Applying Fuzzy Neural Networks to Predict Bus Line Patronage

Yang, Xinmiao; Wang, Wei

doi:10.1061/40630(255)91

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jiang [57] uses RBF-ANN and BP-ANN to predict the long-term passenger flow in one-year interval respectively, the results show the accuracy of RBF-ANN is better than BP-ANN. Yang [58] proposes a model based on the theory of adaptive neural fuzzy inference system to predict bus line passenger flow in day time interval. Compared with the AR and ARMA, the test results from the fuzzy ANN based model are better in accuracy.…”

Section: Applications Of Ann In Short-term Bus Passenger Flow Predictionmentioning

confidence: 99%

A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

et al. 2018

View full text Add to dashboard Cite

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.

show abstract

Section: Applications Of Ann In Short-term Bus Passenger Flow Predictionmentioning

confidence: 99%

A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Yang et al used the time-series model and the fuzzy neural network model to predict the short-term bus passenger flow. Because of the nonstationary bus passenger flow, it failed to achieve good prediction result [13]. Zhang et al proposed the Kalman filter as a short-term passenger flow forecasting model for public transit stations and presented the solution process of the model [14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Bus Trip Characteristics and Demand Forecasting Based on NARX Neural Network Model

Sun

Liu

et al. 2018

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

In recent years, there has been increased interest in the use of bus IC card data to analyze bus transit time characteristics, and the prediction is no longer confined to rail traffic passenger flow prediction and traditional traffic flow prediction. Research on passenger flow forecast for the bus IC card has been increasing year by year. Based on the bus IC card data of Qingdao City, this paper first analyzes the characteristics of one-day passenger flow and passenger flow during subperiods and conducts a separate study on the characteristics of the elderly. e results show that the travel of the elderly is also affected by the weekday and the weekend. en, based on the ARIMA model and the NARX neural network model, the passenger flow forecasting (10-minute interval) is carried out using the IC card data of No. 1 bus for 5 weekdays. e prediction results show that the NARX neural network model is effective in the short-term prediction of bus passenger flow, and especially, it is more accurate in the peak hour and large-scale data prediction.

show abstract

“…The priority development of public transport as an effective way to solve the traffic problem in big city has become the consensus of the world, and also become one of the research hot spots in recent years [1][2][3] . For example, LI Qiang evaluated the status quo of public traffic line network in Tieling city of Liaoning Province and revealed facing problems on the development of urban public transportation for the small and medium-sized [4] , YANG Xinmiao forecasted bus routes passenger flow using fuzzy neural network prediction model [5] , WANG Wei had made the detailed elaboration to the planning method and management of public traffic system. But research on urban public transportation development model is less and has not caused enough attention.…”

Section: Introductionmentioning

confidence: 99%

Study on Development Pattern of Public Transportation in Fuzhou City

He¹

2013

AMR

View full text Add to dashboard Cite

According to statistical data of Fuzhou City, using Delphi method and weight method, we established decision matrix of urban public transportation development mode and estimated the urban public transportation development mode. The result show that bus rapid transit system is the optimal public transportation mode for Fuzhou city, followed by conventional public transport system, then, for light rail system. And that bus rapid transit system matching with urban passenger transportation is good in Fuzhou city

show abstract

Applying Fuzzy Neural Networks to Predict Bus Line Patronage

Cited by 3 publications

References 0 publications

A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Analysis of Bus Trip Characteristics and Demand Forecasting Based on NARX Neural Network Model

Study on Development Pattern of Public Transportation in Fuzhou City

Contact Info

Product

Resources

About