Cellular Network Based Real-Time Urban Road Traffic State Estimation Framework Using Neural Network Model Estimation

This paper presents real time road traffic state estimation framework together with its evaluation. To evaluate the framework, a three-layer Artificial Neural Network model is proposed and used to estimate complete link traffic state. The inputs to the ANN model include probe vehicle's position, time stamps and speeds. To model the arterial road network the microscopic simulation SUMO is used to generate aggregated speed and FCD export files which are used in the training and evaluation of the ANN model. Besides, real A-GPS data gathered using A-GPS mobile phone on a moving vehicle on the sample roads is used to evaluate the ANN model. The performance of the ANN model is evaluated using the performance indicators RMSE and MPAE and on average the MPAE is less than 1.2%. The trained ANN model is also used to estimate the sample road link speeds and compared with ground truth speed (aggregate edge states) on a 10-minute interval for 1hr. The estimation accuracy using MAE and estimation availability indicated that reliable link speed estimation can be generated and used to indicate real-time urban road traffic condition.

[1]  D. N. Rewadkar,et al.  Review of Different Methods Used for Large-Scale Urban Road Networks Traffic State Estimation , 2013 .

[2]  Ananth Ranganathan,et al.  The Levenberg-Marquardt Algorithm , 2004 .

[3]  Brian Lee Smith,et al.  Wireless Location Technology–Based Traffic Monitoring , 2007 .

[4]  D. Hammerstrom,et al.  Neural networks at work , 1993, IEEE Spectrum.

[5]  Saul Rodriguez,et al.  Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes , 2012 .

[6]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[7]  Kyandoghere Kyamakya,et al.  Mobile positioning technologies in cellular networks: an evaluation of their performance metrics , 2002, MILCOM 2002. Proceedings.

[8]  J. Anderson,et al.  Travel time estimation in urban road networks , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[9]  Francisco G. Benitez,et al.  Review of traffic data estimations extracted from cellular networks , 2008 .

[10]  Yingjie Xia,et al.  Sample size analysis of GPS probe vehicles for urban traffic state estimation , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[11]  Liping Fu,et al.  Reducing bias in probe-based arterial link travel time estimates , 2002 .

[12]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[13]  A. Abraham,et al.  In-Vehicle Mobile Phone-Based Road Traffic flow Estimation : A Review Ayalew , 2015 .

[14]  Martin A. Ferman,et al.  An Analytical Evaluation of a Real-Time Traffic Information System Using Probe Vehicles , 2005, J. Intell. Transp. Syst..

[15]  Guillaume Leduc,et al.  Road Traffic Data: Collection Methods and Applications , 2008 .

[16]  P. A. Osanaiye,et al.  Performance Analysis of ANN on Dataset Allocations for Pattern Recognition of Bivariate Process , 2012 .

[17]  Hans van Lint,et al.  Reliable travel time prediction for freeways , 2004 .

[18]  Johan M Karlsson,et al.  Route classification in travel time estimation based on cellular network signaling , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[19]  Yilin Zhao,et al.  Vehicle Location And Navigation Systems , 1997 .

[20]  J C Aydos.  Stochastic real-time urban traffic state estimation: searching for the most likely hypothesis with limited and heterogeneous sensor data , 2012 .

[21]  A. Scorer.  Positioning Systems in Intelligent Transportation Systems ( ITS ). By Chris Drane and Chris Rizos. Artech House, 1998. 350 pages, £65 Hardback. ISBN: 0-89006-536-5. , 1998 .

[22]  A. G. Scorer.  Vehicle Location and Navigation Systems. By Yilin Zhao. Artech House, 1997. 345 pages, £65 Hardback. ISBN , 1998 .

[23]  Eiji Kamioka,et al.  Pinpoint: An Efficient Approach to Traffic State Estimation System Using Mobile Probes , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[24]  M. Ismail,et al.  MobiTraS: A mobile application for a Smart Traffic System , 2010, Proceedings of the 8th IEEE International NEWCAS Conference 2010.

[25]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[26]  S. P. Hoogendoorn,et al.  Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks , 2002 .

[27]  Ajith Abraham,et al.  Road traffic state estimation framework based on hybrid assisted global positioning system and uplink time difference of arrival data collection methods , 2015, AFRICON 2015.