Multi-agent urban transport simulations using OD matrices from mobile phone data
| Autor: | Cuauhtémoc Anda, Pieter J. Fourie, Sergio A. Ordóñez Medina |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
business.product_category
OD matrices Computer science 020209 energy Distributed computing Big data 02 engineering and technology Disaggregation Mobile phone data MATSim Route choice 0502 economics and business 0202 electrical engineering electronic engineering information engineering General Environmental Science 050210 logistics & transportation business.industry 05 social sciences Demand forecasting Traffic count Mobile phone Public transport General Earth and Planetary Sciences Smart card business Transport infrastructure |
| Zdroj: | Procedia Computer Science, 130 ANT/SEIT |
| ISSN: | 1877-0509 |
| Popis: | 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. Procedia Computer Science, 130 ISSN:1877-0509 |
| Databáze: | OpenAIRE |
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