Scholarly Works, Virginia Tech Transportation Institute

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Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control
Elouni, Maha; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2021-01-03)
This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21% and 41%, total delay ranging between 40% and 55%, and emission levels/fuel consumption ranging between 12% and 20%. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.
Applicability of mesopic factors to the driving task
Gibbons, Ronald B.; Terry, Travis N.; Bhagavathula, Rajaram; Meyer, Jason E.; Lewis, A. (SAGE, 2016-02-01)
With the advent of light-emitting diode technology being applied to roadway lighting, the spectral power distribution of the light source is becoming much more important. In this experiment, the detection of pedestrians at five adaptation levels under three light sources, high pressure sodium and light emitting diodes of two colour temperatures was measured in realistic roadway scenarios. The results show that while the light source type was not significant, an increase in adaptation luminance increased the detection distance. As the offset of the object to the roadway increased, some spectral effects became more significant; however, this effect was not consistent across all angles of eccentricity. The conclusions from this work indicate that mesopic factors may not be applicable on high-speed roads.
Application of Balanced Mix Design Methodology to Optimize Surface Mixes with High-RAP Content
Meroni, Fabrizio; Flintsch, Gerardo W.; Diefenderfer, Brian K.; Diefenderfer, Stacey D. (MDPI, 2020-12-10)
The most common use of reclaimed asphalt pavement (RAP) is in the lower layers of a pavement structure, where it has been proven as a valid substitute for virgin materials. The use of RAP in surface mixes is more limited, since a major concern is that the high-RAP mixes may not perform as well as traditional mixes. To reduce risks or compromised performance, the use of RAP has commonly been controlled by specifications that limit the allowed amount of recycled material in the mixes. However, the ability to include greater quantities of RAP in the surface mix while maintaining a satisfying field performance would result in potential cost savings for the agencies and environmental savings for the public. The main purpose of this research was to produce highly recycled surface mixes capable of performing well in the field, verify the performance-based design procedure, and analyze the results. To produce the mixes, a balanced mix design (BMD) methodology was used and a comparison with traditional mixes, prepared in accordance with the requirements of the Virginia Department of Transportation’s volumetric mix design, was performed. Through the BMD procedure, which featured the indirect tensile cracking test for evaluating cracking resistance and the Asphalt Pavement Analyzer (APA) for evaluating rutting resistance, it was possible to obtain a highly recycled mix (45% RAP) capable of achieving a better overall laboratory performance than traditional mixes designed using volumetric constraints while resulting in a reduction in production cost.
AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research
Gabbard, Joseph L.; Smith, Missie; Tanous, Kyle; Kim, Hyungil; Jonas, Bryan (Frontiers, 2019-10-23)
Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.
Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections
Chen, Hao; Rakha, Hesham A. (MDPI, 2020-05-12)
This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.
Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health
Stern, Hal S.; Blower, Daniel; Cohen, Michael L.; Czeisler, Charles A.; Dinges, David F.; Greenhouse, Joel B.; Guo, Feng; Hanowski, Richard J.; Hartenbaum, Natalie P.; Krueger, Gerald P.; Mallis, Melissa M.; Pain, Richard F.; Rizzo, Matthew; Sinha, Esha; Small, Dylan S.; Stuart, Elizabeth A.; Wegman, David H. (Elsevier, 2019-05)
This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.
Decision-adjusted driver risk predictive models using kinematics information
Mao, Huiying; Guo, Feng; Deng, Xinwei; Doerzaph, Zachary R. (Elsevier, 2021-06)
Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.
Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data
Aljamal, Mohammad A.; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2019-10-07)
This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles’ market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate.
Effect of Intersection Lighting Design on Drivers' Perceived Visibility and Glare
Bhagavathula, Rajaram; Gibbons, Ronald B.; Nussbaum, Maury A. (SAGE, 2019-02-01)
A systems-level approach to intersection lighting design has shown that illuminating the intersection box increases drivers’ nighttime visual performance. However, for an intersection lighting design to be effective and accepted, it should not only maximize visual performance but also enhance perceived visibility and minimize glare. The goals of this study were to assess the effects of different intersection lighting designs on these two outcomes. Visibility was assessed with respect to a pedestrian, several targets, and an intersection. Perceptions of visibility and glare were measured using Likert scales, with participants exposed to multiple lighting designs on a realistic intersection. Twenty-four participants completed the study, with an equal number of younger (18–35 years) and older (65+) drivers. The lighting design that illuminated the intersection box had the highest levels of perceived target and intersection visibility and the lowest ratings of glare. For the same lighting configuration, a strong positive correlation was also found between perceived target visibility and previous results on target detection distances. In this configuration, perceived visibility plateaued between 7 and 10 lux of mean intersection illuminance. Increased levels of perceived visibility in different conditions were likely a result of size and contrast differences, and the distribution of the luminaires used. These results suggest that illuminating the intersection box has multiple benefits, in that it not only increases visual performance but also increases perceived visibility and reduces glare.
Effects of Intersection Lighting Design on Nighttime Visual Performance of Drivers
Bhagavathula, Rajaram; Gibbons, Ronald B.; Nussbaum, Maury A. (Taylor & Francis, 2018-01-01)
Nighttime crashes at intersections present a major traffic safety issue in the United States. Existing approaches to intersection lighting design do not account for a driver’s visual performance or the potential interactive effects of vehicle headlamps and roadway lighting. For effective design lighting at intersections, empirical research is required to evaluate the effects of lighting configuration (part of the intersection illuminated) and lighting levels on nighttime driver visual performance. The current study had two goals: First, to quantify visual performance in three lighting configurations (illuminating the intersection box, approach, or both) and second, to determine what lighting levels within each lighting configuration support the best visual performance. The study involved a target detection task completed at night on a realistic roadway intersection. Illuminating the intersection box led to superior visual performance, as indicated by longer target detection distances, fewer missed targets, and more targets identified within a safe stopping distance. For this lighting configuration, visual performance plateaued between 7 and 10 lx of mean intersection illuminance. These results have important implications for the design of intersection lighting at isolated/rural intersections, specifically that illuminating the intersection box is an effective strategy to increase nighttime visual performance for a wider range of driver ages and could also be an energy-efficient solution.
Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior
Wang, Jinghui; Rakha, Hesham A. (MDPI, 2020-06-08)
The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.
Environmental Impact of Freight Signal Priority with Connected Trucks
Park, Sangjun; Ahn, Kyoungho; Rakha, Hesham A. (MDPI, 2019-12-01)
Traffic signal priority is an operational technique employed for the smooth progression of a specific type of vehicle at signalized intersections. Transit signal priority is the most common type of traffic signal priority, and it has been researched extensively. Conversely, the impacts of freight signal priority (FSP) has not been widely investigated. Hence, this study aims to evaluate the energy and environmental impacts of FSP under connected vehicle environment by utilizing a simulation testbed developed for the multi-modal intelligent transportation signal system. The simulation platform consists of VISSIM microscopic traffic simulation software, a signal request messages distributor program, an RSE module, and an Econolite ASC/3 traffic controller emulator. The MOVES model was employed to estimate the vehicle fuel consumption and emissions. The simulation study revealed that the implementation of FSP significantly reduced the fuel consumption and emissions of connected trucks and general passenger cars; the network-wide fuel consumption was reduced by 11.8%, and the CO₂, HC, CO, and NO_X emissions by 11.8%, 28.3%, 24.8%, and 25.9%, respectively. However, the fuel consumption and emissions of the side-street vehicles increased substantially due to the reduced green signal times on the side streets, especially in the high truck composition scenario.
Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches
Aljamal, Mohammad A.; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2020-07-22)
The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.
Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
Xiong, Haocheng; Zhang, Yinning (MDPI, 2019-07-31)
Weigh-in-Motion system has been the primary selection of U.S. government agencies as the weighing enforcement for decades to protect the road pavement. In recent years, the number of trucks has increased by about 40% and in 2017, they travel 25% more annually than in 2016. The lack of the budget has slowed down the expansion of weighing enforcement to catch up with the growing workload of vehicle weighing. Unsupervised pavement section suffers more pavement damage and increased repairing cost. In this work, a piezoelectric material based WIM system (P-WIM) is developed. Such a system consists of several piezoelectric material disks that are capable of generating characteristic voltage output from passing vehicles. The axle loading of the vehicle can be determined by analyzing the voltage generated from the P-WIM. Compared to traditional WIM system, P-WIM requires nearly zero maintenance and costs 80% less on capital investment and less labor and effort to integrate. To evaluate the feasibility of this technology to serve as weighing enforcement on public roadways, prototype P-WIMs are fabricated and installed at a weigh station. The vehicle loading information provided by the weigh station is used to determine the force transmission percentage of the installed P-WIMs, which is an important parameter to determine the vehicles’ axle loading by generated voltage.
Flexible Pavements and Climate Change: A Comprehensive Review and Implications
Qiao, Yaning; Dawson, Andrew R.; Parry, Tony; Flintsch, Gerardo W.; Wang, Wenshun (MDPI, 2020-02-02)
Flexible pavements and climate are interactive. Pavements are climate sensitive infrastructure, where climate can impact their deterioration rate, subsequent maintenance, and life-cycle costs. Meanwhile, climate mitigation measures are urgently needed to reduce the environmental impacts of pavements and related transportation on the macroclimate and microclimate. Current pavement design and life cycle management practices may need to be modified to adapt to changing climates and to reduce environmental impacts. This paper reports an extensive literature search on qualitative and quantitative pavement research related to climate change in recent years. The topics cover climate stressors, sensitivity of pavement performance to climatic factors, impacts of climate change on pavement systems, and, most importantly, discussions of climate change adaptation, mitigation, and their interactions. This paper is useful for those who aim to understand or research the climate resilience of flexible pavements.
Impact of Intersection Control on Battery Electric Vehicle Energy Consumption
Ahn, Kyoungho; Park, Sangjun; Rakha, Hesham A. (MDPI, 2020-06-19)
Battery electric vehicle (BEV) sales have significantly increased in recent years. They have different energy consumption patterns compared to the fuel consumption patterns of internal combustion engine vehicles (ICEVs). This study quantified the impact of intersection control approaches—roundabout, traffic signal, and two-way stop controls—on BEVs’ energy consumption. The paper systematically investigates BEVs’ energy consumption patterns compared to the fuel consumption of ICEVs. The results indicate that BEVs’ energy consumption patterns are significantly different than ICEVs’ patterns. For example, for BEVs approaching a high-speed intersection, the roundabout was found to be the most energy-efficient intersection control, while the two-way stop sign was the least efficient. In contrast, for ICEVs, the two-way stop sign was the most fuel-efficient control, while the roundabout was the least efficient. Findings also indicate that the energy saving of traffic signal coordination was less significant for BEVs compared to the fuel consumption of ICEVs since more regenerative energy is produced when partial or poorly coordinated signal plans are implemented. The study confirms that BEV regenerative energy is a major factor in energy efficiency, and that BEVs recover different amounts of energy in different urban driving environments. The study suggests that new transportation facilities and control strategies should be designed to enhance BEVs’ energy efficiency, particularly in zero emission zones.
Joint Impact of Rain and Incidents on Traffic Stream Speeds
Elhenawy, Mohammed; Rakha, Hesham A.; Ashqar, Huthaifa I. (Hindawi, 2021-01-11)
Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds is essential for policymakers to mitigate congestion and improve road safety. This study investigates the effect of precipitation and incidents on the speed of traffic in the eastbound direction of I-64 in Virginia. To the best of our knowledge, this is the first study that studies the relationship between precipitation and incidents as factors that would have a combined effect on traffic stream speeds. Furthermore, using a mixture model of two linear regressions, we were able to model the two different regimes that the traffic speed could be classified into, namely, free-flow and congested. Using INRIX traffic data from 2013 through 2016 along a 25.6-mi section of Interstate 64 in Virginia, results show that the reduction of traffic speed only due to incidents ranges from 41% to 75% if the road is already congested. In this case, precipitation was found to be statistically insignificant. However, regardless of the incident impact, the effect of light rain in free-flow conditions ranges from insignificant to a 4% speed reduction while the effect of heavy rain ranges from a 0.6% to a 6.5% speed reduction when the incident severity is low but has a roughly double effect when the incident severity is high.
A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems
Elhenawy, Mohammed; Komol, Mostafizur R.; Masoud, Mahmoud; Liu, Shi Qiang; Ashqar, Huthaifa I.; Almannaa, Mohammed Hamad; Rakha, Hesham A.; Rakotonirainy, Andry (MDPI, 2021-07-06)
Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.
A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing
Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2019-05-17)
This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, where players cooperate to reach a mutually agreeable outcome. The controller is implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software, comparing its performance to that of a traditional decentralized adaptive cycle length and phase split traffic signal controller and a centralized fully-coordinated adaptive phase split, cycle length, and offset optimization controller. The comparisons are conducted in the town of Blacksburg, Virginia (38 traffic signalized intersections) and in downtown Los Angeles, California (457 signalized intersections). The results for the downtown Blacksburg evaluation show significant network-wide efficiency improvements. Specifically, there is a 23.6 % reduction in travel time, a 37.6 % reduction in queue lengths, and a 10.4 % reduction in CO 2 emissions relative to traditional adaptive traffic signal controllers. In addition, the testing on the downtown Los Angeles network produces a 35.1 % reduction in travel time on the intersection approaches, a 54.7 % reduction in queue lengths, and a 10 % reduction in CO 2 emissions compared to traditional adaptive traffic signal controllers. The results demonstrate significant potential benefits of using the proposed controller over other state-of-the-art centralized and de-centralized adaptive traffic signal controllers on large-scale networks both during uncongested and congested conditions.
Probability of detection of electric vehicles with and without added warning sounds
Roan, Michael J.; Neurauter, Luke; Song, Miao; Miller, Marty (Acoustic Society of America, 2021-01-26)
Detection performance as a function of distance was measured for 16 subjects who pressed a button upon aurally detecting the approach of an electric vehicle. The vehicle was equipped with loudspeakers that broadcast one of four additive warning sounds. Other test conditions included two vehicle approach speeds [10 and 20 km/h (kph)] and two background noise conditions (55 and 60 dBA). All of the test warning sounds were designed to be compliant with FMVSS 141 proposed regulations in regard to the overall sound pressure levels around the vehicle and in 1/3 octave band levels. Previous work has provided detection results as average vehicle detection distance. This work provides the results as probability of detection (Pd) as a function of distance. The curves provide insight into the false alarm rate when the vehicle is far away from the listeners as well and the Pd at the mean detection distance. Results suggest that, although the test sounds provide an average detection distance that exceeds the National Highway Traffic Safety Administration minimum at the two test speeds, Pd is not always 100% at those distances, particularly at the 10 kph. At the higher speed of 20 kph, the tire-road interaction noise becomes dominant, and the detection range is greatly extended.

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