85th and 98th Percentile Speed Prediction Models of Car, Light, and Heavy Commercial Vehicles for Four-Lane Divided Rural Highways

Maji, Avijit; Sil, Gourab; Tyagi, Ayush

doi:10.1061/jtepbs.0000136

Cited by 41 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A minimum time headway between 4 and 6 s was recommended to avoid vehicular interaction to ensure the influence of road geometry on operating speed [8,27,47]. Hence, a time headway ≥5 s was considered for the speed profiles in the analysis.…”

Section: Data Extraction and Reductionmentioning

confidence: 99%

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Malaghan

Pawar

Dia

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

Several past studies developed acceleration/deceleration rate models as a function of a single explanatory variable. Most of them were spot speed studies with speeds measured at specific locations on curves (usually midpoint of the curve) and tangents to determine acceleration and deceleration rates. Fewer studies adopted an estimated value of 0.85 m/s2 for both deceleration and acceleration rates while approaching and departing curves, respectively. In this study, instrumented vehicles with a high-end GPS (global positioning system) device were used to collect the continuous speed profile data for two-lane rural highways. The speed profiles were used to locate the speeds at the beginning and end of deceleration/acceleration on the successive road geometric elements to calculate the deceleration/acceleration rate. The influence of different geometric design variables on the acceleration/deceleration rate was analysed to develop regression models. This study also inspeced the assumption of constant operating speed on the horizontal curve. The study results indicated that mean operating speeds measured at the point of curvature (PC) or point of tangency (PT), the midpoint of curve (MC), and the end of deceleration in curve were statistically different. Acceleration/deceleration rates as a function of different geometric variables improved the accuracy of models. This was evident from model validation and comparison with existing models in the literature. The results of this study highlight the significance of using continuous speed profile data to locate the beginning and end of deceleration/acceleration and considering different geometric variables to calibrate acceleration/deceleration rate models.

show abstract

Section: Data Extraction and Reductionmentioning

confidence: 99%

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Malaghan

Pawar

Dia

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…[7][8][9][10]), and for multilane roads (e.g. [11][12][13][14][15][16][17]). Through a review of literature, it can be concluded that the studies concerned with modeling the relationship between operating speed and elements of straight sections (tangents) and analyzing their properties are less than studies of speeds on curved sections.…”

Section: Introductionmentioning

confidence: 99%

Operating Speed Prediction Models for Tangent Segments: A Brief Review

Abuaddous

2021

Civ Eng J

View full text Add to dashboard Cite

This paper provides a review of studies aimed at developing operating speed prediction models for road tangent sections. The review included many studies, conducted in different geographical areas of the world, in terms of road classification, types of vehicles, techniques and devices used in data collection, number of study sites, the principle adopted in extracting the free-flow speed, as well as the topography that the road path passes through and grads of the studied sections. Moreover, this review mentioned the analysis methods adopted in the modeling, and included the model formulas that the researchers have reached in their studies, as it showed all the geometric elements and traffic characteristics that appeared in the models as independent variables. The author has avoided critiquing or evaluating the methodologies of the reviewed research and accordingly this paper has been prepared for documentation only. The author aims primarily to save the effort and time of graduate students and researchers interested in modeling the operating speed on straight segments, as all data and information are arranged in tables and coordinated for this purpose. Doi: 10.28991/cej-2021-03091784 Full Text: PDF

show abstract

“…Krammes et al established the operation speed model by collecting the operating speed and design data of 138 plane curve sections based on 1,126 curve sections as samples for a preliminary evaluation to determine the causes of accidents in curve sections and select improvement methods [26]. Collins et al evaluated the freeflow condition by measuring the actual driving speed at the midpoint of the curve [27][28][29][30][31][32][33]. Sil and Maji measured the operating speed through the curve on three sections and established a model between the speed at the curve and the combined curves on the four-lane highway [34].…”

Section: Introductionmentioning

confidence: 99%

Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types

Wang

Chen

Cheng

et al. 2020

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers’ sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.

show abstract

85th and 98th Percentile Speed Prediction Models of Car, Light, and Heavy Commercial Vehicles for Four-Lane Divided Rural Highways

Cited by 41 publications

References 22 publications

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Operating Speed Prediction Models for Tangent Segments: A Brief Review

Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types

Contact Info

Product

Resources

About