Mobility Patterns using Big Data

Transport planners mostly rely on transport demand models for the understanding of mobility behavior and the planning of network infrastructure. Transport travel demand models heavily rely on high-cost and hard-to-update travel surveys as a data source and thus cannot be updated regularly. Furthermore, travel habit surveys provide detailed and in-depth information on the travel habits of those sampled for the survey; these surveys are conducted at the city or region level, with other, lower scope surveys not allowing the required information on the national travel system to be generated with appropriate resolution. Additionally, traditional collection methods result in an overview of the mobility of one weekday; therefore, they only provide a snapshot of people’s movement since they cover a limited sample of the population and a small-time window. As a result, travel demand models may not reflect the variability in travel and travel changes over time. Therefore, new data sources that are richer and more available are needed. This research stream focuses on the analysis of various sources of big data, including one of the most extensive cellular surveys of its kind carried out thus far in the world.