IJSER © 2016 http://www.ijser.org Framework for Traffic Congestion Prediction John FW Zaki 1 , Amr Ali-Eldin, Sherif E. Hussein, Sabry F. Saraya, Fayez F. Areed Abstract— Traffic Congestion is a complex dilemma facing most major cities. It has undergone a lot of research since the early 80s in an attempt to predict traffic in the short-term. Recently, Intelligent Transportation Systems (ITS) became an integral part of traffic research which helped in modeling and forecasting traffic conditions. In this paper, two frameworks for traffic congestion prediction are proposed. The first framework is based on NeuroFuzzy model which is well surveyed in traffic literature. The second framework is based on Hidden Markov Models (HMM) which is rarely used in traffic prediction. The methods are used to define traffic congestion during morning rush hours. The results of the two methods are compared. The empirical evaluation is based on a UK dataset which is provided by the UK Department of Transport. The data is a year on year statistics from 2009 to date and is available in a monthly ''.CSV'' files. It was collected using loop detectors and consolidated every 15 minutes for various links of the UK motorways. Index Terms— NeuroFuzzy, Hidden Markov Models, Traffic Congestion Prediction, Empirical Evaluation —————————— u —————————— 1 INTRODUCTION raffic congestion has become an integral part of to- day's modern life. It forces people to plan additional time whether commuting to work, or traveling for other purposes. It results in longer trip times, lower air quality, and increased fuel wastage which in turn affect the overall quality of life. Therefore, governments, universities, and advanced research are attempting to tackle this problem or at least ease its adverse effects using intelligent transporta- tion systems (ITS). A major part of the ITS is traffic forecasting based on realtime data to enable traffic decision makers to make the right decisions. There are various research methods used in the field of traffic prediction such as deterministic methods, non-deterministic approaches, and stochastic techniques. In this work, two frameworks are proposed for traffic congestion prediction during the morning rush hour. The first framework is based on NeuroFuzzy technique which is well surveyed in traffic literature. The second framework is based on HMM which is rarely used in traffic prediction due to its complex nature. The results of the two methods are compared. The organization of this paper starts with a review perspective of the recent research followed by the introduction of the realtime dataset to be used in the empirical evaluation. Next, the theory behind this research is discussed. Sequentially, the results and discussions, and the concluding remarks are pre- sented. 2REVIEW OF EXISTING TECHNIQUES Short-term traffic forecasting is a challenging research op- portunity. It attracts various researchers using a multitude of methods to attempt forecasting different traffic parameters. In a review paper, Vlahogianni et. al. [1] reviewed 10 challenging research opportunities in the field of ITS focusing on forecast- ing problem in ITS. Recently, Hashemi and Abdelghany [2] developed a real-time traffic state prediction based on closed loop rolling horizon. In their approach, some real-time system deficiencies such as limited prediction accuracy, decision mak- ing latency, and partial coverage of the managed area. In an- other paper, ELHenawy and Rakha [3] detected congestion using two-component mixture model. One is based on free- flow speed distribution and the other is based on congestion speed distribution. The model was calibrated and a threshold was identified where congestion is detected if below the threshold. Dong et. al. [4] proposed a spatio-temporal ap- proach for freeway traffic flow prediction. Their approach shows 5% results improvement over the standard autoregres- sive integrated moving average (ARIMA) model. Yuan et. al. [5] suggested a new model for traffic state estimation based on Lagrangian-space and Kalman Filtering (KF). Their approach provided more accurate numerical results compared to tradi- tional methods in the same coordinate system. Tao et. al. [6] developed a time-space threshold vector error correction (TS- TVEC) model for short-term traffic state prediction. The statis- tical model overcomes unknown structural changes in time T ———————————————— 1. Corresponding Author: John FW Zaki, (M .SC, M BA), is a lecturer assis- tant at the Dept. of Computer and Systems. He is currently pursuing PhD degree in Computer and Systems at Mansoura University, Faculty of Engineering. E-mail: jfzaki@mans.edu.eg International Journal of Scientific & Engineering Research, Volume 7, Issue 5, May-2016 ISSN 2229-5518 1205 IJSER brought to you by CORE View metadata, citation and similar papers at core.ac.uk provided by Leiden University Scholary Publications