Train operation with arranging conflicts between commuters and travelers in railroad transport to and from a metropolitan airport

Hideshima, Eizo; Koike, Norimitsu; Yamamoto, Kazuhisa; Hadase, H.

doi:10.1109/icsmc.2000.885053

Cited by 2 publications

(3 citation statements)

References 1 publication

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“…The current paper's model is a nonlinear multinomial logit model with two explanatory variables, in-vehicle time and wait time. The combination is consistent with the model by Hideshima et al (9). As discussed later, crowding proved to be statistically insignificant in this case.…”

Section: Literature Reviewsupporting

confidence: 92%

“…Asakura et al, while studying how passengers adjust their express-train choice according to shifting timetables, revealed the diversity in passenger preferences in prioritizing between faster or less crowded trains (8). Hideshima et al developed a linear logit model for the problem with in-vehicle time, wait time, and crowding as explanatory variables based on a field survey (9). The study is focused on mitigating the imbalance in disutility between travelers and commuters that emerged because of the former's relative lack of knowledge on the train schedule.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predicting Express Train Choice of Metro Passengers from Smart Card Data

Kim

Hong

et al. 2016

Transportation Research Record

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This paper addresses the problem of predicting the express local train choices of metro passengers. The model was built and tested on the preferences observed from smart card data. The revealed preference data, because of intensiveness, can also accurately capture the marginal effects of the core attributes, in-vehicle time and wait time, on the express train choices by metro passengers. To be specific, the marginal disutility of a path decreases in in-vehicle time and increases in wait time. Accordingly, this paper employs a Box–Cox transform to adjust the constant marginality of a linear model to the nonconstant marginal disutility. The resulting nonlinear logit model improved the predictability of a conventional linear model. Tested on the Incheon–Yongsan interval of the Gyeong-In Line of the Seoul metropolitan area in South Korea, the model predicted a correct choice by a passenger in 99.9% and 99.5% of the cases during peak and nonpeak hour periods, respectively, compared with 96.9% and 95.8%, respectively, from a linear model. The model, applied to Line 9 without a parameter tuning, achieved a predictability greater than 95%.

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Section: Literature Reviewsupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Predicting Express Train Choice of Metro Passengers from Smart Card Data

Kim

Hong

et al. 2016

Transportation Research Record

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“…For example, Baek and Sohn surveyed passengers of Line 9 in Seoul and developed an express train choice model based on a multinomial logit (MNL) model ( 4). Hideshima et al also conducted a field survey and developed a logit model that considers in-vehicle time, waiting time, and crowding as independent variables (5). Lee et al distinguished the preference between the local train boarded group and the express train boarded using the Gaussian mixture model with smart card data (6).…”

mentioning

confidence: 99%

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Lee

Kim

Kho

et al. 2021

Transportation Research Record

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As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

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Train operation with arranging conflicts between commuters and travelers in railroad transport to and from a metropolitan airport

Cited by 2 publications

References 1 publication

Predicting Express Train Choice of Metro Passengers from Smart Card Data

Predicting Express Train Choice of Metro Passengers from Smart Card Data

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

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