With the growing number of vehicles on the road, improving traffic safety and efficiency has become a major challenge. One of the promising solutions is the implementation of Intelligent Transportation Systems (ITS), which utilizes communication technologies to facilitate real-time information sharing between vehicles and infrastructure. This research aims to contribute to the field of Intelligent Transportation Systems (ITS) improving traffic safety and efficiency achieving the Vision Zero goal. In this thesis, we focus on the performance analysis of two communication protocols used for two signal dissemination techniques for IVI (infrastructure-vehicle-information communication). Periodic, which sends signals at fixed intervals, and On-demand which sends signals upon request from road users. The results of our experiments showed that there are trade-offs between the periodic and on-demand approaches in V2I communications. The on-demand approach performs better in terms of PER (Packet error ratio), but it results in higher latency, even with low congestion. On the other hand, the periodic approach exhibits higher PDR (Packet delivery ratio) but suffers from inefficiencies due to the transmission of redundant messages resulting in higher PER. Our findings have implications for the scalability of V2I communications and can be translatable to other access technologies used in ITS.

Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control. / Ph. D.

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

In this research oriented master’s thesis we have proposed a future vision of ITS (Intelligent Transport Systems) by utilizing the novel concept of HWA (Heterogeneous Wireless Access). Our proposal is backed by the investigation of the results of experiments conducted at CERES (Centre for Research on Embedded Systems), Halmstad University, Sweden to evaluate the quality of communication for V2V and V2I by using the IEEE 802.11p standard. We have also identified the expected scenarios with need of any other communication technology in replacement of IEEE 802.11p for V2V and V2I communication. We have also investigated the relevant research projects, experiments and their results on the basis of predefined constraints. In the investigated research projects the concept of HWA has been correlated with our proposal of HWA for ITS. We have identified that for smooth integration of any communication technology with IEEE 802.11p, an efficient and smart vertical handover protocol or method will be required. We have presented a blue print of a custom designed vertical handover technique which can be implemented for future ITS with further enhancements and experimental evaluations. We have also evaluated the worst case scenarios to assess the suitability of the HWA for the ITS. We proposed few solutions based on the evaluation of communication scenarios for the integration of IEEE 802.11p with other wireless communication technologies. Finally we have provided some conclusions and suggested future researches which must be conducted to realize the dream of ITS with support of HWA.

Ces travaux de thèse sont réalisés au sein de l’équipe STI du laboratoire LITIS, en collaboration avec le centre de robotique CAOR de l’école des mines de Paris et l’INRIA Rocquencourt dont ils ont utilisé la plateforme du prototype LARA composée de véhicules instrumentés. L’objectif est de contribuer à la localisation des véhicules intelligents équipés de récepteurs GPS (Global Positionning System), de systèmes de vision et du matériel de communication permettant la coopération entre ces véhicules. En milieu urbain, les performances du GPS sont fortement dégradées. La réception du signal GPS souffre de masquages ou de mauvaises configurations géométriques des satellites. De plus, la qualité du signal peut être corrompue à cause du phénomène de multi-trajets lié à la réflexion du signal sur les bâtiments, tunnels... Alors la robustesse, la précision et la disponibilité de l’estimation de la position peut décroître significativement. D’où la nécessité d’une source d’information complémentaire pour compenser les faiblesses du récepteur GPS. L’originalité de nos travaux consiste à utiliser les données exploitées par notre système de vision. Le système de vision utilisé est basé sur une caméra (monovision). Il permet la détection robuste des obstacles sur la route, ainsi que la détection de la pluie. Le calcul de la distance de l’obstacle à notre véhicule est réalisé à l’aide du modèle sténopé et l’hypothèse de la route plane. Les véhicules utilisant des systèmes de communication sans fil basé sur la norme 802.11g+ coopèrent entre eux en échangeant leurs coordonnées GPS si elles sont disponibles. Cette coopération permet de connaître la position des véhicules qui nous entourent. Le système de communication est aussi utilisé pour l’alerte météorologique V2I ou V2V en utilisant la détection de la pluie réalisée en collaboration avec Valeo. Pour réaliser le positionnement relatif fiable, nous avons mis en oeuvre un algorithme de suivi basé sur le filtrage particulaire. Cette méthode permet de fusionner les données en utilisant les techniques probabilistes lors des différentes étapes du filtre. Finalement, une validation expérimentale en temps réel sur les véhicules du prototype LARA a été réalisée sur différents scénarios. / This thesis work realised at the STI team of the LITIS Laboratory, in collaboration with the Center of Robotics CAOR at the Ecole des Mines of Paris and the INRIA Rocquencourt, and tested on the prototype LARA. The aim is to better positionning of intelligent vehicles equipped with GPS, vision systems and communication devices used for cooperation between vehicles. In urban areas, The usage of GPS is not always ideal because of the poorness of the satellite coverage. Sometimes, the GPS signal may be also corrupted by multipath reflections due to tunnels, high buildings, electronic interferences etc. So, in order to accurate the vehicle positioning in the navigation application, the GPS data will be enhanced with vision data using communication between vehicles. The vision system is based on a monocular real-time vision-based vehicle detection. We can calculate the distance between vehicles using the pinhole model. We developped a rain detection system using the same camera. The inter-vehicle cooperation is made possible thanks to the revolution in the wireless mobile ad hoc network. Localization information can be exchanged between the vehicles through a wireless communication devices. The creation of the system will adopt the Monte Carlo Method or what we call a particle filter for the treatment of the GPS data and vision data. An experimental study of this system is performed on our fleet of experimental communicating vehicles LARA.

A sensitivity analysis was performed on the transit signal priority (TSP) requesting threshold to evaluate its impact on bus performance and general traffic. Two distinct bus routes were evaluated to determine the optimal requesting threshold that would balance the positive impacts on bus performance with the negative impacts on general traffic. Route 217, a conventional bus route, and the Utah Valley Express (UVX), a bus rapid transit line, utilize a dedicated short-range communication (DSRC)-based TSP system as part of their normal, day-to-day operations. Using field-generated data exclusively, bus performance and general traffic were evaluated over a 7-month period from February through August 2019. Bus performance was evaluated through on-time performance (OTP), schedule deviation, travel time, and dwell time, while the traffic analysis was performed by evaluating split failure, change in green time, and the frequency at which TSP was served. The requesting thresholds evaluated for Route 217 were 5-, 3-, 2-, and 0-minutes, which stipulate how far behind schedule the bus must be in order to request TSP. For UVX, 5-minutes and 2-minutes, as well as ON and OFF scenarios were evaluated; ON meant the buses were always requesting regardless of how late they were, while OFF meant that no requests were made and operations would be as if there were no TSP at all. A combination of observational and statistical analyses concluded with convincing evidence that OTP, schedule deviation, and travel time improve as the requesting threshold approaches zero with negligible impacts to general traffic. For Route 217, as the requesting threshold changed from 3, to 2, to 0 minutes, OTP increased 2.0 and 2.5 percent, respectively, mean schedule deviation improved 15.9 and 20.9 seconds, respectively, and travel time decreased at 72 percent of timepoints. Meanwhile, negative impacts to traffic occurred if an increase in split failure was measured after TSP was served, a phenomenon observed a maximum of once every 43 minutes. For UVX, as the requesting threshold changed from 5, to 2 minutes, to ON, OTP increased 7.6 and 4.7 percent, respectively, mean schedule deviation improved 24.3 and 15.0 seconds, respectively, and travel time decreased between 72 percent of timepoints. Thus, it is concluded that under the TSP system as implemented, bus performance improves as the requesting threshold approaches zero with inconsequential impacts to general traffic.

No description available.

Connected and automated vehicles (CVs and AVs, respectively) are rapidly emerging paradigms aiming to deploy and develop transportation systems that enable automated driving and data exchange among vehicles, infrastructure, and mobile devices to improve mobility, enhance safety, and reduce the adverse environmental impacts of transportation systems. Based on these premises, the focus of this research is to quantify the potential benefits of CVs and AVs to provide insight into how these technologies will impact road users and network performance. To assess the traffic operational performance of CVs, a connectivity-based modeling framework was developed based on traffic microsimulation for a real network in the city of Toronto. Then the effects of real-time routing guidance and advisory warning messages were studied for CVs. In addition, the impact of rerouting of non-connected vehicles (non-CVs) in response to various sources of information, such as mobile apps, GPS or VMS, was considered and evaluated. The results demonstrate the potential of such systems to improve mobility, enhance safety, and reduce greenhouse gas emissions (GHGs) at the network-wide level presented for different CVs market penetration. Additionally, the practical application of CVs in travel time estimation and its relationship with the number and location of roadside equipment (RSE) along freeways was investigated. A methodology was developed for determining the optimal number and location of roadside equipment (RSE) for reducing travel time estimation error in a connected vehicle environment. A simulation testbed that includes CVs was developed and implemented in the microsimulation model for Toronto 400-series highway network. The results reveal that the suggested methodology is capable of optimizing the number and location of RSEs in a connected vehicle environment. The optimization results indicate that the accuracy of travel time estimates is primarily dependent on the location of RSEs and less dependent on the total density of RSEs. In addition to CVs, the potential capacity increase of highways as a function of AVs market penetration was also studied and estimated. AVs are classified into Cooperative and Autonomous AVs. While Autonomous AVs rely only to their detection technology to sense their surroundings, Cooperative AVs, can also benefit from direct communication between vehicles and infrastructure. Cooperative car-following and lane-changing models were developed in a microsimulation model to enable AVs to maintain safe following and merging gaps. This study shows that cooperative AVs can adopt shorter gap than autonomous AVs and consequently, can significantly improve the lane capacity of highways. The achievable capacity increase for autonomous AVs appears highly insensitive to the market penetration, namely, the capacity remains within a narrow range of 2,046 to 2,238 vph irrespective of market penetration. The results of this research provide practitioners and decision-makers with knowledge regarding the potential capacity benefits of AVs with respect to market penetration and fleet conversion. / Thesis / Doctor of Philosophy (PhD)

Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the system’s effectiveness in a multi-intersection scenario.

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.