ABSTRACT
The integration of information and communications technologies across existing transportation infrastructure, systems and vehicles is fundamental to reducing traffic congestion, to improving driver safety, and to improving traveler experiences. Central to such intelligent traffic management are techniques and algorithms that are capable of analyzing the wealth of available contextual sensor data in "real time". Initial existing approaches tend to apply probability models and inference techniques to optimize traffic flow but fail to take into account certain aspects of human behavior that can affect the flow of traffic, such as patterns in human travel behavior. In this paper we explore how vehicle context information can be combined with the behavioral patterns of travelers to facilitate and improve intelligent traffic management. We present services for deriving reports on vehicle journeys that assist in the analysis of route performance, for enabling passengers to have remote access to real-time route performance information, and for the observation, learning, and utilization of human travel behavior patterns. These services provide essential traffic analysis information that is ultimately expected to lead to further improvements in intelligent traffic management, which aims at easing the flow of traffic in urban and suburban environments.
- Chen, M. and Chien, S., Dynamic Freeway Travel Time Prediction using Probe Vehicle Data: Link-based vs. Path-based, Proceedings of the Transportation Research Board 81st Annual Meeting, National Research Council, Washington D.C., (January 2002).Google Scholar
- Cushman and Wakefield Healey and Baker, European Distribution Report, 8, (2003), (http://www.investinflanders.com/library/documents/Other%20Publications/European_Distribution_Report.pdf).Google Scholar
- Dey, A. K., Salber, D., Abowd, G. D., A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Human-Computer Interaction (HCI) Journal, Vol. 16 (2--4), pp. 97--166, (2001). Google ScholarDigital Library
- Dresner, K., and Stone, P., Multi-agent Traffic Management: A Reservation-Based Intersection Control Mechanism, Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-agent Systems, pp530--537 (July 19--23, 2004, New York, NY). Google ScholarDigital Library
- Dresner, K., and Stone, P., Sharing the road: Autonomous vehicles meet human drivers, Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India, (January 2007). Google ScholarDigital Library
- Dublin Transportation Office, A Platform for Change Final Report, An Integrated Transportation Strategy for the Greater Dublin Area 2000 to 2016, 13, (November 2001), (http://www.dto.ie/platform1.pdf).Google Scholar
- Froehlich, J. and Krumm, J., Route Prediction from Trip Observations, in Society of Automotive Engineers (SAE) World Congress (Detroit, Michigan USA, 2008) Paper 2008-01-0195.Google ScholarCross Ref
- Irish Department of Transport, Statement of Strategy 2008--20010, Comments of the Competition Authority, pp10--11 (Sept 2007), (http://www.transport.ie/upload/general/9749-0.pdf).Google Scholar
- Krumm, J., Real Time Destination Prediction Based on Efficient Routes. SAE 2006 Transactions Journal of Passenger Cars - Electronic and Electrical Systems, (2006).Google Scholar
- Liao, L., Fox, D., and Kautz, H., Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields, in International Journal of Robotics Research, (2007). Google ScholarDigital Library
- Liao, L., Kautz, H., Fox, D., Learning and inferring Transportation Routines, Proceedings of the 19th National Conference on Artificial Intelligence (AAAI), (2004). Google ScholarDigital Library
- Patterson, D., Liao, L., Gajos, K., Collier, M., Livic, N., Olson, K., Wang, S., Fox, D. and Kautz, H., Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services, UbiComp 2004: Ubiquitous ComputingGoogle Scholar
- STREAMS real time bus passenger information system, Intelligent Transport System Queensland, (http://www.itsq.com.au/news/Traveller_Information.pdf).Google Scholar
Index Terms
- Using context and behavioral patterns for intelligent traffic management
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