Predicting Traffic Congestion in Presence of Planned Special Events

The recent availability of datasets on transportation networks with high spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Within these domains, in this paper we focus on the problem of predicting traffic congestion in urban environments caused by attendees leaving a Planned Special Events (PSE), such as a soccer game or a concert. The proposed approach consists of two steps. In the first one, we use the K-Nearest Neighbor algorithm to predict congestions within the vicinity of the venue (e.g. a Stadion) based on the knowledge from past observed events. In the second step, we identify the road segments that are likely to show congestion due to PSEs and map our prediction to these road segments. To visualize the traffic trends and congestion behavior we learned and to allow Domain Experts to evaluate the situation we also provide a Google Earthbased GUI. The proposed solution has been experimentally proven to outperform current state of the art solutions by about 35% and thus it can successfully serve to reliably predict congestions due to PSEs.

[1]  Chetan Gupta,et al.  Forecasting Spatiotemporal Impact of Traffic Incidents on Road Networks , 2013, 2013 IEEE 13th International Conference on Data Mining.

[2]  Chetan Gupta,et al.  Mining traffic incidents to forecast impact , 2012, UrbComp '12.

[3]  Xiaoyan Zhang,et al.  Short-term travel time prediction , 2003 .

[4]  Roland Chrobok,et al.  Different methods of traffic forecast based on real data , 2004, Eur. J. Oper. Res..

[5]  L. Vanajakshi,et al.  A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[6]  Michela Bertolotto,et al.  Integrating Google Earth within OLAP Tools for Multidimensional Exploration and Analysis of Spatial Data , 2009, ICEIS.

[7]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[8]  Eric Horvitz,et al.  Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service , 2005, UAI.

[9]  Emilia Mendes,et al.  Using tabu search to configure support vector regression for effort estimation , 2013, Empirical Software Engineering.

[10]  Michela Bertolotto,et al.  Exploratory spatio-temporal data mining and visualization , 2007, J. Vis. Lang. Comput..

[11]  Laurence R. Rilett,et al.  Spectral Basis Neural Networks for Real-Time Travel Time Forecasting , 1999 .

[12]  Walter H Kraft,et al.  Managing Travel for Planned Special Events , 2007 .

[13]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[14]  Sherif Ishak,et al.  Performance evaluation of short-term time-series traffic prediction model , 2002 .

[15]  Ugur Demiryurek,et al.  Utilizing Real-World Transportation Data for Accurate Traffic Prediction , 2012, 2012 IEEE 12th International Conference on Data Mining.

[16]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[17]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[18]  P. Varaiya,et al.  Components of Congestion: Delay from Incidents, Special Events, Lane Closures, Weather, Potential Ramp Metering Gain, and Excess Demand , 2006 .

[19]  J. W. C. van Lint,et al.  Online Learning Solutions for Freeway Travel Time Prediction , 2008, IEEE Transactions on Intelligent Transportation Systems.

[20]  Qiu Hong-tong,et al.  Study on traffic organization and management strategies for large special events , 2012, 2012 International Conference on System Science and Engineering (ICSSE).