Driver Behavior on Combination of Vertical and Horizontal Curves of Mountainous Freeways

The study of driver behavior is of great importance to the traffic safety of mountainous freeways. In order to study the characteristics of driver behavior on combination of vertical and horizontal curves (CVHCs) of mountainous freeways in free flow conditions, designated speed measurement tests of two typical segments of the upgrade direction of Xi’an-Hanzhong freeway were carried out. After data processing, vehicles in free flow were screened out and classified into two groups by K-means clustering method, and then the driver behavior with different lanes, different size vehicles, and different CVHCs was analyzed, respectively. Finally, a vehicle distribution prediction model and a speed prediction model were built which were applied to CVHCs, and a verification test was made to test the accuracy of the models. Research results show that the driver behavior is mainly different among vehicle size, longitudinal slopes, and horizontal curves, and the characteristics of speed control and lane distribution on CVHCs vary according to lanes and combination of road alignment. Also, the prediction results of the models are highly consistent with the measured test results.

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Evaluation of the 2010 Highway Capacity Manual Urban Street Free-Flow Speed Prediction Model

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2461-01 ◽

2014 ◽

Vol 2461 (1) ◽

pp. 1-8

Author(s):

Ren Moses ◽

Enock T. Mtoi ◽

Eren E. Ozguven

Keyword(s):

Prediction Model ◽

Flow Speed ◽

Free Flow ◽

Highway Capacity ◽

Highway Capacity Manual ◽

Urban Street ◽

Free Flow Speed ◽

Speed Prediction

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OPERATING SPEED PREDICTION MODEL AS A TOOL FOR CONSISTENCY BASED GEOMETRIC DESIGN OF FOUR-LANE DIVIDED HIGHWAYS

Transport ◽

10.3846/transport.2019.10715 ◽

2019 ◽

Vol 34 (4) ◽

pp. 425-436 ◽

Cited By ~ 7

Author(s):

Gourab Sil ◽

Avijit Maji ◽

Suresh Nama ◽

Akhilesh Kumar Maurya

Keyword(s):

Prediction Model ◽

Lateral Position ◽

Geometric Design ◽

Operating Speed ◽

Vehicle Speed ◽

Horizontal Curve ◽

Passenger Cars ◽

Horizontal Curves ◽

Speed Prediction ◽

Design Consistency

Researchers have studied two-lane rural highways to predict the operating speed on horizontal curves and correlated it with safety. However, the driving characteristics of four-lane-divided highways are different. Weak lane discipline is observed in these facilities, which influences vehicle speed in adjacent lane or space. So, irrespective of its lane or lateral position, vehicles in four-lane divided highways are considered free flowing only when it maintains the minimum threshold headway from any lead vehicle. Examination of two conditions is proposed to ensure the free flow. Vehicles meeting both conditions, when tracked from the preceding tangent section till the centre of the horizontal curve, are considered as free flowing. The speed data of such free flowing passenger cars at the centre of eighteen horizontal curves on four-lane divided highways is analysed to develop a linear operating speed prediction model. The developed model depends on curve radius and preceding tangent length. The operating speed of passenger car in four-lane divided highways is influenced by horizontal curve of radius 360 m or less. Further, longer tangent would yield higher operating speed at the centre of the curve. Finally, two nomograms are suggested for conventional design, consistency based design and geometric design consistency evaluation of four-lane divided horizontal curves.

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Familiar versus Unfamiliar Drivers on Curves: Naturalistic Data Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119846481 ◽

2019 ◽

Vol 2673 (6) ◽

pp. 225-235 ◽

Cited By ~ 1

Author(s):

Michael P. Pratt ◽

Srinivas R. Geedipally ◽

Bahar Dadashova ◽

Lingtao Wu ◽

Mohammadali Shirazi

Keyword(s):

Human Factors ◽

Peripheral Vision ◽

Driver Behavior ◽

Reaction Times ◽

Horizontal Curves ◽

Speed Prediction ◽

Naturalistic Driving ◽

Design Consistency ◽

Roadway Design ◽

Rural Highway

Human factors studies have shown that route familiarity affects driver behavior in various ways. Specifically, when drivers become more familiar with a roadway, they pay less attention to signs, adopt higher speeds, cut curves more noticeably, and exhibit slower reaction times to stimuli in their peripheral vision. Numerous curve speed models have been developed for purposes such as predicting driver behavior, evaluating roadway design consistency, and setting curve advisory speeds. These models are typically calibrated using field data, which gives information about driver behavior in relation to speed and sometimes lane placement, but does not provide insights into the drivers themselves. The objective of this paper is to examine the differences between the speeds of familiar and unfamiliar drivers as they traverse curves. The authors identified four two-lane rural highway sections in the State of Indiana which include multiple horizontal curves, and queried the Second Strategic Highway Research Program (SHRP2) database to obtain roadway inventory and naturalistic driving data for traversals through these curves. The authors applied a curve speed prediction model from the literature to predict the speed at the curve midpoints and compared the predicted speeds with observed speeds. The results of the analysis confirm earlier findings that familiar drivers choose higher speeds through curves. The successful use of the SHRP2 database for this analysis of route familiarity shows that the database can facilitate similar efforts for a wider range of driver behavior and human factors issues.

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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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