Multi-agent urban transport simulations using OD matrices from mobile phone data

Although new available big data sources have revealed themselves to be extraordinarily useful for transport demand modelling, they have not come into widespread use due to the justifiable privacy concerns of data stewards. In this study, we step back and re-evaluate the way in which mobile phone telco data can be introduced for the task of transport and land-use policy evaluation, travel demand forecasting and transport infrastructure testing through large-scale transportation simulations. We investigated that question by deploying a multi-agent transport simulation driven primarily by hourly-aggregated telco Origin-Destination (OD) matrices. We address the principal four challenges: spatial and temporal disaggregation, mode imputation and route choice. For temporal disaggregation, we propose a convolution with an exponential kernel method. As for transport mode imputation, a supervised-learning framework is designed. The simulation results are compared against traffic count data and public transport smart card transactions, showing accurate patterns for private cars but overestimated public transport demand in the morning peak. Lastly, we set the future steps for the improvement of simulations driven by aggregated mobile phone data. (C) 2018 The Authors. Published by Elsevier B.V.