Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

Mirbaha, Babak; Saffarzadeh, Mahmoud; Beheshty, Seyed AmirHossein; Aniran, MirMoosa; Yazdani, Mirbahador; Shirini, Bahram

doi:10.1088/1757-899x/245/4/042024

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…In the study [14], the R 2 value was obtained as 0.88 in the results produced in speed estimation. In the study [15], a 95% success was achieved in the results produced. In the study [16], the speed estimation results were produced by optimizing the system in a way that they remained within the limits of the ISO 14064-2 standard and by using machine learning methods.…”

Section: Resultsmentioning

confidence: 84%

“…The article [14] investigated vehicle speed estimation models in the road design depending on the energy demand. The study [15] examined vehicle average speed estimation in a two-lane highway, depending on the weather and traffic characteristics. The study [16] addressed the subject of travel speed estimation by using machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

Akçay

Akgündoğdu

Tiryaki

2021

Demiryolu Mühendisliği

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Section: Resultsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

Akçay

Akgündoğdu

Tiryaki

2021

Demiryolu Mühendisliği

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“…One study that estimated traffic speed reduction in rain, under various traffic volumes, found that light and heavy rainfall conditions can reduce traffic speed by 2% to 4% and 6% to 9%, respectively ( 17 ). A snowfall may affect traffic speed depending on its intensity ( 18 ). Light snow reduces traffic speed by 0.9 mph whereas heavy snow may result in a 23 to 36-mph reduction ( 19 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

Shabarek

Chien

Hadri³

2020

Transportation Research Record: Journal of the Transportation R

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The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

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“…In the article [14], vehicle speed estimation models are investigated within the road design depending on the energy demand. [15], the average speed estimation of the vehicle was investigated on Constant Speed Time a two-lane highway depending on weather and traffic characteristics. In [16], the subject of cruise speed estimation was examined using machine learning methods.…”

Section: Figure 2 Graphic Of Vehicles Driving Modesmentioning

confidence: 99%

Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Akçay¹

2020

Sakarya University Journal of Computer and Information Sciences

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While the investments in rail transportation systems continue without slowing down, various optimization issues come to the fore in order for the systems to work more efficiently. One of the most important of these issues is the optimization of the vehicle speed profile. Improvement in vehicle speed profile increases efficiency in operating traffic. Vehicle speed profile varies depending on the electrical-characteristic features of the vehicle, the distance between the stations and the line geometry. The vehicle's speed profile consists of several parts, such as acceleration, constant speed travel and braking zones. The constant speed in the constant velocity zone refers to the max operating speed, which is recommended for operation in the restricted area and remains within the limits. This part is critical in creating the speed profile of the vehicle. In this study, the estimation of the value of the constant speed time in the speed profile of the vehicles used in the city metro systems was made by using the Stochastic Gradient Descent method, which is one of the machine learning methods, and compared with various well-known methods. Coefficient of determination (R 2 ) values were calculated as 0.9955 and 0.9951, respectively, with random sampling hold out and cross validation methods.

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Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

Cited by 13 publications

References 12 publications

Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

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