Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression

Priambodo, Bagus; Ahmad, Azlina

doi:10.1007/978-3-319-70010-6_29

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Research on relationships between roads [18], defined congestion correlation from road segment A to segment B with a certain distance d as: If congestion occurs in road segment A at time t and at time t + T, congestion occurs at road segment B. Another research applied data from sensors using correlation method based on mean of average speed at interval time to find relationships between roads segments [6] [19]. Other studies used connected road (upstream and downstream) using neural network [5][8], and k-NN [15].…”

Section: Related Workmentioning

confidence: 99%

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Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

2020

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For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments. Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.

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Section: Related Workmentioning

confidence: 99%

“…Previously we have conducted experiments to find correlation value between neighbouring roads using correlation method. However, correlation method allows the use of one variable, either average speed [19] or vehicle count. To deal with two or more variables, clustering is needed.…”

Section: Problemmentioning

confidence: 99%

Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

2020

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“…Previously, we have conducted a prediction of traffic flow in normal condition based on all day's data using multiple regression method [13]. Other research show there is a difference in congestion pattern between weekdays and weekend [14]and between regular and irregular days [15].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Priambodo

Jumaryadi

2018

MATEC Web Conf.

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During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

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“…Using the Hidden Markov Model algorithm, they collected 1000 American Sign Language (ASL) phrases for the systems (Zafrulla, Brashear, Starner, Hamilton, & Presti, 2011). There is also research which used multiple regression (Priambodo & Ahmad, 2017). Adriansyah analyzed the goalseeking behaviors based on Particle Swarm Fuzzy Controller (2015), Fitrianah et al, explored feature extraction in prediction (2015), and Aswari and Diana (2016) used Bezier Curve Method.…”

Section: Introductionmentioning

confidence: 99%

Designing Translation Tool: Between Sign Language to Spoken Text on Kinect Time Series Data Using Dynamic Time Warping

Putera

Anasanti

Priambodo

2018

Sinergi

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Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression

Cited by 9 publications

References 15 publications

Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Designing Translation Tool: Between Sign Language to Spoken Text on Kinect Time Series Data Using Dynamic Time Warping

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