1. Presenter: Sihong LIN Adviser: Bei ZHOU 9 July, 2019
Solutions for Improving Transportation in South Africa: Traffic Demand Forecasting of Public Bicycle Station Based on BP Neural Network
Presenter: Sihong LIN
Adviser: Bei ZHOU
9 July, 2019
Mr. Chairman, Ladies and Gentlemen, it is with great sense of honour that I am able to speak at the 38th Southern African Transport Conference.
Today I would like to talk to you about some of my resent research project, and my paper title is….

2. Predict the trip rules of public bicycles in the future
1. BACKGROUND
How to efficiently optimize the deployment of public bicycles?
Supply
Meet the requirements of actual demand and trip rules
Demand
According to existing trip data of public bicycles
(such as weather conditions, travel trajectories, time distribution rules, etc.)
Predict the trip rules of public bicycles in the future
Provide important reference for product operation, transportation planning, road design, etc.
In China, public bicycles paly an important role in alleviating urban traffic congestion, but there is also a problem of imbalance between supply and demand, which not only leads to resource wastage, but also hinders unban traffic order….?
Here is my overall research ideal.
Intelligently allocate the number of public bicycles

3. 2. METHODOLOGY Biological neuron structure
Feature engineering is used to analyse the basic characteristics of data.
The artificial neural network is inspired by human nervous system. And BP neural network is one of the most effective multi-layer neural network learning method.
Biological neuron structure
Multilayer neural network structure

4. 3. RESEARCH PROCESS Data processing Model Building Model training
Data collection
Handling dummy variables
Data standardization
Feature Engineering
Model Building
Forward propagation
Back propagation
Grid initialization
Weight update
Model training
Model test
1.Firstly I gained the data from Center for Machine Learning and Intelligent System at University of California, Irvine. After data processing, Feature engineering is used to analyse the basic characteristics of data.
2.And then the network was constructed based on forward propagation of signal and back propagation of error. The grid was initialized and initial values were then assigned to the weight.
3.Next, for model test, it includes…
4.Finally, for model training, in order to make the model training results reach a better level, I adjusted some parameters, like…
number of iterations
learning rate
number of hidden nodes
Unit test
Function verification
Model accuracy test

5. 4. RESULTS AND DISCUSSION
Prediction results and comparison
1.Multi-linear regression was performed by screening the features with strong correlations with the total traffic demand from correlation coefficient matrix thermogram. And it was used as benchmark model to compare with BP neural network.
2.From fitting curve of BP neural network, we can see that predicted values are basically consistent with the true values.
3.And then, I used Mean Squared Error and Mean Absolute Percentage Error to evaluate the model, and it can be easily seen from the table that the BP neural network prediction model is significantly advanced compared to the Multi-Linear regression model.

6. Public Bicycle Management System
5. RECOMMENDATIONS
Last but not least, I have some recommendations for improving he public bicycle management system in South Africa.
Public Bicycle Management System

7. Student Essay Presenter
With great thanks to:
Sihong LIN
Student Essay Presenter
Tel: (+86)
In clothing I would like to express my deep appreciation to Southern African Transport Conference and School of Highway, Chang’an University making my visit here possible.