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.
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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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-42007-3_66
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