Big Data Platform for Smart Transportation Applications with Heterogeneous Data Sources

Hong Kong University of Sciences and Technology (HKUST) and Thales are carrying out research and development to build a Big Data platform to address two critical problems in the public transport.
Indeed, the platform aims at effectively monitoring and directing the crowd in railway stations so that early warnings can be given on potential dangers and (2) at ensuring smooth operation of railway transport system by predicting potential major equipment failure.
The big data platform is, therefore, designed and developed to integrate heterogeneous (ranging from surveillance videos to twitter chats) data and transform them into structure and easy-to- query formats.
In addition, a suite of analytic tools is applied to elicit useful patterns and information from the data. Finally, specific application-drive models are applied to automatically monitor the current situations and make forecasts. A closed-loop optimization module is also designed for decision support.
The platform fuses together many state-of- the-art big-data research topics, such as data fusion, data analytics, human factors, optimization/visualization, transfer learning, simulation and operations research.
The inputs are heterogeneous data sources. The outputs are structured data formats that can be queried. The project is unique to the extent that academia, industry and government work closely together to build an interdisciplinary and cross-domain solution for problems pertinent to Hong Kong and other large cities. The Big Data platform helps public transport agencies to build smart transport solutions in moving people efficiently and safely and, hence, enhancing citizen’s quality of living in a smart city.