analyzing passengers' travel behaviors [28][29][30]. In the case of the urban railway trip information, the smartcard data only recorded the origin and destination station of the passenger trip.…”

Section: Description Of the Smartcard Datamentioning

confidence: 99%

A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

Lee

Cho

et al. 2019

Sustainability

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This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.

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Section: Description Of the Smartcard Datamentioning

confidence: 99%

A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

Lee

Cho

et al. 2019

Sustainability

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“…Song et al [14] develop quantitative indicators for public transportation service evaluation and utilize data mining technique which derives data from the smart card system in the city of Seoul. Isabello et al [15] review efficiency and effectiveness of interurban public transport services of the Piedmont region of Italy.…”

Section: Survey Of Previous Workmentioning

confidence: 99%

Fuzzy Analysis and Evaluation of Public Transport Service Quality: A Case Study of Dar es Salaam City, Tanzania

Massami¹,

Myamba²,

Edward³

2016

JTTs

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Urban public transport plays a critical role in stimulating economic development of any nation since most of the revenues come from cities. The majority of the city dwellers of any country use public transport. The evaluation of public transport service quality provides a valuable feedback to commuter operators to ensure continuous improvement of level of service and to the government to take appropriate measures for enhancing the quality of public transport service. This paper analyses and evaluates service quality of Road Public Transport (RPT) (i.e. minibuses and buses) and Urban Rail Transport (URT) in Dar es Salaam City, Tanzania. Since service quality and its attributes are intangible and vague, a fuzzy evaluation model is developed and applied. The formulated model is composed of Fuzzy Entropy Method (FEM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The overall evaluation procedure is as follows: initially, an intensive literature search and experts' opinions are employed to establish criteria for evaluating the service quality of public transport in Dar es Salaam City. The developed FEM is used to obtain criteria weight. Lastly, the formulated TOPSIS is used to provide an overall ranking of urban public transport service quality. The overall evaluation shows that urban rail transport outperforms road public transport in terms of service quality. Nevertheless, the urban rail transport service in Dar es Salaam City is currently not well developed as it is provided on very limited routes. Thus, the Tanzania government, the rail transport operator i.e. Tanzania Railway Limited (TRL) and the agency responsible for the provision of rail infrastructure i.e. Reli Assets Holding Company (RAHCO) are advised to design and employ Public-Private-Partnership (PPP) schemes i.e. concession contracts to invest more in rolling stocks, locomotives

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“…However, the results have low interpretability and lose information about the original data at the same time. For clustering algorithms, the k ‐means algorithm is widely used and Euclidean distance is often used to measure distance (Song, Eom, Lee, Min, & Yang, 2015). Nevertheless, the k ‐means algorithm needs to set the number of clusters in advance and is sensitive to the initial value (Bao, Xu, Liu, & Wang, 2017; Kim, Kim, Heo, & Sohn, 2017).…”

Section: Introductionmentioning

confidence: 99%

Analysis of passenger flow characteristics and their relationship with surrounding urban functional landscape pattern

Liu

Tang

et al. 2020

Transactions in GIS

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The passenger flow characteristics of rail transit stations and their relationship with surrounding urban space play an important role in urban planning and transit‐oriented development policies. However, passenger flow data are high‐dimensional and complex, and corresponding characteristics are difficult to extract. Moreover, the integration of natural, social and economic factors in urban space may satisfactorily explain passenger flow characteristics. In this study, we clustered stations with passenger flow by using a dynamic time warping (DTW) distance‐based affinity propagation (AP) algorithm. We introduced the urban functional landscape to reveal the urban space surrounding the station by landscape metrics. Then, we explored the relationship between passenger flow characteristics and urban functional landscape pattern. The DTW distance‐based AP algorithm method identified passenger flow characteristics under total, inbound and outbound conditions, with cluster numbers of 3, 5 and 5, respectively. With the distance from the station, the proportion of natural landscape increased (from 26.11 to 36.62%), whereas that of socioeconomic landscape decreased (from 59.87 to 51.57%). The multivariate regression analysis indicated that types of urban functional landscape had significant influence on the passenger flow characteristics in different accessible areas of the station. These findings highlighted the effect of urban functional landscape pattern on passenger flow.

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Public Transportation Service Evaluations Utilizing Seoul Transportation Card Data

Cited by 8 publications

References 0 publications

A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

Fuzzy Analysis and Evaluation of Public Transport Service Quality: A Case Study of Dar es Salaam City, Tanzania

Analysis of passenger flow characteristics and their relationship with surrounding urban functional landscape pattern

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