Discovering hot routes using license plate number data

Traffic camera plays an important role in intelligent transportation systems (ITS). A major function of traffic camera is license plate number recognition. This paper focuses on discovering city-wide hot routes using license plate number data recorded by traffic cameras deployed throughout the city. This task is challenging due to the following two reasons: First, a vehicle trajectory could usually contribute to only a small portion of a hot route. Second, the high degree of uncertainty of license plate number data makes the existing mining algorithms ineffective. Aiming at these problems, a two-phase method is proposed: First, it extracts hot routes by aggregating the license plate number data from multiple traffic cameras and vehicles. Second, it compresses the mined hot routes based on a clustering and ranking algorithm. We have evaluated our method based on real-world license plate number data from a city-wide traffic camera system.

[1]  Adrian Groza,et al.  Mining traffic patterns from public transportation GPS data , 2010, Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing.

[2]  Zhi-Hua Zhou,et al.  B-Planner: Night bus route planning using large-scale taxi GPS traces , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[3]  Nikos Mitsou,et al.  Traffic mining in a road-network: How does the traffic flow? , 2008, Int. J. Bus. Intell. Data Min..

[4]  M. A. ShehnazBegum,et al.  T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence , 2014 .

[5]  Gaetano Valenti,et al.  Traffic Estimation And Prediction Based On Real Time Floating Car Data , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[6]  Enrique Castillo,et al.  Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations , 2008 .

[7]  Nikos Mamoulis,et al.  Discovery of Periodic Patterns in Spatiotemporal Sequences , 2007, IEEE Transactions on Knowledge and Data Engineering.

[8]  Enrique Castillo,et al.  Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks , 2010 .

[9]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[10]  Xifeng Yan,et al.  CloSpan: Mining Closed Sequential Patterns in Large Datasets , 2003, SDM.

[11]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[12]  Daqing Zhang,et al.  Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces , 2012, Pervasive.

[13]  Helmut Hlavacs,et al.  Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation , 2012, UbiComp '12.

[14]  Jae-Gil Lee,et al.  Traffic Density-Based Discovery of Hot Routes in Road Networks , 2007, SSTD.

[15]  Jiawei Han,et al.  Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach , 2007, VLDB.

[16]  Farnoush Banaei Kashani,et al.  Discovering patterns in traffic sensor data , 2011, IWGS '11.

[17]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[18]  Pietro Liò,et al.  Collective Human Mobility Pattern from Taxi Trips in Urban Area , 2012, PloS one.