Analyzing Speed Disparity in Mixed Vehicle Technologies on Horizontal Curves

Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles (σc) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types.

Guidance-oriented advanced curve speed warning system in a connected vehicle environment

The most frequently used criteria to evaluate design consistency are the difference between design speed and operating speed, operating speeds on successive elements, and assumed side friction of the road and that demanded by the driver. The operating speed, often obtained using speed prediction models, influences each design consistency criteria. There has been considerable research in developing design consistency measures and evaluation tools, but little research has been done on the influence that different speed prediction models have on design consistency criteria. In addition, there has been little research as to how design factors such as driver perception of combined horizontal and vertical curves influence speed prediction models. This paper investigates how the selection of speed prediction models and driver perception of combined horizontal and vertical curves influence design consistency evaluation. The selection of the speed prediction model has a significant impact on design consistency evaluation. The use of perceived radius, which accounts for driver perception of combined alignments, slightly influenced the outcome of the design consistency evaluation.Key words: design consistency, speed prediction models, driver perception, combined horizontal and vertical curves, perceived radius, geometric design.

The development of operating speed models has been the subject of numerous research studies in the past. Most of them present models that aim to predict free-flow speed in conjunction with the road geometry at the curved road sections considering various geometric parameters e.g., radius, length, preceding tangent, deflection angle. The developed models seldomly take into account the operating speed profiles of motorcycle riders and hence no significant efforts have been put so far to associate the geometric characteristics of a road segment with the speed behavior of motorcycle riders. The dominance of 4-wheel vehicles on the road network led the researchers to focus explicitly on the development of speed prediction models for passenger cars, vans, pickups, and trucks. However, although the motorcycle fleet represents only a small proportion of the total traffic volume motorcycle riders are over-represented in traffic accidents especially those that occur on horizontal curves. Since operating speed has been thoroughly documented as the most significant precipitating factor of vehicular accidents, the study of motorcycle rider's speed behavior approaching horizontal curves is of paramount importance. The subject of the present paper is the development of speed prediction models for motorcycle riders traveling on two-lane rural roads. The model was the result of the execution of field measurements under naturalistic conditions with the use of an instrumented motorcycle conducted by experienced motorcycle riders under different lighting conditions. The implemented methodology to determine the most efficient model evaluates a series of road geometry parameters through a comprehensive literature review excluding those with an insignificant impact to the magnitude of the operating speeds in order to establish simple and handy models.

Horizontal curves are designed using a design speed, which is the speed used to determine the geometric features of highways. In sharp curves, drivers sometimes exceed the design speed. Depending of the difference between design speed and operating speed, drivers can face risky situations, which can be mitigated using signs informing drivers of advisory speeds. Advisory speeds at the horizontal curves are normally estimated as a function of the lateral acceleration for a maximum driving speed, at which comfort is low. This paper proposes an analytical model to estimate advisory speeds based on ball-bank readings and consistency concepts. Field data were obtained using a digital ball-bank indicator, an accelerometer, and a GPS logger. Readings were obtained on 24 test sections at speeds between 40 and 110 km/h. The model proposed yields an advisory speed value and a limit ball-bank angle that require consistent behavior of drivers.

This article demonstrates several approaches to the vehicle energy consumption and tailpipe emission reduction opportunities. The article leverages the vehicle storage dynamics through smart and personalized optimization and control approaches in the context of connected vehicles. Recent advances in vehicle connectivity and automation have brought unprecedented information richness and new degrees of freedom that can be synergized with insightful understanding of vehicle powertrain and aftertreatment physical systems. Vehicle automation also provides new degrees of freedom that can be further leveraged by the vehicle control systems to improve vehicle energy efficiency and reduce tailpipe emissions. While vehicle automation levels probably will keep increasing, humans will still be involved in vehicle operations at various levels for the foreseeable future. The prediction of future vehicle’s power demand based on vehicle connectivity can significantly benefit tailpipe emission reductions and fuel economy.

In this research we utilize PARAMICS traffic micro-simulation software to study the impact of deploying Connected Vehicles (CV) in Deerfoot trail, Calgary, Alberta. We have implemented a V2V (Vehicle-to-Vehicle) Assisted V2I (Vehicle-to-Infrastructure) system for PARAMICS. It uses Dedicated Short Range Communication (DSRC) protocol to acquire traffic data, calculate and compare important traffic safety and mobility parameters and their impacts on CV by testing five scenarios differentiated by the percentage of 0% to 40% market penetration of CVs. Despite of previous studies which focused on upstream traffic, in this study we demonstrate effect of considering DSRC, re-routing guidance and advisory speed for upstream and downstream traffic. The study demonstrated that the CV technology can enhance traffic safety and mobility in freeways, if the percentage of CVs is significant (e.g. 30–40%) and the CV technology is accompanied by advisory speed reflected on Variable Message Signs (VMS) on both upstream and downstream of the incident location using DSRC range. In other words, equipping freeways with VMS, to use V2I communication, complements the CV technology, improves CV efficiency and leads to higher safety and mobility enhancement in freeways.

The evolution of scientific advances has often been characterized by the amalgamation of two or more technologies. With respect to vehicle connectivity and automation, recent literature suggests that these two emerging transportation technologies can and will jointly and profoundly shape the future of transportation. However, it is not certain how the individual and synergistic benefits to be earned from these technologies is related to their prevailing levels of development. As such, it may be considered useful to revisit the primary concepts of automation and connectivity, and to identify any current and expected future synergies between them. Doing this can help generate knowledge that could be used to justify investments related to transportation systems connectivity and automation. In this discussion paper, we attempt to address some of these issues. The paper first reviews the technological concepts of systems automation and systems connectivity, and how they prospectively, from an individual and collective perspective, impact road transportation efficiency and safety. The paper also discusses the separate and common benefits of connectivity and automation, and their possible holistic effects in terms of these benefits where they overlap. The paper suggests that at the current time, the sibling relationship seems to be lopsided: vehicle connectivity has immense potential to enhance vehicle automation. Automation, on the other hand, may not significantly promote vehicle connectivity directly, at least not in the short term but possibly in the long term. The paper argues that future trends regarding market adoption of these two technologies and their relative pace of advancement or regulation, will shape the future synergies between them.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

To improve highway design consistency, several studies developed operating speed prediction models and investigated drivers' speed behavior. Most existing models are based on spot speed data that assume constant operating speed throughout the horizontal curves and occurrence of acceleration and deceleration only on tangents. To overcome limitations associated with existing models, this study investigated continuous speed profiles with an experiment that used a high-fidelity dynamic-driving simulator on a two-lane highway. A piecewise linear regression model and locally weighted regression scatter-plot smoothing were used to remove noise in the data set while preserving underlying patterns and to identify significant changes in the speed profile. Based on the smoothed speed profiles, models to predict operating speed in curves and in tangents, deceleration and acceleration rates to be used in the operating speed profiles, and starting and ending points of constant operating speed in curve were developed. Radius of the curve notably affected not only the operating speed in the curve but also the operating speed of the tangent following the curve: the smaller the radius, the lower the operating speed of the exit tangent. Both acceleration and deceleration rates increased with curvature. This study found that operating speed was not constant along curves. On small radius curves, deceleration ended close to the center of the curve, and acceleration starts, close to the end of the curve. Increasing the curve radius, the end point of deceleration moves toward the curve's beginning, whereas the start of acceleration moves toward the center of the curve.

SINCE the U.S. Department of Transportation issued a mandate for car makers to enable connected vehicle (CV) technology for a safe driving experience in 2012, tremendous interest in CVs has reemerged. CVs refer to the enabling technologies to connect vehicles inside and outside their operational environments to improve safe driving, congestion control, air pollution reduction, energy savings, etc. Although research activities on CVs have attracted intensive attention under the umbrella of vehicle ad hoc networks and intelligent transportation systems (ITSs), most research outcomes are still limited to theory and are far from practicality. With the mandate, together with various kinds of sensing and learning capabilities, CVs can potentially form reliable and sustainable intelligence harvesting networks to collect various kinds of information related to driving conditions and road traffic situations, conduct effective data analysis, and then take proactive actions for a safe driving experience. Hence, CVs hold high promise for future ITSs that impact people's lives.

A vehicle negotiating a curve experiences a centripetal force that is partially supplied by the tire–pavement friction. The American Association of State Highway and Transportation Officials’ design handbook, A Policy on Geometric Design of Highways and Streets (“Green Book”), dictates the design of horizontal curve segments according to the side friction (demand) factor, which is a fraction of the available friction used during a maneuver. However, the Green Book casts the side friction factor not as a measure of tire–pavement friction but as a measure of lateral acceleration. In this way, the Green Book conflates these independent concepts (tire–pavement friction versus lateral acceleration). It is shown in this work that keeping these curve design parameters independent has meaningful benefits. A rigorous mathematical clarification of the differences among the side friction (demand) factor, lateral acceleration, and coefficient of friction is developed. It is shown that changing the coefficient of friction affects the side friction factor, but the opposite is not necessarily true. An alternate metric is proposed, the performance margin [Formula: see text], that has several advantages for assessing friction margins. Currently, horizontal curves are designed only with a lateral friction reserve. Excluding longitudinal dynamics may not be realistic given the manner in which drivers react in limit handling maneuvers. The [Formula: see text] accounts for both bidirectional dynamics and road geometry, and highlights regions where the Green Book’s standards are inconsistent with vehicle performance capabilities. The work concludes by posing recommendations aimed at implementing the [Formula: see text] in future Green Book editions.

Objective This study investigates the interaction between human perception and driver behavior on horizontal curves, focusing on how road geometry and visibility affect driving performance. Methods A driving simulator replicated 3 curve types by radius—200 m (sharp), 400 m (moderate), and 600 m (loose)—under day and night conditions. The focus of expansion (FOE) is the source point of optical flow, and an FOE model was established to linked the driver’s visual perception with vehicle dynamics. Data on eye movement and vehicle dynamics were collected from 24 drivers (mean age: 27 years, mean driving experience: 3.8 years). Results The results indicate that driving at night on sharp curves significantly impairs the ability of drivers to align their perception with vehicle motion, leading to delayed steering adjustments and increased lateral errors. The most dangerous areas, identified as the back half of the test curves and corresponding to the minimum FOE radius, were where the misalignment between perception and motion was most significant. On loose curves, decreased driver vigilance was observed, potentially due to a perceived reduction in steering demands, underscoring the role of psychological and contextual factors during curve negotiation. Conclusion This study underscores the importance of optimizing curve radii and enhancing the alignment between drivers’ visual perceptions and vehicle dynamics to reduce accident risks. In real traffic, placing traffic guidance mid-curve may better prompt drivers to slow down, particularly at night. Integrating FOE-based feedback into advanced driver assistance systems (ADAS) could further enhance performance by offering real-time cues tailored to curve geometry in low visibility.

This paper presents a case study of driving speed at horizontal curves in the regional road. The literature review of operating speed at horizontal curves in regional roads expresses the difference between design speed and driving speed. Driving speed was measured in ten horizontal curves on regional road No. 2610. Collected data was compared to the design speed, various design standards, and Operating Speed Prediction Models for traffic in low-volume roads. Based on result analysis, was proposed initial adjusted minimum radius of the horizontal curve based on operating speed. It is justified that proposed corrections based on operating speed ensure a credible speed limit effect on road safety. The performed experiment showed different tendencies comparing measured driving speed with permitted and design speeds. It was determined that dependent on specific curved section drivers tend to exceed posted speed limit from 17% to 98% of cases, and from 41% to 100% − the design speed in the horizontal curve. This research led to identifying the limitations of experimental research methodology. These limitations are related to experimental Site selection with different combinations of alignment elements, traffic, and accident data. A pilot study showed significant results and gave essential insights into the full-scale research plan. The results are expected to benefit both other researchers and the organisations responsible for the development and implementation of normative technical documents for road design.

Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments