Acceleration and Deceleration Behavior in Departing and Approaching Sections of Curve Using Naturalistic Driving Data

Nama, Suresh; Sil, Gourab; Maurya, Akhilesh Kumar; Maji, Avijit

doi:10.1007/978-981-15-3742-4_44

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…These curve warnings provide perceptual cues that can be processed "bottom-up". Nama et al (2020) found that curve radius and the length of the preceding tangent are the most influential factors affecting driver behaviour while approaching a curve. Finally, Lehtonen et al (2012) observed that drivers anticipate open curves by switching their visual attention between the road and the occlusion point.…”

Section: Literature Reviewmentioning

confidence: 99%

'I Did Not See that Coming': A Latent Variable Structural Equation Model for Understanding the Effect of Road Predictability on Crashes Along Horizontal Curves

Afghari

Vos

Farah

et al. 2022

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

'I Did Not See that Coming': A Latent Variable Structural Equation Model for Understanding the Effect of Road Predictability on Crashes Along Horizontal Curves

Afghari

Vos

Farah

et al. 2022

Preprint

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“…A naturalistic driving study by Nama et al [39] fitted a GPS device in passenger cars for the speed data collection on four-lane divided highways in mountainous terrain. e operating speed data were divided at every 4 m interval from tangent-to-curve and curve-to-tangent to draw an average position speed (APS) profile and acceleration/deceleration profile obtained from APS.…”

Section: Deceleration/acceleration Rate Models Using Vehiclesmentioning

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

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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.

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“…In the last decade several studies have generated speed profiles using driving simulators (Bella, 2014;Montella, Galante, Imbriani, Mauriello, & Pernetti, 2014;Montella, Galante, Mauriello, & Aria, 2015;Wang, Guo, & Tarko, 2020), usually to research specific elements of the road (Bobermin, Silva, & Ferreira, 2021). Other studies used instrumented vehicles to analyse speed profiles (Altamira, García Ramírez, Echaveguren, & Marcet, 2014;Cafiso & Cerni, 2012;Cafiso & La Cava, 2009;Echaveguren, Henríquez, & Jiménez-Ramos, 2020;Hashim, Abdel-Wahed, & Moustafa, 2016;Malaghan, Pawar, & Dia, 2020Montella, Pariota, Galante, Imbriani, & Mauriello, 2014;Nama, Sil, Maurya, & Maji, 2020). These methods usually have low sample sizes of observed curves or participants and suffer from participant bias.…”

Section: Introductionmentioning

confidence: 99%

Speed development at freeway curves based on high frequency floating car data

Vos

Farah

2022

European Journal of Transport and Infrastructure Research

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Road designers need to have insights where deceleration and acceleration are expected related to the position of the curve, and in in which amount so that drivers are able to safely decelerate and accelerate respectively into and out of a freeway curve. For this, empirical speed data is needed. Therefore, Floating Car Data in 153 curves in The Netherlands were collected at a resolution of 1 Hz and were filtered on free-flow periods, to analyse over 800 thousand unique continuous free-flow speed observations on these curves. Regression models were developed to predict speed development, including deceleration and acceleration behaviour upon entering and exiting freeway curves. The models rely on easy to generate geometric design variables, including the start and end position of the horizontal curve, the horizontal radius and the number of lanes. Using these variables, the designer can predict the speed development based on the 85th percentile of speed and acceleration, relative to the position of the curve. The regression models reveal strong goodness-of-fit of the predicted 85th percentiles of speed in a curve, showing acceleration and deceleration inside the curve, and higher predicted 85th percentile speeds than the design speeds. The models also show satisfying results in speed development prediction in sets of consecutive curves with different characteristics, as well as deceleration when entering a first curve and acceleration when exiting a last curve. These insights are valuable in evaluating road design in relation to traffic safety based on its predicted use.

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Acceleration and Deceleration Behavior in Departing and Approaching Sections of Curve Using Naturalistic Driving Data

Cited by 4 publications

References 12 publications

'I Did Not See that Coming': A Latent Variable Structural Equation Model for Understanding the Effect of Road Predictability on Crashes Along Horizontal Curves

'I Did Not See that Coming': A Latent Variable Structural Equation Model for Understanding the Effect of Road Predictability on Crashes Along Horizontal Curves

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

Speed development at freeway curves based on high frequency floating car data

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