Data Driven Tranportation Analytics
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The document discusses the implementation of a data-driven transportation analytics system in Dublin, utilizing the SCATS (Sydney Coordinated Adaptive Traffic System) to monitor traffic conditions through a network of sensors. It emphasizes the importance of real-time and historical data analysis to improve city-planning and transportation services, and outlines the challenges faced in data processing and user interface design. Key lessons include the necessity of domain knowledge for effective data utilization and the need for user involvement in platform development.
Data Driven Tranportation Analytics
- 1. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation Francesco Calabrese, Yiannis Gkoufas IBM Research - Ireland Data driven transportation analytics
- 2. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation Data-driven decision making in Transportation and City-Planning • In the era of Big Data, authorities are provided with a rapidly increasing number of datasets and data-streams to facilitate them in monitoring and planning on a city level • The goal is to empower the city operators to take advantage of those data and provide better services to the citizens • In the last decade, a lot of city-wide sensoring systems have been installed to various cities in the world. Our research focus is the SCATS System in the City of Dublin
- 3. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation SCATS System in Dublin • Intelligent Transportation System which involves installation of sensors on the intersections of the City which monitor and report various KPIs related to traffic flow (saturation, flow, green time, etc) • The Dublin system is deployed across 550 intersections with 3300 sensors in total and generating 150MB/hour • Historical data are publicly available in the Dublinked website: • http://dublinked.com/datastore/datasets/dataset-289.php • http://dublinked.com/datastore/datasets/dataset-305.php • http://dublinked.com/datastore/datasets/dataset-274.php time
- 4. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation Goals of SCATS-Analytics Platform • Leverage both open data stream and historical data obtained from Dublin's SCATS sensors • Run analytics on the raw data and provide insights on real-time • Present the results in a comprehensive manner, easily accessible to the City-operators
- 5. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation SCATS-Analytics: Architecture Overview • Real-time data stream is stored on a database • The analytics run offline on fixed intervals producing the models for each detector • Web application connects to real-time data stream, obtains the trained models and performs online classification and detection Historical Data SCATS Sensors Trained Models For Detectors Analytics WEB Application
- 6. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation SCATS-Analytics: Challenges • The input data were unfiltered and many times contained erroneous entries • The large amount of data volume produced made it challenging to perform analytics • The real-time KPIs were necessary to be extracted as fast as possible in order to give the City-operator prompt input for decision-making • Finally, the results of all those advanced analytics should be presented to the domain expert in a user-friendly and comprehensive way
- 7. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation SCATS-Analytics: State Classification Scheme Free flow F Congestion C Transient states U (due to “network-effects”) Optimal service rate Characterization of network traffic processes under adaptive traffic control systems A. Pascale, T.L. Hoang, R. Nair 21st International Symposium on Transporation and Traffic Theory, 2015
- 8. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation8 SCATS-Analytics: City-wide overview
- 9. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation9 SCATS-Analytics: Historical data inspection
- 10. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation SCATS-Analytics: Lessons Learned • Available Open Data are useful to be incorporated in the process of decision-making, but domain knowledge is required to fully leverage on them. • Investing on improving the quality and the availability of data sources can only be beneficial for the citizens • When designing a platform, it's necessary to keep the end-user involved in the process of the development, looking for feedback and in the end creating a high-level user experience
- 11. © 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation THANK YOU Questions?