Delay margin comparison in a velocity-only versus headway-only connected vehicle model

2021 ◽  
pp. 095965182110555
Author(s):  
Duo Wang ◽  
Adrián Ramírez ◽  
Rifat Sipahi
Keyword(s):  
Reaction Times ◽  
Connected Vehicle ◽  
Computational Tool ◽  
Velocity Measurements ◽  
Vehicle Model ◽  
Equilibrium Dynamics ◽  
Trade Offs ◽  
Underlying Network ◽  
Communication Lines ◽  
Delay Margin

Two different connected vehicle models are considered: one based on only using velocity measurements and the other using only headway, where each model is affected by delays due to human reaction times and sensing and communication lines. The presence of delays is established between any two vehicles that are neighbors as determined by the underlying network topology. The main focus here is to put light into our understanding of how much of time delay can the equilibrium of these models tolerate before becoming unstable. To this end, we compute, using the parallel processing computational tool Delay Margin Finder (parDMF), the delay margin of the equilibrium dynamics and discuss design trade-offs. We report that delay margin in the headway-only model is much less sensitive against the presence/absence of links in the network and that this model can have larger delay margin when larger number of vehicles are capable of communicating with other vehicles in the platoon.

KNIFE-EDGE CONDITIONS AND THE MACRODYNAMICS OF SMALL OPEN ECONOMIES

2002 ◽  
Vol 6 (2) ◽  
pp. 307-335 ◽  
Author(s):  
Stephen J. Turnovsky
Keyword(s):  
Growth Model ◽  
Open Economies ◽  
Small Open Economies ◽  
Knife Edge ◽  
Generic Structure ◽  
Equilibrium Dynamics ◽  
Trade Offs ◽  
Special Cases ◽  
Edge Conditions ◽  
Endogenous Growth Model

Macrodynamic models of small open economies are inevitably characterized by “knife-edge conditions,” meaning that certain parameters are constrained for a viable equilibrium to exist. This paper examines the macrodynamic structure of such an economy and considers the role played by various standard knife-edge conditions. The dynamic model presented is sufficiently general so as to provide a unifying framework within which alternative models can be embedded. We identify three important models as special cases of this generic structure: (i) The traditional stationary Ramsey model, (ii) the endogenous growth model, and (iii) the nonscale growth model. We consider three margins along which knife-edge conditions are imposed. These include (i) preference parameters, (ii) production and employment characteristics, and (iii) openness of international financial markets. These restrictions are shown to play key roles in determining the equilibrium dynamics, and how the economy responds to various shocks. The existence of trade-offs between these knife-edge conditions is discussed.

User Acceptance and Willingness to Pay for Connected Vehicle Technologies: Adaptive Choice-Based Conjoint Analysis

2015 ◽  
Vol 2531 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Hyeon-Shic Shin ◽  
Michael Callow ◽  
Seyedehsan Dadvar ◽  
Young-Jae Lee ◽  
Z. Andrew Farkas
Keyword(s):  
Willingness To Pay ◽  
Online Survey ◽  
Connected Vehicle ◽  
Purchasing Decisions ◽  
Important Constraint ◽  
Trade Offs ◽  
Collision Prevention ◽  
Adaptive Choice ◽  
The Difference ◽  
Vehicle Technologies

The preferences of drivers and their willingness to pay (WTP) for connected vehicle (CV) technologies were estimated with the use of adaptive choice-based conjoint (ACBC) analysis, the newest such method available. More than 500 usable surveys were collected through an online survey. Respondents were asked to choose from variously priced CV technology bundles (e.g., collision prevention, roadway information system). The study found that the acceptance level of the CV technologies was high, given that an absolute majority of survey respondents had the highest preferences for the most comprehensive technology bundle in each attribute. However, a comparison of the average importance of each attribute, including bundle prices, implied that price would be an important constraint and would influence CV deployment rates. At the attribute level, collision prevention technology received the highest importance score (i.e., the safety benefits most appealed to drivers). The ACBC analysis seemed to mimic well the trade-offs that people would consider in their actual purchasing decisions. The difference between WTP and self-explicated prices obtained before preferences were estimated was statistically significant (i.e., participants chose bundles after they considered product attributes and prices). This finding also affirmed that the ACBC analysis was a more appropriate method than the direct questioning methods used in past studies. Finally, certain socioeconomic characteristics were positively related to WTP. Those respondents that were knowledgeable about CV technologies and showed more innovativeness had higher WTP as well.

scholarly journals Multi-Timescale Drowsiness Characterization Based on a Video of a Driver’s Face

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2801 ◽  
Author(s):  
Quentin Massoz ◽  
Jacques Verly ◽  
Marc Van Droogenbroeck
Keyword(s):  
Real Time ◽  
Facial Expressions ◽  
Cross Validation ◽  
Time Window ◽  
Reaction Times ◽  
Ground Truth ◽  
Trade Off ◽  
Trade Offs ◽  
Psychomotor Vigilance ◽  
Long Timescales

Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and timely warnings of drowsiness to the driver. In practice, the least intrusive, physiology-based approach is to remotely monitor, via cameras, facial expressions indicative of drowsiness such as slow and long eye closures. Since the system’s decisions are based upon facial expressions in a given time window, there exists a trade-off between accuracy (best achieved with long windows, i.e., at long timescales) and responsiveness (best achieved with short windows, i.e., at short timescales). To deal with this trade-off, we develop a multi-timescale drowsiness characterization system composed of four binary drowsiness classifiers operating at four distinct timescales (5 s, 15 s, 30 s, and 60 s) and trained jointly. We introduce a multi-timescale ground truth of drowsiness, based on the reaction times (RTs) performed during standard Psychomotor Vigilance Tasks (PVTs), that strategically enables our system to characterize drowsiness with diverse trade-offs between accuracy and responsiveness. We evaluated our system on 29 subjects via leave-one-subject-out cross-validation and obtained strong results, i.e., global accuracies of 70%, 85%, 89%, and 94% for the four classifiers operating at increasing timescales, respectively.

Uncertainty Quantification in Vehicle Content Optimization for General Motors

2020 ◽  
Vol 50 (4) ◽  
pp. 225-238
Author(s):  
Eunhye Song ◽  
Peiling Wu-Smith ◽  
Barry L. Nelson
Keyword(s):  
Uncertainty Quantification ◽  
Business Performance ◽  
Estimation Error ◽  
Computation Time ◽  
Performance Measure ◽  
General Motors ◽  
Vehicle Model ◽  
Computational Overhead ◽  
Trade Offs ◽  
Content Optimization

A vehicle content portfolio refers to a complete set of combinations of vehicle features offered while satisfying certain restrictions for the vehicle model. Vehicle Content Optimization (VCO) is a simulation-based decision support system at General Motors (GM) that helps to optimize a vehicle content portfolio to improve GM’s business performance and customers’ satisfaction. VCO has been applied to most major vehicle models at GM. VCO consists of several steps that demand intensive computing power, thus requiring trade-offs between the estimation error of the simulated performance measures and the computation time. Given VCO’s substantial influence on GM’s content decisions, questions were raised regarding the business risk caused by uncertainty in the simulation results. This paper shows how we successfully established an uncertainty quantification procedure for VCO that can be applied to any vehicle model at GM. With this capability, GM can not only quantify the overall uncertainty in its performance measure estimates but also identify the largest source of uncertainty and reduce it by allocating more targeted simulation effort. Moreover, we identified several opportunities to improve the efficiency of VCO by reducing its computational overhead, some of which were adopted in the development of the next generation of VCO.

Stop Sign Gap Assist Application in a Connected Vehicle Simulation Environment

2021 ◽  
pp. 036119812110061
Author(s):  
Mahmoud Arafat ◽  
Mohammed Hadi ◽  
Thodsapon Hunsanon ◽  
Kamar Amine
Keyword(s):  
Simulation Models ◽  
Utilization Rate ◽  
Connected Vehicle ◽  
Acceptance Probability ◽  
Vehicle Simulation ◽  
Unsignalized Intersections ◽  
Trade Offs ◽  
Stop Sign ◽  
Study Results ◽  
Gap Time

Assessment of the safety and mobility impacts of connected vehicles (CVs) and cooperative automated vehicle applications is critical to the success of these applications. In many cases, there may be trade-offs in the mobility and safety impacts depending on the setting of the parameters of the applications. This study developed a method to evaluate the safety and mobility benefits of the Stop Sign Gap Assist (SSGA) system, a CV-based application at unsignalized intersections, which utilizes a calibrated microscopic simulation tool. The study results confirmed that it was critical to calibrate the drivers’ gap acceptance probability distributions in the utilized simulation model to reflect real-world driver behaviors when assessing SSGA impacts. The simulation models with the calibrated gap parameters were then used to assess the impacts of the SSGA. The results showed that SSGA can potentially improve overall minor approach capacity at unsignalized intersections by approximately 35.5% when SSGA utilization reaches 100%. However, this increase in capacity depended on the setting of the minimum gap time in the SSGA and there was a clear trade-off between capacity and safety. The analysis indicated that as the minimum gap time used in the SSGA increased, the safety of the intersection increased, showing for example that with the utilization of an 8-s gap at a 750 vph main street flow rate, the number of conflicts could decrease by 30% as the SSGA utilization rate increased from 0% to 100%.

scholarly journals Analysis of speed-accuracy trade-offs using the Wiener diffusion model for choice reaction times

2021 ◽  
Author(s):  
Annalise Aleta LaPlume
Keyword(s):  
Response Time ◽  
Diffusion Model ◽  
Response Times ◽  
Reaction Times ◽  
Response Time Data ◽  
Choice Response ◽  
Time Data ◽  
Trade Offs ◽  
R Programming ◽  
Speed Accuracy

A methodology review paper on the utility and challenges of modelling speed-accuracy trade-offs in response time data. The paper reviews the importance of accounting for speed-accuracy trade-offs when measuring response times, and provides background on diffusion models for response time data. It then describes a practical software implementation of the EZ-diffusion model to model speed-accuracy trade-offs in choice response time data using the R programming language.

scholarly journals Ego-Centered Cognitive Social Structures of Close Personal Networks in the United States

2017 ◽  
Author(s):  
Christopher Steven Marcum ◽  
Jeffrey Lienert ◽  
Megan Goldring ◽  
Jielu Lin ◽  
Alicia Miggins ◽  
...  
Keyword(s):  
Network Structure ◽  
The United States ◽  
Cognitive Networks ◽  
Social Structures ◽  
Personal Networks ◽  
Social Worlds ◽  
Mental Maps ◽  
Trade Offs ◽  
The Social ◽  
Underlying Network

Social network analysis is increasingly important in the social and behavioral sciences and has been employed to study a host of inter- and intra-personal social processes. One of the challenges researchers face in this area, however, is balancing the trade-offs between different modes of network measurement and study design. At one end of the spectrum, entirely ego-centered network designs facilitate access to a large, generalizable sample of the population but often lack details on the underlying network structure that embed each respondent. At the other end, whole-network designs offer fine details about the network structure but are costly and suffer from generalizability limitations. In this paper, we employ an ego-centered network sampling design that strikes a balance between these two cases by leveraging how individuals perceive their social worlds vis-a-vis respondent reports of their alter-alter ties. We describe a large sample of close personal networks where respondents informed on their perceptions of the ties between their alters on multiple types of relations. Specifically, we characterize the distribution of network statistics (size, density, and multiplexity) for over a thousand individual ego-centered cognitive networks drawn from a representative sample of the U.S. population. To our knowledge this is the first study to characterize the distribution of mental maps vis-a-vis perceived alter-alter relationships in this large of a sample of respondents involved in close personal networks. In doing so, we more clearly shed light on how Americans perceive the structure of their social worlds and provide an empirical case study in what we characterize as ego-centered cognitive social structures.

Joint Optimization of Signal Phasing and Timing and Vehicle Speed Guidance in a Connected and Autonomous Vehicle Environment

2019 ◽  
Vol 2673 (4) ◽  
pp. 70-83 ◽  
Author(s):  
Xiao (Joyce) Liang ◽  
S. Ilgin Guler ◽  
Vikash V. Gayah
Keyword(s):  
Autonomous Vehicle ◽  
Reaction Times ◽  
Signal Timing ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Average Delay ◽  
Human Errors ◽  
Human Driver ◽  
Sensitivity Tests ◽  
Acceptance Levels

A joint traffic signal optimization algorithm is proposed which utilizes connected vehicle (CV) information to identify optimum signal timing and phasing plans while also providing speed guidance to individual vehicles to minimize total number of stopping maneuvers. The contribution of this paper is provision of speed guidance to both autonomous (AVs) and human-driven speed guidance-enabled vehicles (SGVs), recognizing that the latter may not fully comply with the speed guidance and would require some delay (i.e., reaction time) to implement it. The control algorithm is triggered at regular discrete time intervals during which CV information is used to identify the presence of non-CVs and incorporate them into signal timing decision-making. Optimal speeds are determined for any AVs or SGVs so that they can travel through the intersection at the expected departure time without stopping, considering both acceleration/deceleration and human reaction times. Simulation tests are performed under different CV, AV, and SGV penetration rates, while explicitly modeling the potential human errors and varying acceptance levels. The results suggest that average delay and number of stops decrease with higher CV penetration rate. Furthermore, the number of stops decreases as the ratio of both AVs and SGVs increases. While AVs are about 10% more efficient than SGVs, human-driven vehicles still provide a benefit even when they do not fully comply with speed guidance information. Sensitivity tests suggest that operation is not significantly affected by the range of human driver errors in speed compliance or range of reaction times.

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