Neural Network-Based Passenger Flow Prediction: Take a Campus for Example

2020 ◽  
Author(s):  
Lijuan YAO ◽  
Shuang ZHANG ◽  
Guocai LI
Keyword(s):  
Neural Network ◽  
Passenger Flow ◽  
Flow Prediction

The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
Author(s):  
Yao Wang ◽  
Dan Zheng ◽  
Shi Min Luo ◽  
Dong Ming Zhan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Good Effect ◽  
Passenger Flow ◽  
Model Based ◽  
Flow Prediction ◽  
Transport Organization ◽  
Railway Passenger ◽  
Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Feng Sun ◽  
Wenheng Su ◽  
Weixuan Liu ◽  
Hui Cao ◽  
Dong Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Arima Model ◽  
Demand Forecasting ◽  
The Elderly ◽  
Ic Card ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Narx Neural Network

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. The results show that the travel of the elderly is also affected by the weekday and the weekend. Then, 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. The 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.

scholarly journals Metro Passenger Flow Prediction Model Using Attention-Based Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 30953-30959
Author(s):  
Jun Yang ◽  
Xuchen Dong ◽  
Shangtai Jin
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Passenger Flow ◽  
Flow Prediction

scholarly journals Study on the Prediction Model of Short-term Bus Passenger Flow Based on Big Data

2018 ◽  
Vol 232 ◽  
pp. 02050 ◽  
Author(s):  
Cheng Wang ◽  
Zhiying Cao ◽  
Xiuguo Zhang ◽  
Weishi Zhang ◽  
Huawei Zhai
Keyword(s):  
Neural Network ◽  
Parallel Algorithm ◽  
Rank Correlation ◽  
Short Term ◽  
Model Learning ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Marquardt Algorithm ◽  
Flow Changes ◽  
Coefficient Method

Prediction of short-term bus passenger flow can help bus managers timely and accurately get the changes of the passenger flow and make scientific and reasonable vehicle scheduling to meet passengers' needs. In this paper, a SLMBP model is constructed to predict the bus passenger flow. The SRCC(Spearman rank correlation coefficient) method is used to determine the factors that have significant influence on passenger flow changes. The Levenberg-Marquardt algorithm is used to optimize the BP neural network to avoid getting stuck in local optimal solutions and prompt the convergence speed. A SLMBP neural network parallel algorithm is constructed to perform multiple stations prediction. The experimental results show that the SLMBP neural network parallel algorithm can not only guarantee the accuracy of short-term passenger flow prediction, but reduce the time spent on model learning and prompt the prediction speed.

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