Leveraging Machine Learning Algorithms to Perform Online and Offline Highway Traffic Flow Predictions

Moussavi-Khalkhali, Arezou; Jamshidi, Mo

doi:10.1109/icmla.2014.75

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…When data is used for any kind of decision-making and some of the data is missing, the most important input for the decision-making is to quantify the uncertainty due to the missing data. The only knowledge about the traffic characterisics of the location of the missing data that was needed are the parameters μ 1 , σ 2 1 and mutual correlations ρ 12 and ρ 13 . In the reconstruction, we assumed availability of the historical data but, if no data is available from the location, then justified guess estimates for the values of these parameters already yield estimates that may be useful.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Considering machine-learning methods, both parametric and nonparametric approaches have been applied in the literature. Moussavi-Khalkhali and Jamshidi, [13] used similar loop detector data and they attempt to predict traffic flows with the use of Multi-Layer Perceptrons and Principal Component Analysis. Recently, the most prominent approaches have revolved around Bayesian Networks and, above all, Neural Networks.…”

Section: Related Workmentioning

confidence: 99%

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Robust methods and conditional expectations for vehicular traffic count analysis

Kilpi

Norros

Kuusela

et al. 2020

Eur. Transp. Res. Rev.

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We study the problem of making algorithmic statistical inferences about the dynamics of city traffic. Our data is based on loop detector counts of observed vehicles in various roads in the city of Tampere, Finland. We show that meaningful correlations can be found between traffic asymmetries at different measurement locations. The traffic asymmetry is the difference of the traffic counts of the opposite directions of a road. The correlations can be further quantified by estimating how much they effect on the average values of the traffic asymmetries at the neighbouring locations. Conditional expectations, both sample and binormal model-based versions are useful tools for quantifying this effect. The uncertainty bounds of conditional expectations of the binormal model distribution are extremely useful for outlier detection. Furthermore, conditional expectations of the multinormal distribution model can be used to recover missing data with bounds to uncertainty.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Robust methods and conditional expectations for vehicular traffic count analysis

Kilpi

Norros

Kuusela

et al. 2020

Eur. Transp. Res. Rev.

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show abstract

“…Arezou et al in [33] they have proposed a machine learning approach to predict the traffic flow both in offline and online mode. They have proposed two different algorithms; Multi-Layer Perception (MLP) that is trained using the yearly data for offline prediction and stochastic gradient descent which is employed in online forecasting.…”

Section: Smart Transporationmentioning

confidence: 99%

A Brief Survey on Smart Community and Smart Transportation

Azgomi

Jamshidi

2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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World population growing in conjunction with the preference to live in the cities; make the city management a challenging issue. Traditional Cities with their common features will not be able to handle the human needs. As a result, smart city and its beneficial outcomes will attract a lot of attention recently. In fact, Smart city/community that is the field of collecting and processing data from so many different areas while making proper decisions and feeding to all parts of the system will be the future style of the cities. In other words, smart community is a complex big data problem which should combine different fields of research to make a unit environment. This brief survey will deal with all aspects of the smart communities. There will also be explained different categories of the smart communities in addition to their future challenges. In this work we try to introduce some aspects of the smart transportation in more detail.

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