1Big Data for Social Transportation

Abstract—Big data for social transportation brings us unprecedented opportunities for resolving transportation problems that traditional approaches are not competent and building the next generation intelligent transportation systems. Despite that social data has been applied for transportation analysis, there remains many challenges. First, social data evolves with time and contains abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions and precisions are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions for effectively utilizing real-time traffic information. Third, social data is connected to cyber- and physical- spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, like crowdsourcing, visual analysis and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and suggest a few future research directions for this new social transportation field