Building the road network for city-scale active transport simulation models

TL;DR

This paper presents a universal algorithm for creating detailed, efficient road networks for city-scale active transport simulations, balancing accuracy and computational speed, applicable worldwide.

Contribution

The paper introduces a novel, open-data-based algorithm that captures detailed minor roads and infrastructure for active transport modeling, improving scalability and applicability.

Findings

Achieves comparable accuracy to existing tools in shortest path distances.

More than twice as fast as existing network creation methods.

Effectively balances simulation accuracy with computational efficiency.

Abstract

In this paper, we introduce and test our algorithm to create a road network representation for city-scale active transportation simulation models. The algorithm relies on open and universal data to ensure applicability for different cities around the world. In addition to the major roads, their geometries and the road attributes typically used in transport modelling (e.g., speed limit, number of lanes, permitted travel modes), the algorithm also captures minor roads usually favoured by pedestrians and cyclists, along with road attributes such as bicycle-specific infrastructure, traffic signals, and road gradient. Furthermore, it simplifies the network's complex geometries and merges parallel roads if applicable to make it suitable for large-scale simulations. To examine the utility and performance of the algorithm, we used it to create a network representation for Greater Melbourne, Australia and compared the output with a network created using an existing transport simulation toolkit along with another network from an existing city-scale transport model from the Victorian government. Through simulation experiments with these networks, we illustrated that our algorithm achieves a very good balance between simulation accuracy and run-time. For routed trips on our network for walking and cycling it is of comparable accuracy to the common network conversion tools in terms of travel distances of the shortest paths while being more than two times faster when used for simulating different sample sizes. Therefore, our algorithm offers a flexible solution for building accurate and efficient road networks for city-scale active transport models for different cities around the world.