Successive-Stage Speed Limit on Exit Ramp Upstream of Direct-Type Freeway in China

The first objective of this study is to analyze a successive-stage speed limit model developed for vehicles along the exit upstream ramp of direct-type freeway in China. This paper (1) explains the necessity to implement speed limit to the exit ramp upstream, (2) analyzes whether speed limit is related to the length of the deceleration lane, vehicle type, saturation, and turning ratio and (3) proposes a speed prediction model and calibrates speed-limit sign validity model and establishes successive-stage speed limit model.The results.Δν85≥10illustrates the necessity of the using speed limit on the exit ramp. Speed-deceleration lane length curve presents two trends bounded by 200 m, so the speed limit should be in accordance with the deceleration length. Speed-small vehicle curve closing to speed-large vehicle curve presents that the vehicle type is not the factor of the speed limit. After curve fitting and polynomial regression, saturation is considered to be the most influential factor of speed. Speed-saturation prediction model and calibrated speed-limit sign validity model are built through linearization. According to the above results, successive-stage speed limit model is established. An exit ramp was implemented to verify the feasibility and validity of the model.

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A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework

Transportmetrica B Transport Dynamics ◽

10.1080/21680566.2015.1060582 ◽

2015 ◽

Vol 4 (3) ◽

pp. 159-186 ◽

Cited By ~ 20

Author(s):

Haizhong Wang ◽

Lu Liu ◽

Shangjia Dong ◽

Zhen Qian ◽

Heng Wei

Keyword(s):

Prediction Model ◽

Work Zone ◽

Short Term ◽

Vehicle Type ◽

Speed Prediction ◽

Traffic Speed

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Vehicle Operating Speed Prediction Model on Freeway Exit Ramp

ICLEM 2012 ◽

10.1061/9780784412602.0064 ◽

2012 ◽

Author(s):

Liguo Xia ◽

Jian Lu ◽

Yongfeng Ma

Keyword(s):

Prediction Model ◽

Operating Speed ◽

Speed Prediction ◽

Exit Ramp

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Speed Prediction Model of Trucks on Gradient Sections Considering Vertical Curve

CICTP 2020 ◽

10.1061/9780784483053.138 ◽

2020 ◽

Author(s):

Qianqian Liang ◽

Xiaodong Zhang ◽

Jinliang Xu ◽

Yang Zhang

Keyword(s):

Prediction Model ◽

Speed Prediction

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A Short-term Traffic Speed Prediction Model Based on LSTM Networks

International Journal of Intelligent Transportation Systems Research ◽

10.1007/s13177-021-00260-7 ◽

2021 ◽

Author(s):

Yu-Ling Hsueh ◽

Yu-Ren Yang

Keyword(s):

Prediction Model ◽

Short Term ◽

Model Based ◽

Speed Prediction ◽

Traffic Speed

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An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

Wind Engineering ◽

10.1177/0309524x19849843 ◽

2019 ◽

Vol 44 (3) ◽

pp. 266-281 ◽

Cited By ~ 1

Author(s):

Zhongda Tian ◽

Yi Ren ◽

Gang Wang

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Prediction Model ◽

Least Squares ◽

Support Vector ◽

Short Term ◽

Backtracking Search ◽

Wind Speed Prediction ◽

Search Optimization ◽

Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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