A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network

Si, Bingfeng; Fu, Liping; Liu, Jianfeng; Shiravi, Sajad; Gao, Ziyou

doi:10.1002/atr.1309

Cited by 37 publications

(20 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Beijing subway network has been expanding from 4 lines with 114 km in 2006 to 16 lines with 442 km in 2012, leading to a sudden increase of daily ridership from 1.93 million to 6.74 million [34]. Such a burst of ridership stimulates several critical issues such as crowdedness in trains and insufficient Sustainability 2016, 8, 1100 6 of 16 capacity of transferring channels between different lines.…”

Section: Data Sources and Preparationmentioning

confidence: 99%

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

et al. 2016

View full text Add to dashboard Cite

Understanding the relationship between short-term subway ridership and its influential factors is crucial to improving the accuracy of short-term subway ridership prediction. Although there has been a growing body of studies on short-term ridership prediction approaches, limited effort is made to investigate the short-term subway ridership prediction considering bus transfer activities and temporal features. To fill this gap, a relatively recent data mining approach called gradient boosting decision trees (GBDT) is applied to short-term subway ridership prediction and used to capture the associations with the independent variables. Taking three subway stations in Beijing as the cases, the short-term subway ridership and alighting passengers from its adjacent bus stops are obtained based on transit smart card data. To optimize the model performance with different combinations of regularization parameters, a series of GBDT models are built with various learning rates and tree complexities by fitting a maximum of trees. The optimal model performance confirms that the gradient boosting approach can incorporate different types of predictors, fit complex nonlinear relationships, and automatically handle the multicollinearity effect with high accuracy. In contrast to other machine learning methods-or "black-box" procedures-the GBDT model can identify and rank the relative influences of bus transfer activities and temporal features on short-term subway ridership. These findings suggest that the GBDT model has considerable advantages in improving short-term subway ridership prediction in a multimodal public transportation system.

show abstract

Section: Data Sources and Preparationmentioning

confidence: 99%

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

et al. 2016

View full text Add to dashboard Cite

show abstract

“…These results indicate that the performance of the proposed model is as good as the dual-constrained gravity model, while being easier to conduct. The main factors influencing a passenger's route choice are travel time, transfer convenience, level of service, and random factors [26]. Considering these factors, the generalized travel cost function, which describes passengers' travel impedance, can be defined by…”

Section: Forecasting Demand For Existing Stationsmentioning

confidence: 99%

“…An improved logit model was used to calculate the probability of passengers choosing different paths as shown in (8). From this equation, the probability of choosing a specific path is calculated by using the relative travel cost rather than the absolute travel cost that is used in the traditional logit model [26]. This improvement is suitable for trip assignment based on the k-shortest paths.…”

Section: Forecasting Demand For Existing Stationsmentioning

confidence: 99%

Planning for Operation: Can Line Extension Planning Mitigate Capacity Mismatch on an Existing Rail Network?

Zhu

Wang

Chen

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Operational planning in China is perhaps more important today than ever before owing to the ongoing expansion of urban rail in the country. As urban rail networks increase in size and complexity, new lines added to them significantly alter both their topologies and operational characteristics. Thus, appraisal of alternative lines from the perspective of operation while planning is crucial. In this study, a method to forecast demands for new lines and obviate the effects of their addition, in terms of overcrowding in urban rail networks, was developed based on smart card data from existing networks. Using the card data and forecasted demand, transfer demand and section load can be estimated through the route choice model, and hence the influence of new lines on the operation of the network can be analyzed. The results of application of the proposed method to a case of line extension of a network in Beijing showed that it effectively prevented overcrowding by fewer interchanges on the line extension. Approximately 63% of passengers desiring an interchange on the target line altered their interchange from the station that had acted as bottleneck to the new interchange. Consequently, the headway of the feeding line was reduced from 6 min to 3.5 min. Hence, the capacity mismatch problem no longer occurred.

show abstract

“…Si et al. () focused on metro passenger assignment problem, and developed a multiclass transit assignment model for Beijing metro system to estimate transit passenger flows. Based on automated fare collection data, Zhu et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Li et al (2015) proposed the resource allocation model for multiclass services in multipath networks with the objective of utility maximization. Si et al (2016) focused on metro passenger assignment problem, and developed a multiclass transit assignment model for Beijing metro system to estimate transit passenger flows. Based on automated fare collection data, Zhu et al (2014) proposed a methodology based on genetic algorithm for calibrating urban rail transit assignment.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

Yang

Jin

et al. 2017

Computer aided Civil Eng

View full text Add to dashboard Cite

A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network

Cited by 37 publications

References 33 publications

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

Planning for Operation: Can Line Extension Planning Mitigate Capacity Mismatch on an Existing Rail Network?

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

Contact Info

Product

Resources

About