Simple and Efficient Prediction of Near Future State of Traffic Using Only Past Speed Information
Intelligent traffic systems attempt to solve the problem of traffic congestion, which is one of the most important environmental and economic issues of urban life. In this study, we approach this problem via prediction of traffic status using past average traveler speed (ATS). Five different algorithms are proposed for predicting the traffic status. They are applied to real data provided by the Traffic Control Center of Istanbul Metropolitan Municipality. Algorithm 1 predicts future ATS on a highway section based on the past speed information obtained from the same road section. The other proposed algorithms, Algorithms 2 through 5, predict the traffic status as fluent, moderately congested, or congested, again using past traffic state information for the same road segment. Here, traffic states are assigned according to predetermined intervals of ATS values. In the proposed algorithms, ATS values belonging to past five consecutive 10-minute time intervals are used as input data. Performances of the proposed algorithms are evaluated in terms of root mean square error (RMSE), sample accuracy, balanced accuracy, and processing time. Although the proposed algorithms are relatively simple and require only past speed values, they provide fairly reliable results with noticeably low prediction errors.
Lighthill MJ, Whitham GB. On Kinematic Waves II: A Theory of Traffic Flow on Long Crowded Roads. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 1955;229(1178): 317-345.
Richards PI. Shock Waves on The Highway. Operations Research. 1956;4(1): 42-51.
Yang L. Stochastic traffic flow modeling and optimal congestion pricing. PhD thesis. University of Michigan; 2017.
Qui Z. Macroscopic traffic state estimation for large scale freeway network using wireless network data. PhD thesis. University of Wisconsin-Madison; 2007.
Škorput P, Mandžuka S, Jelušić N. Real-time Detection of Road Traffic Incidents. Promet - Traffic&Transportation. 2010;22(4): 273-283.
Munoz L, Sun X, Horowitz R, Alvarez L. Traffic Density Estimation with The Cell Transmission Model. Proceedings of American Control Conference, 2003 June 4-6, Denver, CO, USA. Available from: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1240418
Daganzo CF. The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with The Hydrodynamic Theory. Transportation Research Part B: Methodological. 1994;28(4): 269-287.
Daganzo CF. The Cell Transmission Model, Part II: Network Traffic. Transportation Research Part B: Methodological. 1995;29(2): 79-93.
Bosnjak I, Jusufranic I, Visnjic V. Modelling Framework for Dynamic Multiclass Traffic Assignment in ITS Environment. Promet-Traffic & Transportation. 2004;16(2): 71-76.
Gülaçar H, Yaslan YH, Oktuğ SF. Short Term Traffic Speed Prediction Using Different Feature Sets and Sensor Clusters. IEEE/IFIP NOMS 2016 Workshop: International Workshop on Platforms and Applications for Smart Cities (PASC), 2016 Apr 25-29, İstanbul, Turkey.
Available from: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7503000
Kababulut FY, Kuntalp D, Düzenli T. New Methods of Density Estimation for Vehicle Traffic. 9th International Conference on Electrical and Electronics Engineering (ELECO), 2015 Nov 26-28, Bursa, Turkey.
Available from: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7394525
Kay S. Intuitive probability and random processes using MATLAB. New York: Springer US; 2006.
Carrillo H, Brodersen KH, Castellanos JH. Probabilistic Performance Evaluation for Multiclass Classification Using The Posterior Balanced Accuracy. ROBOT 2013: First Iberian Robotics Conference, 2013, Madrid, Spain; p. 347-361.
Copyright (c) 2018 Fevzi Yasin KABABULUT, Damla KUNTALP, Olcay AKAY, Timur Düzenli
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).