Speed behaviour upon approaching freeway curves

Vos, J. W. E.; Farah, Haneen; Hagenzieker, Marjan

doi:10.1016/j.aap.2021.106276

Cited by 15 publications

(8 citation statements)

References 37 publications

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“…It was the aim of this study to include only variables which are easily extracted from a geometric design, namely horizontal radius, start and end position of curves, and the number of lanes. Other, more complex variables (Vos et al, 2021), might improve the models further, but are harder to implement in design evaluations. Furthermore, variables such as type of roadway, superelevation, transition curves, Curvature Change Rate or weather conditions don't have significant impact on speed behaviour or are collinear to horizontal radius (Vos et al, 2021).…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Other, more complex variables (Vos et al, 2021), might improve the models further, but are harder to implement in design evaluations. Furthermore, variables such as type of roadway, superelevation, transition curves, Curvature Change Rate or weather conditions don't have significant impact on speed behaviour or are collinear to horizontal radius (Vos et al, 2021).…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…In these individual speed profiles, breakpoints (BPs) were identified. These BPs are the positions surrounding curve start and curve end where drivers deviate from a constant speed (Montella et al, 2015;Vos et al, 2021). These BPs are defined based on the position where drivers first deviate from 0 m/s 2 upstream of curve start (BP1), first reset to 0 m/s 2 downstream of curve start (BP2), first deviate from 0 m/s 2 upstream of a curve end (BP3), and first reset to 0 m/s 2 downstream of a curve end (BP4).…”

Section: High Frequency Floating Car Datamentioning

confidence: 99%

“…It does not generate a model to position the deceleration related to the position of the curve, and it assumes a constant deceleration. Several studies have shown that vmin is not reached at curve start, otherwise known as the Point of Curvature (PC), or at the Midpoint of the Curve (MC) (Bella, 2014;Malaghan et al, 2021;Vos, Farah, & Hagenzieker, 2021). So, there seems to be no geometrically fixed point defining vmin.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussion and Limitationsmentioning

confidence: 99%

Section: Discussion and Limitationsmentioning

confidence: 99%

Section: High Frequency Floating Car Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…DOI: https://doi.org/10.19053/01211129.v30.n57.2021.13410 past the midpoint of the curve [17]. There is high variability in speed behavior at the curve's start, and deceleration stops about 135 meters after entering the curve [18]. Also, earlier deceleration before entering the curve turns into a higher safety maneuver [19].…”

Section: Introductionmentioning

confidence: 99%

Clasificación de comportamiento de conductores en curvas horizontales de carreteras rurales de dos carriles

2021

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Los componentes de una vía infieren directamente en su seguridad vial y, a partir de un análisis de la consistencia en el diseño geométrico se puede cualificar esta condición. La velocidad del vehículo es un factor considerado en la evaluación de la consistencia de diseño geométrico y se considera constante a lo largo de la transición del conductor entre las tangentes de las curvas horizontales. Sin embargo, los conductores en la mayoría de los casos adoptan una velocidad diferente dependiendo de la posición del vehículo antes, durante y después de la curva, por lo cual, un perfil de velocidad permite describir mejor este comportamiento y, estudiar la relación entre la velocidad y los elementos de la curva. La literatura previa muestra que el entorno rural influye en la velocidad de los usuarios de la carretera. Asimismo, las negociaciones en las curvas horizontales están vinculadas a un elevado número de accidentes, la mayoría de los cuales están causados por errores del conductor y un estilo de conducción agresivo/arriesgado. En esta investigación se estudia el número óptimo de comportamientos de los conductores y el proceso de segregación de estos para una base de datos de perfiles de velocidad registrados en campo. Para esto se propone un esquema basado en conglomerados, partiendo de la selección de un numero óptimo de conglomerados aceptable para las características de los datos y posteriormente, utilizando la técnica k-means para agrupar los perfiles semejantes. Se emplea un grupo de datos de velocidad a flujo libre utilizado en 38 curvas horizontales en vías rurales en Puerto Rico que fueron instrumentadas en ambas direcciones. La validación de la metodología propuesta se realiza mediante la comparación directa de la cantidad de comportamientos observados en los usuarios con las características geométricas y operacionales de las curvas. Los resultados muestran que al emplear 3 tipos estilos de conducción referidos como agresivo, moderado y cauto y, agregar dos variables inferidas que consideran el tiempo que el conductor permanece cerca o por encima del límite de velocidad y los cambios en la aceleración durante su desplazamiento por la curva permiten una categorización más clara y uniforme de los perfiles de velocidad. Adicionalmente, se observa que la cantidad de perfiles agrupados presentan una alta dependencia con los valores que toman las variables del diseño de la curva como es de esperarse debido a su influencia en la percepción de la seguridad por parte de los conductores.

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