There is a growing need to optimize mobility in medium to large-size cities. The use of a car for one-person trips is widely established as a common trend, which combined with the age of the vehicle fleet from many countries leads to high levels of pollution. Besides, the time wasted on commuting is more than significant for many people. Under these premises, it is paramount to understand the dynamics of mobility in every city. In this work, the problem of modeling and predicting transport demand in large cities with high spatio-temporal resolution is tackled. The city studied and its metropolitan area are subdivided into a new mobility mesh-grid, and transport demand is binned into short time intervals. The proposed Spatio-Temporal Mobility Demand Forecaster (ST-MDF) model is trained with real mobility demand data (such as taxi and bicycle rental), historical weather data (e.g., temperature, precipitation, and wind speed), and temporal information (e.g., weekday, time, and holiday) to predict mobility demand in every region of the mesh, for several forecast horizons. The experiments show that the ST-MDF model exhibits flexibility and robustness, while at the same time it outperforms the baseline models, such as a Long Short-Term Memory (LSTM) network, or the persistence and naive models.
There is a growing need to optimize mobility in medium to large-size cities. The use of a car for one-person trips is widely established as a common trend, which combined with the age of the vehicle fleet from many countries leads to high levels of pollution. Besides, the time wasted on commuting is more than significant for many people. Under these premises, it is paramount to understand the dynamics of mobility in every city. In this work, the problem of modeling and predicting transport demand in large cities with high spatio-temporal resolution is tackled. The city studied and its metropolitan area are subdivided into a new mobility mesh-grid, and transport demand is binned into short time intervals. The proposed Spatio-Temporal Mobility Demand Forecaster (ST-MDF) model is trained with real mobility demand data (such as taxi and bicycle rental), historical weather data (e.g., temperature, precipitation, and wind speed), and temporal information (e.g., weekday, time, and holiday) to predict mobility demand in every region of the mesh, for several forecast horizons. The experiments show that the ST-MDF model exhibits flexibility and robustness, while at the same time it outperforms the baseline models, such as a Long Short-Term Memory (LSTM) network, or the persistence and naive models. Read More
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