Connected Vehicle research has emerged as one of the highest priorities in the transportation systems because connected vehicle technology has the potential to improve safety, mobility, and environment for the current transportation systems. Various connected vehicle based applications have been identified and evaluated through various measurements to assess the performance of connected vehicle applications. However, most of these previous studies have used hypothetical study areas with simple networks for connected vehicle environment. This study represents connected vehicle environment in TRANSIMS to assess the performance of V2V communication applications in the realistic urban network. The communication duration rate and spatial-temporal dispersion of equipped vehicles are investigated to evaluate the capability of V2V communication based on the market penetration rate of equipped vehicles and wireless communication coverage in the whole study area. The area coverage level is used to assess the spatial-temporal dispersion of equipped vehicles for two study areas. The distance of incident information propagation and speed estimation error are used to measure the performance of event-driven and periodic applications based on different market penetration rates of equipped vehicles and wireless communication coverage in both morning peak and non-peak times. The wireless communication coverage is the major factor for event-driven application and the market penetration rate of equipped vehicles has more impact on the performance of periodic application. The required minimum levels of deployment for each application are determined for each scenario. These study findings will be useful for making decisions about investments on deployment of connected vehicle applications to improve the current transportation systems. Notably, event-driven applications can be reliably deployed in the initial stage of deployment despite the low level of market penetration of equipped vehicles. / Ph. D.

Σκοπός αυτής της διπλωματικής εργασίας είναι να αναπτυχθεί ένα μοντέλο αποφυγής συγκρόυσεων μεταξύ των οχημάτων μέσω της επικοινωνίας αυτών. Το σύστημα αυτό θα πρέπει να αναγνωρίζει τις περιπτώσεις που εγκυμονούν κίνδυνο μέσω της ανταλλαγής μηνυμάτων από τα οχήματα κάθε χρονική στιγμή και μέσα από διάφορους μηχανισμούς που επιτελούνται από αυτό, τελικά, να επεμβαίνει και να αποτρέπει τη σύγκρουση ή να ειδοποιεί τον οδηγό ώστε αυτός να αντιδράσει εγκαίρως. Για να το πετύχουμε αυτό ακολουθήσαμε τα εξής βήματα: 1. Προσομοιώσαμε τη λειτουργία μίας συσκευής GPS. 2. Σχεδιάσαμε λεκτικά και γραφικά τα μοντέλα κίνησης στα οποία θα τρέχει το σύστημά μας. 3. Ορίσαμε ένα πρωτόκολλο ανταλλαγής μηνυμάτων και προειδοποίησης σε περίπτωση συγκρουσης για αυτά τα μοντέλα κίνησης. 4. Αναπτύξαμε το σύστημα μέσω της matlab, λαμβάνοντας υπόψιν το πρωτόκολλο και τα σενάρια κίνησης των προηγούμενων βημάτων. 5. Κατασκευάσαμε μία διεπαφή εποπτίας και ελέγχου όλου του συστήματος. 6. Τέλος, κατασκευάσαμε ένα GUI ειδοποίησης του οδηγού σε περιπτώσεις κινδύνου. / Distributed traffic Control for Optimisation of the Safety Conditions.

Vehicle-to-vehicle (V2V) communication using Dedicated Short Range Communications (DSRC) technology promises to help drastically reduce vehicle collisions. DSRC allows vehicles in a highly mobile and complex network to send and receive safety messages with more reliability and lower latency compared with other wireless technologies used for automotive communications. However, there are many factors that could cause a safety-critical automotive application to become unreliable due to communication failures. While the reliability of V2V communication has been a subject of study by several researchers, there are still open questions regarding how the placement of the DSRC devices (inside or outside the host vehicle), the vehicle’s interior elements and the differences in altitude can affect the V2V communications. This article provides experimental testing data and analyses in order to quantify the impacts of relative vehicle speeds, altitude differences between vehicles, and interior obstacles on V2V communication range and reliability for opposite traffic, in both city and highway environments. We discuss how these results can adversely affect the design parameters of safety critical applications by considering the V2V application “Safe Pass Advisory” on two-lane rural highways.

New cooperative intelligent transportation system (C-ITS) applications become enabled thanks to advances in communication technologies between vehicles(V2V) and with the infrastructure (V2I). Communicating vehicles share information with each other and cooperate, which results in improved safety, fuel economy, and traffic efficiency. An example of a C-ITS application is platooning, which comprises a string of vehicles that travel together with short inter-vehicle distances (IVDs). Any solution related to C-ITS must comply with high safety requirements in order to pass standardization and be commercially deployed. Furthermore, trusted safety levels should be assured even for critical scenarios. This thesis studies the conditions that guarantee safety in emergency braking scenarios for heterogeneous platooning, or string-like, formations of vehicles. In such scenarios, the vehicle at the head of the string emergency brakes and all following vehicles have to automatically react in time to avoid rear-end collisions. The reaction time can be significantly decreased with vehicle-to-vehicle (V2V) communication usage since the leader can explicitly inform other platooning members about the critical braking. The safety analysis conducted in the thesis yields computationally efficient methods and algorithms for calculating minimum inter-vehicle distances that allow avoiding rear-end collisions with a predefined high guarantee. These IVDs are theoretically obtained for an open-loop and a closed-loop configurations. The former implies that follower drives with a constant velocity until braking starts, whereas in the latter, an adaptive cruise control (ACC) with a constant-distance policy serves as a controller. In addition, further optimization of inter-vehicle distances in the platoon is carried out under an assumption of centralized control. Such an approach allows achieving better fuel consumption and road utilization. The performed analytical comparison suggests that our proposed V2V communication based solution is superior to classical automated systems, such as automatic emergency braking system (AEBS), which utilizes only onboard sensors and no communication. Wireless communication, enabling to know the intentions of other vehicles almost immediately, allows for smaller IVDs whilst guaranteeing the same level of safety. Overall, the presented thesis highlights the importance of C-ITS and, specifically, V2V in the prevention of rear-end collisions in emergency scenarios. Future work directions include an extension of the obtained results by considering more advanced models of vehicles, environment, and communication settings; and applying the proposed algorithms of safety guaranteeing to other controllers, such as ACC with a constant time headway policy.

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.

Dans le cadre du développement des innovations dans les Systèmes de Transport Intelligents, les véhicules connectés devront être capables de télécharger des informations basées sur la position sur et depuis des serveurs distants. Ces véhicules seront équipés avec des différentes technologies d’accès radio, telles que les réseaux cellulaires ou les réseaux véhicule-à-véhicule (V2V) comme IEEE 802.11p. Les réseaux cellulaires, avec une couverture presque omniprésente, fournissent un accès à internet avec garanties de qualité de service. Cependant, l’accès à ces réseaux est payant. Dans cette thèse, un algorithme de clustering multi-saut est proposé avec pour objectif de réduire le coût d’accès au réseau cellulaire en agrégeant des données sur le réseau V2V. Pour faire ceci, le leader du cluster (CH, de l’anglais Cluster Head) est utilisé comme passerelle unique vers le réseau cellulaire. Pour le test d’une application d’exemple pour télécharger du Floating Car Data agrégé, les résultats des simulations montrent que cette approche réduit l’utilisation du réseau cellulaire de plus de 80%, en s’attaquant à la redondance typique des données basées sur la position dans les réseaux véhiculaires. Il y a une contribution en trois parties : Premièrement, une approche pour déléguer la sélection du CH à la station de base du réseau cellulaire afin de maximiser la taille des clusters, et par conséquent le taux de compression. Deuxièmement, un algorithme auto-adaptatif qui change dynamiquement le nombre maximum de sauts afin de maintenir un équilibre entre la réduction des coûts d’accès au réseau cellulaire et le taux de perte de paquets dans le réseau V2V. Finalement, l’incorporation d’une théorie de la justice distributive, afin d’améliorer l’équité sur la durée concernant la distribution des coûts auxquels les CH doivent faire face, améliorant ainsi l’acceptabilité sociale de la proposition. Les algorithmes proposés ont été testés via simulation, et les résultats montrent une réduction significative dans l’utilisation du réseau cellulaire, une adaptation réussie du nombre de sauts aux changements de la densité du trafic véhiculaire, et une amélioration dans les métriques d’équité, sans affecter la performance des réseaux. / For the development of innovative Intelligent Transportation Systems applications, connected vehicles will frequently need to upload and download position-based information to and from servers. These vehicles will be equipped with different Radio Access Technologies (RAT), like cellular and vehicle-to-vehicle (V2V) technologies such as LTE and IEEE 802.11p respectively. Cellular networkscan provide internet access almost anywhere, with QoS guarantees. However, accessing these networks has an economic cost. In this thesis, a multi-hop clustering algorithm is proposed in the aim of reducing the cellular access costs by aggregating information and off-loading data in the V2V network, using the Cluster Head as a single gateway to the cellular network. For the example application of uploading aggregated Floating Car Data, simulation results show that this approach reduce cellular data consumption by more than 80% by reducing the typical redundancy of position-based data in a vehicular network. There is a threefold contribution: First, an approach that delegates the Cluster Head selection to the cellular base station in order to maximize the cluster size, thus maximizing aggregation. Secondly, a self-adaptation algorithm that dynamically changes the maximum number of hops, addressing the trade-off between cellular access reduction and V2V packet loss. Finally, the incorporation of a theory of distributive justice, for improving fairness over time regarding the distribution of the cost in which Cluster Heads have to incur, thus improving the proposal’s social acceptability. The proposed algorithms were tested via simulation, and the results show a significant reduction in cellular network usage, a successful adaptation of the number of hops to changes in the vehicular traffic density, and an improvement in fairness metrics, without affecting network performance.