Evaluating the Efficiency of Transit-Oriented Development Using Network Slacks-Based Data Envelopment Analysis

Transportation Research Record: Journal of the Transportation R

Kim

Kho

et al. 2021

Self Cite

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.

Section: Description Of Smart Card and Train Log Datamentioning

confidence: 99%

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

Transportation Research Record: Journal of the Transportation R

Kim

Kho

et al. 2021

Self Cite

“…e transit network in Seoul is operated with only a 100% smart card system without any other payment method, for example, cash and ticket, and the smart card data in Seoul provides 99% of transit users' trip information. us, it is widely used for microscopic user behavior analysis [16][17][18].…”

Section: Description Of Smart Card Datamentioning

confidence: 99%

Exploring Transit Use during COVID-19 Based on XGB and SHAP Using Smart Card Data

Journal of Advanced Transportation

2022

Self Cite

As the coronavirus (COVID-19) pandemic continues, many protective measures have been taken in Seoul, Korea, and around the world. This situation has drastically changed lifestyle and travel behavior. An important issue concerns understanding the reasons for giving up transit use and the potential impact on travel patterns during the COVID-19 pandemic. To shed light on these issues that are essential for transit policy, this study explores transit use choice, such as whether users have given-up transit use or not, during the COVID-19 pandemic. Two days of smart card data, before and during the COVID-19 pandemic, were used to look at users who gave up transit use during the COVID-19 pandemic. The choice set of the dataset includes two alternatives, for example, transit use and given-up transit use. An extreme gradient boosting (XGB) model was used to estimate the transit use behavior. Shapley additive explanations were performed to interpret the estimation results of the XGB model. The results for the overall specificity, sensitivity, and balanced accuracy of the proposed XGB model were estimated to be 0.909, 0.953, and 0.931, respectively. The feature analysis based on the Shapley value shows that the number of origin-to-destination trip feature substantially impacts transit use. As such, users tend to avoid transit use as travel time increased during the COVID-19 pandemic. The proposed model shows remarkable performance in accuracy and provided an understanding of the estimated results.

“…With smart card data obtained from the AFC system, it has much attention to estimate the travel route of passengers on subway networks [1]. Seoul's transit fare system charges passengers based on their travel distance, so it is essential to ascertain the passenger's travel routes [2]. Smart card data of the AFC system provide travel route information of bus trips and transfer trips between the bus and subway networks [3,4].…”

Section: Introductionmentioning

confidence: 99%

Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data

Journal of Advanced Transportation

Kim

Kho

et al. 2022

Self Cite

As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.