A Models Comparison to Estimate Commuting Trips Based on Mobile Phone Data
Upon an overall human mobility behavior within the city of Rio de Janeiro, this paper describes a methodology to predict commuting trips based on the mobile phone data. This study is based on the mobile phone data provided by one of the largest mobile carriers in Brazil. Mobile phone data comprises a reasonable variety of information about subscribers’ usage, including time and location of call activities throughout urban areas. This information was used to build subscribers’ trajectories, describing then the most relevant characteristics of commuting over time. An Origin-Destination (O-D) matrix was built to support the estimation for the number of commuting trips. Traditional approaches inherited from transportation systems, such as gravity and radiation models – commonly employed to predict the number of trips between locations(regularly upon large geographic scales) – are compared to statistical and data mining techniques such as linear regression, decision tree and artificial neural network. A comparison of these models shows that data mining models may perform slightly better than the traditional approaches from transportation systems when historical information are available. In addition to that, data mining models may be more stable for great variances in terms of the number of trips between locations and upon different geographic scales. Gravity and radiation models work very well based on large geographic scales and they hold a great advantage, they are much easier to be implemented. On the other hand, data mining models offer more flexibility in incorporating additional attributes about locations – such as number of job positions, available entertainments, schools and universities posts, among others –and historical information about the trips over time.
Keywordshuman mobility behavior trips prediction transportation models pattern recognition
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