Journal article

Learning to walk: Modeling transportation mode choice distribution through neural networks

Gideon DPA Aschwanden, Jasper S Wijnands, Jason Thompson, Kerry A Nice, Haifeng Zhao, Mark Stevenson

ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE | SAGE PUBLICATIONS LTD | Published : 2019

Abstract

Transportation mode distribution has a large implication on the resilience, economic output, social cost of cities and the health of urban residents. Recent advances in artificial intelligence and the availability of remote sensing data have opened up opportunities for bottom-up modeling techniques that allow understanding of how subtle differences in the urban fabric can impact transportation mode share distribution. This project presents a novel neural network-based modeling technique capable of predicting transportation mode distribution. Trained with millions of images labeled with information from a georeferenced transportation survey, the resulting model is able to infer transportation..

View full abstract