Modeling Operating Speed Using Continuous Speed Profiles on Two-Lane Rural Highways in India

Abstract

The geometric elements of the road, such as tangents and curves, play a vital role in road safety because significant crashes are reported on the horizontal curves and tangent-to-curve transitions. Literature reveals that inconsistent geometric design of roads violates driver's expectation of operating speed leading to crashes. For safe manoeuver, it is necessary to achieve consistent operating speed with road geometry based on the driver's expectations rather than the designer's perception. Estimation of reliable operating speeds in the design phase will help to design safer road alignments. Several past studies developed operating speed models on the curves and tangent-to-curve transitions. However, these models used spot speed data with the assumption that the constant speed persists on the horizontal curves and entire deceleration/acceleration occurs on the approach/departure tangents. In this study, an instrumented vehicle with a high-end GPS (global positioning system) device was used to obtain the continuous speed profiles for passenger cars which resulted in reliable and robust speed prediction models for a tangent, curve, and tangent-to-curve. The study also establishes a relationship between the differential of the 85th percentile speed ðΔV 85 Þ and 85th percentile speed differential ðΔ 85 VÞ. The analysis results revealed that ΔV 85 underestimates Δ 85 V by 5.32 km=h, and Δ 85 V predicted the actual speed reduction from tangent-to-curve transitions. Statistical analysis results showed low errors, variations, and strong correlation of the proposed models with the field data. The models developed in the present study were validated and compared with various other models available in the literature. The comparative study highlights the importance of using continuous speed profile data to calibrate the operating speed models.

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

“…us, the predictions from field study are more accurate in comparison to those obtained from the driving simulator. is inference is in line with the conclusion made by Malaghan et al [6].…”

“…For this, the interaction between road infrastructure and human factors has been commonly studied for the evaluation of geometric design consistency. In this context, geometric design consistency can be defined as how likely the drivers' expectations are met with the geometric design features of the road [5,6]. A good consistent roadway optimizes surprising events and allows to maintain consistent operating speed meeting the drivers' expectations leading to safe maneuvers.…”

“…Note. R: radius, L c : curve length, D a : deflection angle, D c : degree of curve, TL: tangent length, NH: national highway, SH: state highway, and MDR: major district road 6. Journal of Advanced Transportation…”

Examining the feasibility of current spiral curve design controls for LiDAR‐based automated vehicles

“…It is worth noting that given the same N T and L S , we still listed each V max_isd curve by V d (corresponding to the ASD required for the inverse calculation of V max_isd ), which further confirms that most V d (40–100 km/h)‐matched V max_isd under the same R , L S , and N T condition does fluctuate in a small range. Also, an acceptable example of the maximum speed limit range at each V d was illustrated in Figure 11, where the speed limit against inadequate sight distance ( V L ) corresponding to a certain V d should not be less than ( V d − 20) km/h to ensure driving consistency [32, 80, 81]. It should be noted that V max_isd decreases with larger R or longer L T , which contradicts the case with the maximum speed against the disengagement of the semi‐AV system reported in García et al.…”

Modelos de velocidad de operación de carreteras rurales en terreno llano en Costa Rica

“…Además, puede inferirse que un gran número de accidentes está relacionado con las interrelaciones de las condiciones geométricas del trazado con el factor humano, lo que por sí solo justifica la importancia del estudio. Varios investigadores coinciden en que la velocidad de operación se define como el percentil 85 de la distribución de velocidades a la que operan los vehículos ligeros en condiciones de flujo libre (Choudhari & Maji, 2019;García-Ramírez & Damián Alverca, 2019;Llopis-Castelló et al, 2019;Malaghan et al, 2020;Saleem & Persaud, 2017;Sil et al, 2018). El percentil 85 es el más empleado porque representa aproximadamente la velocidad a la que operan los conductores y que se considera segura.…”