Towards a cycling city: a large-scale agent-based modelling approach to study cycling for transportation in Melbourne
Creators: name: Jafari, Afshin
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 330409
subject: Transport planning
classificationCode: 330499
subject: Urban and regional planning not elsewhere classified

description: Cycling for transportation offers a promising solution to several key challenges of today’s unhealthy and unsustainable lifestyles in rapidly urbanising cities, from obesity and chronic diseases to congestion and air pollution. Increasing the share of bicycle use requires a supportive environment and well-designed cycling built-environment interventions. However, due to the heterogeneous nature of cycling behaviour, designing interventions to promote cycling is challenging.
 
In this thesis, a city-scale agent-based simulation model for cycling for transport in Greater Melbourne, Australia, was designed, developed and further extended. This model serves as a test-bed for planners to test different built-environment change interventions, such as adding a bike lane to examine the potential impact of the proposed changes on cycling mode share and cyclists travel behaviour and to help decision-makers prioritise cycling infrastructure investments. This model is the first of its kind in Melbourne and Australia.
 
The model development process started with a series of statistical analyses of secondary data from travel survey, census, and smartphone data to explore the factors associated with cycling behaviour in Melbourne. The analysis showed that demographic factors such as age, gender, and employment status, road infrastructure attributes such as bicycle-way availability, road elevation, and intersection attributes such as traffic signals were associated with Melbournians’ decision to cycle and what routes to choose when cycling.
 
A novel open-source algorithm was developed to generate the road network representation based on publicly available data such as OpenStreetMap and Digital Elevation Models. It was demonstrated that the presented algorithm can produce networks of comparable accuracy to the outputs of common network conversion tools while being more than twice as fast when used for simulating different sample sizes. Therefore, the algorithm offers a flexible and adjustable solution for users to create road networks for city-scale active transport modelling while balancing between their desired simulation accuracy and run-time. The algorithm was used to build the Melbourne road network model to be used as input to the agent-based simulation model, and a similar approach can be followed to build similar networks for other cities around the world.

This thesis outlined an open workflow for creating a multi-modal agent-based transport simulation model for Greater Melbourne and as well as the steps taken for mode choice calibration for the four main travel modes of car, public transport, bike and walk. This workflow was used to build a city-wide transport model for Melbourne, referred to as the Activity-based and agent-based Transport model of Melbourne (AToM). AToM is an opensource multi-modal agent-based transport simulation model and simulates a single day of Melbourne’s transport system with a 10% sample population. The mode choice function of the model was calibrated for all four main travel modes of car, public transport, walk, and bicycle. Thus, the model could be used to examine transport mode choice behaviour of the Melbourne travellers in different scenarios and to understand the intended and unintended consequences for the transport system.
 
The first intervention tested with the simulation model was investigating the impact of traffic signals on cycling moving speed. First, a random forest prediction model was used to find physical environment factors that had the highest impact on cycling speed and to predict the speed for all road segments in Greater Melbourne. Aggregated and de-identified smartphone data from Strava Metro along with automated cycling volume and speed sensor data from the Department of Transport were the main data sources used to train and test the random forest model. The variable importance analysis of the random forest model showed that leading to a signalised intersection had the most significant impact on cycling speed among all road attribute factors included. The AToM baseline was extended by incorporating the impact of physical environment attributes on agents’ cycling speed. Then the negative impact of traffic signals on cycling moving speed was removed from the main corridors to Melbourne's Central Business District at morning peak, creating green waves for cyclists using these corridors. The simulation outputs showed that this intervention could result in up to 11.1% increase in cycling volume for these corridors and the potential reduction in travel time of up to 9.5%.
 
The second extension to the AToM baseline was adding different mobility behaviour models for agents from different demographic cohorts. To achieve this, first, a K-means clustering was used to group the different age and sex cohorts of cyclists from the Victorian Integrated Survey of Travel and Activity (VISTA). Clustering variables included how far and for how long survey participants cycled. Four clusters were identified, and the parameters of the mode choice model were estimated for each cluster separately. As a result, cycling agents had a different mode choice model based on the age and gender cohort to which they belonged. This model was used to examine the impact of a city-wide implementation of Melbourne’s Strategic Cycling Corridors, a series of existing and proposed bicycle-ways connecting cyclists to major destinations in Greater Melbourne. The results of the experiments showed an increase of between 133.3% to 154.5% in bicycle mode share across Melbourne, which indicates the importance of a safe and connected bicycle-way network across the city to encourage more cycling.
 
The methods, model development workflow, and the simulation models developed using this workflow that are discussed this thesis filled the gap of a reliable large-scale agent-based models of cycling for transport in Melbourne. Furthermore, a reproducible and open workflow for large-scale cycling agent-based model development was presented that can be followed for building similar models for other cities around the world. In future research, the models could be extended to include how the social environment influences cycling behaviour; and to consider newer modes of transport such as e-bikes and e-scooters. They could also incorporate the health, economic, and environmental impacts of the change in bicycle usage due to built-environment interventions. This is necessary to build the case for fast-tracking the implementation of cycling infrastructure, to increase the uptake of cycling as a form of transport and moving towards an active and sustainable transport system.