Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Zahid, Muhammad; Chen, Yangzhou; Jamal, Arshad; Memon, Muhammad Qasim

doi:10.3390/s20030685

Cited by 42 publications

(24 citation statements)

References 59 publications

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“…Most of the existing traffic control schemes deploy fixed time program are based on historical traffic information without considering real-time traffic information [49]. Accurate short-term traffic sate prediction have been reported to have numerous applications for intelligent traffic control [50,51]. Numerous research studies have focused on minimizing delay through signalized intersections by rationally optimizing the cycle length.…”

Section: Previous Studiesmentioning

confidence: 99%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

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Section: Previous Studiesmentioning

confidence: 99%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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“…The optimization of parameters using the multiobjective optimization problem (MOP) swarm algorithm 53 designed to increase the efficiency of the DBN model. In Zahid et al, 54 optimization of hyperparameters in the various machine‐learning models was proposed to overcome the problem of overfitting 9 . The performance of LSTM in arterial road traffic prediction explained in Mackenzie et al 55 A multitask learning DNN 56 used stacked autoencoder for traffic flow prediction.…”

Section: Related Workmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

100

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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“…It is established that strict enforcement could discourage the drivers from committing traffic violations, and significantly reduce the number of crashes, as well as their severity [ 16 ]. Similarly, studies have shown that pro-active traffic control and forecasting could be very beneficial to monitor dynamic drivers maneuvers, thus ensuring strict compliance traffic regulations and mitigate congestion in urban areas [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

Zahid

Chen

Jamal

et al. 2020

IJERPH

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Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.

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Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Cited by 42 publications

References 59 publications

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

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