This thesis project presents a traffic micro simulation study that investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves simulations for a mixed traffic environment of conventional and automated vehicles both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is also accessed for its potential to improve traffic efficiency in a full market penetration of connected automated vehicles with enhanced capabilities such as having shorter time head ways.

This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future.  The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.

The connected vehicle technology presents an innovative way of sharing information between vehicles and the transportation infrastructure through wireless communications. The technology can potentially solve safety, mobility, and environmental challenges that face the transportation sector. Signal phasing and timing information is one category of information that can be broadcasted through connected vehicle technology. This thesis presents an in-depth study of possible ways signal phasing and timing information can be beneficial as far as safety and mobility are concerned. In total, three studies describing this research are outlined. The first study presented herein focuses on data collection and calibration efforts of the simulation model that was used for the next two studies. The study demonstrated a genetic algorithm procedure for calibrating VISSIM discharge headways based on queue discharge headways measured in the field. Video data was used to first compute intersection discharge headways for individual vehicle queue position and then to develop statistical distributions of discharge headways for each vehicle position. Except for the 4th vehicle position, which was best fitted by the generalized extreme value (GEV) distribution, the Log-logistic distribution was observed to be the best fit distribution for the rest of vehicle positions. Starting with the default values, the VISSIM parameters responsible for determining discharge headways were heuristically adjusted to produce optimal values. The optimal solutions were achieved by minimizing the Root Mean Square Error (RMSE) between the simulated and observed data. Through calibration, for each vehicle position, it was possible to obtain the simulated headways that reflect the means of the observed field headways. However, calibration was unable to replicate the dispersion of the headways observed in the field mainly due to VISSIM limitations. Based on the findings of this study, future work on calibration in VISSIM that would account for the dispersion of mixed traffic flow characteristics is warranted. The second study addresses the potential of connected vehicles in improving safety at the vicinity of signalized intersections. Although traffic signals are installed to reduce the overall number of collisions at intersections, rear-end collisions are increased due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposed and evaluated an Advanced Stop Assist System (ASAS) at signalized intersections by using Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes communication data, received from Roadside Unit (RSU), to provide drivers in approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking upon a yellow and red signal indication. A simulation test bed was modeled using VISSIM to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that at least 60% penetration rate is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. The last study addresses the fact that achieving smooth urban traffic flow requires reduction of excessive stop-and-go driving on urban arterials. Smooth traffic flow comes with several benefits including reduction of fuel consumption and emissions. Recently, more research efforts have been directed towards reduction of vehicle emissions. One such effort is the use of Green Light Optimal Speed Advisory (GLOSA) systems which use wireless communications to provide individual drivers with information on the approaching traffic signal phase and advisory speeds to arrive at the intersection on a green phase. Previously developed GLOSA algorithms do not address the impact of time to discharge queues formed at the intersection. Thus, this study investigated the influence of formed intersection queues on the performance of GLOSA systems. A simulation test-bed was modeled inside VISSIM to evaluate the algorithm’s effectiveness. Three simulation scenarios were designed; the baseline with no GLOSA in place, scenario 2 with GLOSA activated and queue discharge time not considered, and scenario 3 with GLOSA activated and where queue dissipation time was used to compute advisory speeds. At confidence level the results show a significant reduction in the time spent in queue when GLOSA is activated (scenarios 2 and 3). The change in the average number of stops along the corridor was found not to be significant when the base scenario was compared against scenario 2. However, a comparison between scenarios 2 and 3 demonstrates a significant reduction in the average number of stops along the corridor, and also in the time spent waiting in queues

<p>  </p> <p>Advancements in technology have propelled the availability of enriched and more frequent information about traffic conditions as well as the external factors that impact traffic such as weather, emergency response etc. Most newer vehicles are equipped with sensors that transmit their data back to the original equipment manufacturer (OEM) at near real-time fidelity. A growing number of such connected vehicles (CV) and the advent of third-party data collectors from various OEMs have made big data for traffic commercially available for use. Agencies maintaining and managing surface transportation are presented with opportunities to leverage such big data for efficiency gains. The focus of this dissertation is enhancing the use of CV data and applications derived from fusing it with other datasets to extract meaningful information that will aid agencies in data driven efficient decision making to improve network wide mobility and safety performance.   </p> <p>One of the primary concerns of CV data for agencies is data sampling, particularly during low-volume overnight hours. An evaluation of over 3 billion CV records in May 2022 in Indiana has shown an overall CV penetration rate of 6.3% on interstates and 5.3% on non-interstate roadways. Fusion of CV traffic speeds with precipitation intensity from NOAA’s High-Resolution Rapid-Refresh (HRRR) data over 42 unique rainy days has shown reduction in the average traffic speed by approximately 8.4% during conditions classified as very heavy rain compared to no rain. </p> <p>Both aggregate analysis and disaggregate analysis performed during this study enables agencies and automobile manufacturers to effectively answer the often-asked question of what rain intensity it takes to begin impacting traffic speeds. Proactive measures such as providing advance warnings that improve the situational awareness of motorists and enhance roadway safety should be considered during very heavy rain periods, wind events, and low daylight conditions.</p> <p>Scalable methodologies that can be used to systematically analyze hard braking and speed data were also developed. This study demonstrated both quantitatively and qualitatively how CV data provides an opportunity for near real-time assessment of work zone operations using metrics such as congestion, location-based speed profiles and hard braking. The availability of data across different states and ease of scalability makes the methodology implementable on a state or national basis for tracking any highway work zone with little to no infrastructure investment. These techniques can provide a nationwide opportunity in assessing the current guidelines and giving feedback in updating the design procedures to improve the consistency and safety of construction work zones on a national level.  </p> <p>CV data was also used to evaluate the impact of queue warning trucks sending digital alerts. Hard-braking events were found to decrease by approximately 80% when queue warning trucks were used to alert motorists of impending queues analyzed from 370 hours of queueing with queue trucks present and 58 hours of queueing without the queue trucks present, thus improving work zone safety. </p> <p>Emerging opportunities to identify and measure traffic shock waves and their forming or recovery speed anywhere across a roadway network are provided due to the ubiquity of the CV data providers. A methodology for identifying different shock waves was presented, and among the various case studies found typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were between 5.78 to 16.54 mph. The significance of this is illustrated with a case study of  a secondary crash that suggested  accelerating the clearance by 9 minutes could have prevented the secondary crash incident occurring at the back of the queue. Such capability of identifying and measuring shock wave speeds can be utilized by various stakeholders for traffic management decision-making that provide a holistic perspective on the importance of both on scene risk as well as the risk at the back of the queue. Near real-time estimation of shock waves using CV data can recommend travel time prediction models and serve as input variables to navigation systems to identify alternate route choice opportunities ahead of a driver’s time of arrival.   </p> <p>The overall contribution of this thesis is developing scalable methodologies and evaluation techniques to extract valuable information from CV data that aids agencies in operational decision making.</p>

Connected and autonomous vehicles are emerging technologies that have fostered the Intelligent Transportation System (ITS). ITS has the objective of optimizing traffic safety, mobility, and fuel consumption. To achieve this, a range of different services are provided that utilize communication in a vehicular network. One of these services that has received a lot of attention lately due to its ongoing standardization is the Maneuver Coordination Service (MCS). MCS has already shown great potential in the support of complex traffic areas, also called Transition Area (TA), where vehicles must cooperate to avoid Transition of Controls (ToCs). ITS-services often rely on communicated data; small errors, such as inaccessible or incorrect data, can cause the system to behave incorrectly. Signal interference (jamming) can cause communication interruptions, making vehicles unaware of each other. Incorrect data can be intentional due to data injection attacks, but also unintentional due to malfunctioning sensors, making vehicles incorrectly aware of each other. Incorrect behavior in systems such as ITS can lead to traffic congestion or even life-threatening collisions. This study focuses on MCS and examines traffic behavior when the service, in a generic traffic scenario, is subjected to jamming and falsification attacks with a variety of strategies (negative and positive speed, acceleration and position offset). We considered external attackers (not authenticated) that can disrupt communication, as well as internal attackers (authenticated) that are limited to tampering with outgoing data. Through severe collisions and travel time delays, the results show an impact on both safety and mobility. The results also show that different attacks with different impacts on the adversary can cause similar effects on the traffic, thus allowing the adversary to choose attacks based on the desired impact and its rationality, i.e. its willingness to be part of the impact. The study also proposes an extension to an already proposed Maneuver Coordination Protocol (MCP). We show that our extended MCP can be beneficial in avoiding dangerous maneuvers that could lead to collisions with cars in the blind spot. / Uppkopplade och autonoma fordon är framväxande teknologier som har främjat Intelligenta Transporteringssystem (ITS). ITS har som mål att optimera trafiksäkerhet, mobilitet och bränsleförbrukning. För att uppnå detta tillhandahålls en rad olika tjänster som utnyttjar kommunikation i ett fordonsnät. En av dessa tjänster som har fått mycket uppmärksamhet under den senaste tiden, tack vare sin pågående standardisering, är Manöverkoordinationtjänsten (MCS). MCS har redan visat stor potential för att stödja komplexa trafikområden, även kallade Övergångsområden (TA), där fordon måste samarbeta för att undvika kontrollövergångar (ToCs). ITS-tjänster förlitar sig ofta på kommunicerad data; små fel, som otillgängliga eller felaktiga data, kan göra att systemet beter sig felaktigt. Signalstörningar kan orsaka kommunikationsavbrott, vilket gör fordon omedvetna om varandra. Felaktig data kan vara avsiktliga på grund av datainjektionsattacker, men också oavsiktliga på grund av felaktiga sensorer, vilket gör fordon felaktigt medvetna om varandra. Felaktigt beteende i system som ITS kan leda till trafikstockningar eller till och med livshotande kollisioner. Denna studie fokuserar på MCS och undersöker trafikbeteendet när tjänsten, i ett generiskt trafikscenario, utsätts för signalstörningar och förfalskningsattacker med en mängd olika strategier (negativ och positiv hastighet, acceleration och positionsförskjutning). Vi tog hänsyn till externa angripare (ej autentiserade) som kan störa kommunikationen, såväl som interna angripare (autentiserade) som är begränsade till att manipulera utgående data. Genom allvarliga kollisioner och restidsförseningar visar resultaten en inverkan på både säkerhet och mobilitet. Resultaten visar också att olika attacker med olika inverkan på angriparen kan orsaka liknande effekter på trafiken, vilket gör att angriparen kan välja attacker baserat på den önskade effekten och rationaliteten, d.v.s. dens villighet att vara en del av påverkan. Studien föreslår också en utökning av en redan föreslagen MCP. Vi visar att vårt utökade MCP kan vara till nytta för att undvika farliga manövrar som kan leda till kollisioner med bilar i döda vinkeln.

Delning av data genom öppna data börjar bli allt mer vanligt i samhället. Samtidigt blir fordon på vägarna mer uppkopplade, tekniskt avancerade och samlar in mer data än tidigare. Öppna data i kombination med uppkopplade fordon i syfte att tillgängliggöra fordonsdata som öppna data har stor potential att bidra med värde till samhället. Denna studie identifierade och konkretiserade fem drivande och fem utmanande faktorer relaterade till potentialen för fordonsdata att utgöra öppna data. Studien applicerar en kvalitativ metod med en induktiv ansats. Sju informanter med varierande erfarenheter inom öppna data intervjuades genom semi-strukturerade intervjuer. Urvalet baserades främst på ett informationsurval. Studiens analys underbyggs av teoriområdena: databaserat värdeskapande, öppna dataekosystem och öppen innovation. Sammanfattningsvis är de drivande faktorerna som identifierats att data har ett högt värde, öppna fordonsdata kan bidra till förbättrade tjänster och infrastruktur, öppna fordonsdata främjar innovation, informationssäkerheten kan stärkas och slutligen att öppna dataekosystem främjar öppna data. De utmanande faktorerna är bristande interoperabilitet, komplicerade dataägarskapsförhållanden, att fordonsdata kan innehålla känsliga data och integritetsproblem, ekonomiska och strategiska hinder och till sist bristande kunskap och medvetenhet om öppna data. För att realisera värdet i fordonsdata som öppna data krävs det att fler aktörer delar öppna fordonsdata och bidrar till ett mer etablerat öppna dataekosystem. / Sharing data through open data is becoming increasingly common in society. Simultaneously, vehicles on the roads are becoming more connected, technologically advanced, and collect more data than before. Open data combined with connected vehicles, with the aim of making vehicle data available as open data, has great potential to contribute value to society. This study identified and concretized five driving and five challenging factors related to the potential for vehicle data to serve as open data. The study employs a qualitative method with an inductive approach. Seven interviewees with varying experiences in open data were interviewed through semi-structured interviews. The selection was mainly based on an information-oriented sampling. The study’s analysis is supported by the theoretical areas of data-based value creation, open data ecosystems, and open innovation. In summary, the identified driving factors are that data has high value, open vehicle data can contribute to improved services and infrastructure, open vehicle data promotes innovation, information security can be enhanced, and finally, open data ecosystems facilitate open data. The challenging factors are a lack of interoperability, complicated data ownership relationships, vehicle data potentially containing sensitive data and privacy issues, economic and strategic barriers, and finally, a lack of knowledge and awareness about open data. To realize the value of vehicle data as open data, more actors need to share open vehicle data and contribute to a more established open data ecosystem.

La localisation précise des véhicules et la sécurité des échanges sont deux grands axes qui font la fiabilité des services fournis dans les systèmes de transport intelligent. Ces dernières années, elles font l’objet de nombreux projets de recherche pour des champs d’application divers. Dans cette thèse, le contexte d’application est la réalisation d’un service de télépéage utilisant la technologie ITS-G5. Cette technologie de communication sans-fil permet dans un premier temps le partage des informations de sécurité routière entre les véhicules (V2V), le véhicule et l’infrastructure (V2I). Dans cette thèse, on propose une architecture permettant d’échanger des transactions de télépéage utilisant les équipements communicants en ITS-G5 embarqués dans les véhicules connectés et les unités bord de route (UBR) de l’infrastructure. Les problématiques de nos travaux de recherche se concentrent sur la méthode de localisation des véhicules ayant effectué la transaction afin de pouvoir la valider et sur la sécurité de l’architecture proposée pour assurer l’échange de cette transaction. Afin de bien localiser les véhicules lors du passage au péage, notre approche propose la compréhension de la cinématique du véhicule par une modélisation adéquate à partir des données recueillies dans les messages coopératifs (CAM : Cooperative Awareness Message) en approche du péage. Cela améliorera les informations de géolocalisation déjà présentes. Notre objectif est d’arriver à une précision de moins d’un mètre pour distinguer 2 véhicules adjacents. D’autre part, le protocole de sécurité proposé permet d’assurer l’authentification des équipements participant à l’échange et à la validation de la transaction, l’intégrité des données échangées ainsi que la confidentialité des échanges compte tenu du contexte de communication sans-fil et de la sensibilité des données échangées. Une preuve de concept de la solution de télépéage utilisant la technologie ITS-G5 est développée et intègre nos deux contributions. / The precise localization of vehicles and the security of communication are requirements that make almost of the services provided in intelligent transport systems (ITS) more reliable. In recent years, they have been the subject of numerous research projects for various fields of application. In this thesis, the context is the development of an electronic toll service using the ITS-G5 technology. This wireless communication technology initially allows the sharing of traffic safety information between vehicles (V2V), vehicle and infrastructure (V2I). In our work, we propose a tolling application using equipment operating in ITS-G5 embedded in the connected vehicles and roadside units. For this, ensuring both precise geolocation of the vehicles and security of communication are required to validate the transaction.In order to properly locate the vehicles during the toll crossing, our approach is based on the understanding of the kinematics of the vehicle through a suitable modeling from the data collected in the cooperative messages (called CAM: Cooperative Awareness Message). This approach aims to improve the geolocation information already present in the message. Our goal is to achieve vehicle localization with an accuracy lower than one meter to distinguish two adjacent vehicles. On the other hand, the proposed tolling protocol ensures the authentication of the equipment or entities involved in the exchange and the validation of the transaction, the integrity of the transmitted data as well as the confidentiality of the communication. In this way, we take into account the context of the wireless communication and the sensitivity of the exchanged data. Our two contributions are integrated in the implemented Proof of Concept of the tolling application using the ITS-G5 technology.