This research study focused on the development and subsequent evaluation of an in-vehicle Active Traffic and Demand Management (ATDM) system deployed on I-66. The ATDM elements inside the vehicle allowed drivers to remain consistently aware of traffic conditions and roadway requirements even if external signage was inaccessible. Forty participants were accompanied by a member of the research team and experienced the following features from the in-vehicle device (IVD): 1) dynamic speed limits, 2) dynamic lane use/shoulder control, 3) High Occupancy Vehicle (HOV) restrictions, and 4) variable message signs (VMS). This system was equipped with auditory and visual alerts to notify the driver when relevant information was updated. The research questions addressed distraction, desirability, and driver behavior associated with the system. Participant data was collected from the instrumented vehicle, various surveys, and researcher observation. Analysis of Variance (ANOVA) and Tukey-Kramer tests were performed to analyze participant eye glance durations towards the IVD and instrument cluster. Wilcoxon signed rank tests were used to draw conclusions from participant speed data and some survey responses. Several key findings were uncovered related to each research category: 1) the IVD would not be classified as a distraction according to NHTSA distraction guidelines, 2) seventy-three percent of participants would want the in-vehicle technology in their next vehicle, and 3) the speed limit alert motivated participants to alter their speed (based on both survey results and actual participant speed data). / Master of Science

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle's onboard unit (OBU). The main research question asks: 'What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?' The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network's probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models. / Ph. D. / The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle’s onboard unit (OBU). The main research question asks: “What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?” The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network’s probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models.

Cette thèse présente des applications véhiculaires à différentes échelles : de la petite qui permet d'effectuer des tests réels de communication et de service ; à des plus grandes incluant plus de contraintes mais permettant des simulations sur l'ensemble du réseau. Dans ce contexte nous soulignons l'importance d'avoir et de traiter des données réelles afin de pouvoir interpréter correctement les résultats. A travers ces échelles nous proposons différents services utilisant la communication V2V et V2I. Nous ne prétendons pas prendre le contrôle du véhicule, notre but est de montrer le potentiel de la communication à travers différents services. La petite échelle se focalise sur un service à un feu de circulation permettant d'améliorer les temps de parcours et d'attente, et la consommation en CO2 et en carburant. La moyenne échelle se situant sur un rond-point, permet grâce à un algorithme décentralisé, d'améliorer ces mêmes paramètres, mais montre également qu'avec une prise de décision simple et décentralisée, le système est robuste face à la perte de paquet, à la densité, aux comportements humains ou encore aux taux d'équipement. Enfin à l'échelle d'une ville, nous montrons que grâce à des décisions prises de manière locale et décentralisée, avec seulement un accès à une information partielle dans le réseau, nous obtenons des résultats proches des solutions centralisées. La quantité de données transitant ainsi dans le réseau est considérablement diminuée. Nous testons également la réponse de ces systèmes en cas de perturbation plus ou moins importante tels que des travaux, un acte terroriste ou une catastrophe naturelle. Les modèles permettant une prise de décision locale grâce aux informations délivrées autour du véhicule montrent leur potentiel que se soit avec de la communication avec l'infrastructure V2I ou entre les véhicules V2V. / In this thesis we present vehicular applications across different scales: from small scale that allows real tests of communication and services; to larger scales that include more constraints but allowing simulations on the entire network. In this context, we highlight the importance of real data and real urban topology in order to properly interpret the results of simulations. We describe different services using V2V and V2I communication. In each of them we do not pretend to take control of the vehicle, the driver is present in his vehicle, our goal is to show the potential of communication through services taking into account the difficulties outlined above. In the small scale, we focus on a service with a traffic light that improves travel times, waiting times and CO2 and fuel consumption. The medium scale is a roundabout, it allows, through a decentralized algorithm, to improve the same parameters. It also shows that with a simple and decentralized decision-making process, the system is robust to packet loss, density, human behavior or equipment rate. Finally on the scale of a city, we show that local and decentralized decisions, with only a partial access to information in the network, lead to results close to centralized solutions. The amount of data in the network is greatly reduced. We also test the response of these systems in case of significant disruption in the network such as roadworks, terrorist attack or natural disaster. Models, allowing local decision thanks to information delivered around the vehicle, show their potential whatsoever with the V2I communication or V2V.