Modeling Large-Scale Walking and Cycling Networks: A Machine Learning Approach Using Mobile Phone and Crowdsourced Data

TL;DR

This study develops a machine learning model to estimate daily walking and cycling volumes across a large regional network using crowdsourced, mobile phone, and other data sources, addressing data limitations and providing valuable insights for urban planning.

Contribution

It introduces a novel machine learning approach that integrates multiple data sources to model active transportation at a large scale, overcoming data bias and scarcity issues.

Findings

Successfully modeled over 300,000 links for walking and cycling.

Identified and mitigated outliers in model estimates.

Provided a scalable methodology for transportation planning.

Abstract

Walking and cycling are known to bring substantial health, environmental, and economic advantages. However, the development of evidence-based active transportation planning and policies has been impeded by significant data limitations, such as biases in crowdsourced data and representativeness issues of mobile phone data. In this study, we develop and apply a machine learning based modeling approach for estimating daily walking and cycling volumes across a large-scale regional network in New South Wales, Australia that includes 188,999 walking links and 114,885 cycling links. The modeling methodology leverages crowdsourced and mobile phone data as well as a range of other datasets on population, land use, topography, climate, etc. The study discusses the unique challenges and limitations related to all three aspects of model training, testing, and inference given the large geographical extent of the modeled networks and relative scarcity of observed walking and cycling count data. The study also proposes a new technique to identify model estimate outliers and to mitigate their impact. Overall, the study provides a valuable resource for transportation modelers, policymakers and urban planners seeking to enhance active transportation infrastructure planning and policies with advanced emerging data-driven modeling methodologies.