Paper Info
Title
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
Abstract
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing origin-destination (OD) car flows between a location of interest and every other location in a city, given their features and the structural characteristics of the graph. We propose three neural network architectures, including graph neural networks (GNN), and conduct a systematic comparison between the proposed methods and state-of-the-art spatial interaction models, their modifications, and machine learning approaches. The objective of the paper is to address the practical problem of estimating potential flow between an urban project location and other locations in the city, where the features of the project location are known in advance. We evaluate the performance of the models on a regression task using a custom data set of attributed car OD flows in London. We also visualise the model performance by showing the spatial distribution of flow residuals across London.
Year
DOI
Venue
2020
10.1109/SMARTCOMP50058.2020.00028
2020 IEEE International Conference on Smart Computing (SMARTCOMP)
Keywords
DocType
ISBN
urban mobility flows,spatial interaction models,graph neural networks,urban computing
Conference
978-1-7281-6998-9
Citations 
PageRank 
References 
0
0.34
18
Authors
5
Name
Order
Citations
PageRank
Gevorg Yeghikyan100.34
Felix L. Opolka201.01
Mirco Nanni3141284.47
Bruno Lepri498172.52
Pietro Liò555099.98