Predicting Travel Demand From Origin - Destination Data Using Machine Learning Approach

1. Motivation Approach – Systematic Review
Analysis
Paratransit transport carries about 65% of
passenger trips. However, it is characterized by
long waiting times, no timetables for trips, no
predictive model for demands and many more.
This research in tend improve the planning and
operation of paratransit of trips.
Search Strategy (PRISMA Guideline)
Three research databases; Scopus, Google Scholar and Crossref.
Trip inferences: travel pattern, trip inference, passenger density, travel
demand, and origin-destination.
Machine learning techniques: Machine learning, neural network, decision
tree, computer vision, artificial intelligence, random forest, boosting,
support vector, deep vision, and image processing.
Results
Applying search criteria there were 102 papers for the review.
1. A Spatio-temporal Distribution Model for Determining Origin–
Destination Demand from Multisource Data, S Zhong et al., 2022
2. AI-based neural network models for bus passenger demand
forecasting using smart card data. Sohani et al, 2022
3. Exploring temporal variability in travel patterns on public transit using
big smart card data. X. Zhao 2022
4. Survey of Machine Learning and Deep Learning Techniques for Travel
Demand Forecasting. Nicolai et al, 2021
Predicting Travel Demand From Origin - Destination Data Using
Machine Learning Approach
Ing. Adjei Boateng, Prof. A. Adams, Prof. E. Akowuah, Dr. William Ackaah, Dr. Augutus Ababio-Donkor
Problem Tree Diagram
Implementation Model
Regional Transport Research and Education Center
Instrumentation
Jupyter Notebook
Laptop Python Spyder IDE
Expected outcomes
The evaluation techniques will be applied in this research: Root Mean
Squared Error, Mean Absolute, Structural Similarity Index, least-squares..
The performance of various machine learning models for estimating OD
will be determined.
Objectives
Specific Objectives vs. Problems
The main objective is to use Origin Destination
data to support public transportation service
Question Development (PICO Framework)
How can passenger and trip inferences be estimated for paratransit with
a non-automatic fare collection system using machine learning models?
Develop a framework to collect passenger data seamlessly:
The outcome of the objective will demonstrate how vehicle and passenger
behavior can be quantified. For policymakers to synchronize diverse
intelligent transport projects.
Improve Demand management Strategy:
Operators will be able to balance fluctuating demand with appropriate
supplies after completing the study.
Methodology of model acceptability:
A satisfactory level of accuracy to boost confidence for key transportation
decision-makers to consider implementing the methodology for transport
forecasting.
Contribute to Open Data Movement (OPM)
OPM is a significant global engine of growth for ITS services such travel
information, fleet management, and planning systems. Where open
application programming interfaces can exploit varying ITS services from
the contributions.
The problem tree analysis creates a realistic
overview and awareness of the problem by
identifying the primary causes and their most
important effects.
Jupyter Notebook