Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models
Journal article, 2023

Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics. Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modelling.

Traffic simulation

Sensitivity analysis

Intelligent transportation systems

Spatial interaction model

Passenger flow prediction

Transport planning

Author

Omkar Parishwad

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Sida Jiang

WSP Sverige

Kun Gao

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Multimodal Transportation

27725871 (ISSN) 27725863 (eISSN)

Vol. 2 3 100085

Areas of Advance

Information and Communication Technology

Transport

Building Futures (2010-2018)

Life Science Engineering (2010-2018)

Driving Forces

Sustainable development

Innovation and entrepreneurship

Subject Categories

Transport Systems and Logistics

Environmental Management

Computer Science

Roots

Basic sciences

DOI

10.1016/j.multra.2023.100085

Related datasets

Github:: parishwadomkar [dataset]

URI: https://lnkd.in/eAPiRg6s

More information

Latest update

10/5/2023