Speed prediction models for heavy passenger vehicles on rural highways based on an instrumented vehicle study

Malaghan, Vinayak; Pawar, Digvijay S.; Dia, Hussein

doi:10.1080/19427867.2020.1811005

Cited by 19 publications

(5 citation statements)

References 18 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%

See 1 more Smart Citation

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

Malaghan

Pawar

Dia

2021

Journal of Advanced Transportation

Self Cite

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%

“…ere are various measures available to evaluate geometric design consistency: operating speed, side friction, vehicle dynamics, alignment indices, and mental workload [7,8]. Among these, operating speed measure is commonly used to develop speed profile models for the design consistency evaluation.…”

Section: Introductionmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…This kind of method can predict the speed and direction of the vehicle but can be more influenced by factors such as weather and lighting intensity. Vehicle-dynamics-based methods [29][30][31][32] use vehicle dynamics to build a prediction model to predict the future speeds of vehicles, in which a large number of factors such as vehicle dynamics parameters and road conditions are required to be considered. Machine learning methods [33][34][35] are one of the most commonly used techniques for vehicle speed prediction.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data

Tian

Zheng

et al. 2023

BDCC

View full text Add to dashboard Cite

At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks.

show abstract

“…[21]. Although several models have been developed to evaluate the design consistency of roadways, most are based on the operating speed of passenger cars, and few have included heavy vehicles [22,23]. Additionally, studies on specific vehicle classes, such as light, medium, and heavy vehicles, are limited.…”

Section: Introductionmentioning

confidence: 99%

Speed Behavior of Heterogeneous Traffic on Two-Lane Rural Roads in Malaysia

Faiz¹,

Mashros²,

Hassan³

2022

Sustainability

View full text Add to dashboard Cite

Highway geometry is a significant factor that affects the efficiency and safety of highway systems. The present study aims to investigate the speed behavior of various vehicle classes on the horizontal alignment of two-lane rural roads. An automatic data collection system based on a pressure sensor was employed to collect the speed of each individual vehicle, vehicle type, and headway at seven sites in each travel direction. The 85th percentile speed under free-flow conditions was used to observe the relationship between the operating speeds of various vehicle classes at consecutive curve points and the effect of the travel direction, time of day, and curve radius on the operating speed of the vehicle. A one-way ANOVA was employed to evaluate whether there is a significant difference in speed on horizontal curves. Then, a Tukey post hoc test was used to assess the significance of the difference in speed across four classes of vehicles. The results revealed that the horizontal curve affects the operating speed for all vehicle classes. A curve radius of less than 500 m, the travel direction, and the time of day are significant variables that affect the speed of all vehicle classes. The findings from this study can provide insight to transportation engineers for safer road design of horizontal curves and to assess traffic safety based on actual speed behavior.

show abstract

Speed prediction models for heavy passenger vehicles on rural highways based on an instrumented vehicle study

Cited by 19 publications

References 18 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

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data

Speed Behavior of Heterogeneous Traffic on Two-Lane Rural Roads in Malaysia

Contact Info

Product

Resources

About