Optimization of Traffic Signals Using Deep Learning Neural Networks

Lawe, Saman; Wang, Ruili

doi:10.1007/978-3-319-50127-7_35

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Using a measure of centrality is the easiest and quickest approach to imputing missing values [43]. Such measures represent the most common value in a variable's distribution and are therefore a logical option for this method.…”

Section: Methods and Implementationmentioning

confidence: 99%

Adaptive traffic lights based on traffic flow prediction using machine learning models

Moumen,

Abouchabaka,

Rafalia

2023

IJECE

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<span lang="EN-US">Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.</span>

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Section: Methods and Implementationmentioning

confidence: 99%

Adaptive traffic lights based on traffic flow prediction using machine learning models

Moumen,

Abouchabaka,

Rafalia

2023

IJECE

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“…The cameras allowed for accurate data collection at all junctions. However, for the missing data points, the authors used the historical average data value substituting where missing value and zero data were presented in the dataset [24,30]. The typical volumes along with 12 h volumes range between 10,545 and 23,900 vehicles.…”

Section: Methodology and Data Source 21 Dataset Description And Prepa...mentioning

confidence: 99%

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Katambire,

Musabe,

Uwitonze

et al. 2023

Forecasting

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Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.

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“…However, AI is often not transparent (a.k.a. 'black box'), stochastic, and therefore hard to rationalize (Lawe and Wang 2016). Terzidis' team constructed a system that uses permutations to optimize traffic lights' timing at intersections with AI prediction.…”

Section: Flexible Traffic Lights: Traffic Light Management Using Aimentioning

confidence: 99%

Virtual World16 - Virtual Design Collaboration for the Intersection of Academia and Industry

Schnabel¹,

Kobayashi²,

Pencreach³

et al. 2021

CAADRIA Proceedings

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Optimization of Traffic Signals Using Deep Learning Neural Networks

Cited by 10 publications

References 15 publications

Adaptive traffic lights based on traffic flow prediction using machine learning models

Adaptive traffic lights based on traffic flow prediction using machine learning models

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Virtual World16 - Virtual Design Collaboration for the Intersection of Academia and Industry

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