Abstract
Traffic congestion is influenced by various factors like the weather, the physical conditions of the road, as well as the traffic routing. Since such factors vary depending on the type of road network, restricting the traffic prediction model to pre-decided congestion factors could compromise the prediction accuracy. In this paper, we propose a traffic prediction model that could adapt to the road network and appropriately consider the contribution of each congestion causing or reflecting factors. Basically our model is based on the multiple symbol Hidden Markov Model, wherein correlation among all the symbols (congestion causing factors) are build using the bivariate analysis. The traffic congestion state is finally deduced on the basis of influence from all the factors. Our prediction model was evaluated by comparing two different cases of traffic flow. We compared the models built for uninterrupted (without traffic signal) and interrupted (with traffic signal) traffic flow. The resulting prediction accuracy is of 79 % and 88 % for uninterrupted and interrupted traffic flow respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
\(\chi \) is a set of congestion reflecting factors that may consist of factors such as occupancy, speed, waiting time, etc.
- 2.
Congestion reflecting factors and congestion causing factors are used interchangeably.
- 3.
The curve representing the change in one factor with change in other factor with time.
- 4.
Interruption: Hindrance in normal flow of traffic because of traffic signal, cross sectional traffic flow, speed limit zone, etc.
References
Road traffic dataset. http://www.dft.gov.uk/traffic-counts/index.php
Antoniou, C., Koutsopoulos, H.N., Yannis, G.: Traffic state prediction using Markov chain models. In: Proceedings of the European Control Conference (2007)
Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo-simulation of urban mobility. In: The Third International Conference on Advances in System Simulation (SIMUL 2011), Barcelona, Spain (2011)
Geroliminis, N., Skabardonis, A.: Prediction of arrival profiles and queue lengths along signalized arterials by using a Markov decision process. Transp. Res. Rec. J. Transp. Res. Board 1934, 116–124 (2005)
Li, X., Parizeau, M., Plamondon, R.: Training hidden Markov models with multiple observations - a combinatorial method. IEEE Trans. Pattern Anal. Mach. Intell. 22(4), 371–377 (2000)
Manual, H.C.: Highway capacity manual, Washington, D.C. (2000)
Murphy, K.: Hidden Markov model (HMM) toolbox for matlab (1998). http://www.ai.mit.edu/~murphyk/Software/HMM/hmm.html
Porikli, F., Li, X.: Traffic congestion estimation using HMM models without vehicle tracking. In: IEEE Intelligent Vehicles Symposium, 2004, pp. 188–193. IEEE (2004)
Qi, Y.: Probabilistic models for short term traffic conditions prediction. Ph.D. thesis, Louisiana State University (2010)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Schenk, J., Schwärzler, S., Rigoll, G.: Discrete single vs. multiple stream HMMs: a comparative evaluation of their use in on-line handwriting recognition of whiteboard notes. In: Proceedings of the International Conference on Frontiers in Handwriting Recognition, pp. 550–555 (2008)
Sen, R., Cross, A., Vashistha, A., Padmanabhan, V.N., Cutrell, E., Thies, W.: Accurate speed and density measurement for road traffic in India. In: Proceedings of the 3rd ACM Symposium on Computing for Development, p. 14. ACM (2013)
Witten, I.H., Frank, E., Trigg, L.E., Hall, M.A., Holmes, G., Cunningham, S.J.: Weka: practical machine learning tools and techniques with Java implementations (1999)
Acknowledgement
The research results have been achieved by Congestion Management based on Multiagent Future Traffic Prediction, Researches and Developments for utilizations and platforms of social big data, the Commissioned Research of National Institute of Information and Communications Technology (NICT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mishra, P., Hadfi, R., Ito, T. (2016). Adaptive Model for Traffic Congestion Prediction. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-42007-3_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42006-6
Online ISBN: 978-3-319-42007-3
eBook Packages: Computer ScienceComputer Science (R0)