A RELIABILITY-BASED ROUTING PROTOCOL FOR VEHICULAR AD-HOC NETWORKS

Vehicular Ad hoc NETworks (VANETs), an emerging technology, would allow vehicles to form a self-organized network without the aid of a permanent infrastructure. As a prerequisite to communication in VANETs, an efficient route between communicating nodes in the network must be established, and the routing protocol must adapt to the rapidly changing topology of vehicles in motion. This is one of the goals of VANET routing protocols. In this thesis, we present an efficient routing protocol for VANETs, called the Reliable Inter-VEhicular Routing (RIVER) protocol. RIVER utilizes an undirected graph that represents the surrounding street layout where the vertices of the graph are points at which streets curve or intersect, and the graph edges represent the street segments between those vertices. Unlike existing protocols, RIVER performs real-time, active traffic monitoring and uses this data and other data gathered through passive mechanisms to assign a reliability rating to each street edge. The protocol then uses these reliability ratings to select the most reliable route. Control messages are used to identify a node’s neighbors, determine the reliability of street edges, and to share street edge reliability information with other nodes

Estimation and Control of Traffic Relying on Vehicular Connectivity

Vehicular traffic flow is essential, yet complicated to analyze. It describes the interplay among vehicles and with the infrastructure. A better understanding of traf-fic would benefit both individuals and the whole society in terms of improving safety, energy efficiency, and reducing environmental impacts. A large body of research ex-ists on estimation and control of vehicular traffic in which, however, vehicles were assumed not to be able to share information due to the limits of technology. With the development of wireless communication and various sensor devices, Connected Vehicles(CV) are emerging which are able to detect, access, and share information with each other and with the infrastructure in real time. Connected Vehicle Technology (CVT) has been attracting more and more attentions from different fields. The goal of this dissertation is to develop approaches to estimate and control vehicular traffic as well as individual vehicles relying on CVT. On one hand, CVT sig-nificantly enriches the data from individuals and the traffic, which contributes to the accuracy of traffic estimation algorithms. On the other hand, CVT enables commu-nication and information sharing between vehicles and infrastructure, and therefore allows vehicles to achieve better control and/or coordination among themselves and with smart infrastructure. The first part of this dissertation focused on estimation of traffic on freeways and city streets. We use data available from on road sensors and also from probe One of the most important traffic performance measures is travel time. How-ever it is affected by various factors, and freeways and arterials have different travel time characteristics. In this dissertation we first propose a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribu-tion for travel time. The probability distribution is generated using a Monte Carlo simulation and an Online Expectation Maximization clustering algorithm. Results show that the approach is able to generate a reasonable multimodal distribution for travel-time. For arterials, this dissertation presents methods for estimating statistics of travel time by utilizing sparse vehicular probe data. A public data feed from transit buses in the City of San Francisco is used. We divide each link into shorter segments, and propose iterative methods for allocating travel time statistics to each segment. Inspired by K-mean and Expectation Maximization (EM) algorithms, we iteratively update the mean and variance of travel time for each segment based on historical probe data until convergence. Based on segment travel time statistics, we then pro-pose a method to estimate the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. The results are compared to high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach. The second part of this dissertation emphasize on control approaches enabled by vehicular connectivity. Estimation and prediction of surrounding vehicle behaviors and upcoming traffic makes it possible to improve driving performance. We first propose a Speed Advisory System for arterial roads, which utilizes upcoming traffi

Crowdsourcing traffic data for travel time estimation

Travel time estimation is a fundamental measure used in routing and navigation applications, in particular in emerging intelligent transportation systems (ITS). For example, many users may prefer the fastest route to their destination and would rely on real-time predicted travel times. It also helps real-time traffic management and traffic light control. Accurate estimation of travel time requires collecting a lot of real-time data from road networks. This data can be collected using a wide variety of sources like inductive loop detectors, video cameras, radio frequency identification (RFID) transponders etc. But these systems include deployment of infrastructure which has some limitations and drawbacks. The main drawbacks in these modes are the high cost and the high probability of error caused by prevalence of equipment malfunctions and in the case of sensor based methods, the problem of spatial coverage.;As an alternative to traditional way of collecting data using expensive equipment, development of cellular & mobile technology allows for leveraging embedded GPS sensors in smartphones carried by millions of road users. Crowd-sourcing GPS data will allow building traffic monitoring systems that utilize this opportunity for the purpose of accurate and real-time prediction of traffic measures. However, the effectiveness of these systems have not yet been proven or shown in real applications. In this thesis, we study some of the current available data sets and identify the requirements for accurate prediction. In our work, we propose the design for a crowd-sourcing traffic application, including an android-based mobile client and a server architecture. We also develop map-matching method. More importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key role in estimation in the last two decades. They are proved by providing better accuracy in estimation and in classification. However, employing a machine learning technique in any application requires creative modeling of the system and its sensory data. In this thesis, we model the road network as a graph and train different models for different links on the road. Modeling a road network as graph with nodes and links enables the learner to capture patterns occurring on each segment of road, thereby providing better accuracy. To evaluate the prediction models, we use three sets of data out of which two sets are collected using mobile probing and one set is generated using VISSIM traffic simulator. The results show that crowdsourcing is only more accurate than traditional statistical methods if the input values for input data are very close to the actual values. In particular, when speed of vehicles on a link are concerned, we need to provide the machine learning model with data that is only few minutes old; using average speed of vehicles, for example from the past half hour, as is usually seen in many web based traffic information sources may not allow for better performance

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Contribution to design a communication framework for vehicular ad hoc networks in urban scenarios

The constant mobility of people, the growing need to be always connected, the large number of vehicles that nowadays can be found in the roads and the advances in technology make Vehicular Ad hoc Networks (VANETs) be a major area of research. Vehicular Ad hoc Networks are a special type of wireless Mobile Ad hoc Networks (MANETs), which allow a group of mobile nodes configure a temporary network and maintain it without the need of a fixed infrastructure. A vehicular network presents some specific characteristics, as the very high speed of nodes. Due to this high speed the topology changes are frequent and the communication links may last only a few seconds. Smart cities are now a reality and have a direct relationship with vehicular networks. With the help of existing infrastructure such as traffic lights, we propose a scheme to update and analyse traffic density and a warning system to spread alert messages. With this, traffic lights assist vehicular networks to take proper decisions. This would ensure less congested streets. It would also be possible that the routing protocol forwards data packets to vehicles on streets with enough neighbours to increase the possibility of delivering the packets to destination. Sharing updated, reliable and real-time information, about traffic conditions, weather or security alerts, increases the need of algorithms for the dissemination of information that take into account the main beneffits and constraints of these networks. For all this, routing protocols for vehicular networks have the difficult task to select and establish transmission links to send the data packets from source to destination through multiple nodes using intermediate vehicles efficiently. The main objective of this thesis is to provide improvements in the communication framework for vehicular networks to improve decisions to select next hops in the moment to send information, in this way improving the exchange of information to provide suitable communication to minimize accidents, reduce congestion, optimize resources for emergencies, etc. Also, we include intelligence to vehicles at the moment to take routing decisions. Making them map-aware, being conscious of the presence of buildings and other obstacles in urban environments. Furthermore, our proposal considers the decision to store packets for a maximum time until finding other neighbouring nodes to forward the packets before discarding them. For this, we propose a protocol that considers multiple metrics that we call MMMR (A Multimetric, Map-Aware Routing Protocol ). MMMR is a protocol based on geographical knowledge of the environment and vehicle location. The metrics considered are the distance, the density of vehicles in transmission range, the available bandwidth and the future trajectory of the neighbouring nodes. This allows us to have a complete view of the vehicular scenario to anticipate the driver about possible changes that may occur. Thus, a node can select a node among all its neighbours, which is the best option to increase the likelihood of successful packet delivery, minimizing time and offering a level of quality and service. In the same way, being aware of the increase of information in wireless environments, we analyse the possibility of offering anonymity services. We include a mechanism of anonymity in routing protocols based on the Crowd algorithm, which uses the idea of hiding the original source of a packet. This allowed us to add some level of anonymity on VANET routing protocols. The analytical modeling of the available bandwidth between nodes in a VANET, the use of city infrastructure in a smart way, the forwarding selection in data routing byvehicles and the provision of anonymity in communications, are issues that have been addressed in this PhD thesis. In our research work we provide contributions to improve the communication framework for Vehicular Ad hoc Networks obtaining benefits toenhance the everyday of the population.La movilidad constante de las personas y la creciente necesidad de estar conectados en todo momento ha hecho de las redes vehiculares un área cuyo interés ha ido en aumento. La gran cantidad de vehículos que hay en la actualidad, y los avances tecnológicos han hecho de las redes vehiculares (VANETS, Vehicular Ad hoc Networks) un gran campo de investigación. Las redes vehiculares son un tipo especial de redes móviles ad hoc inalámbricas, las cuales, al igual que las redes MANET (Mobile Ad hoc Networks), permiten a un grupo de nodos móviles tanto configurar como mantener una red temporal por si mismos sin la necesidad de una infraestructura fija. Las redes vehiculares presentan algunas características muy representativas, por ejemplo, la alta velocidad que pueden alcanzar los nodos, en este caso vehículos. Debido a esta alta velocidad la topología cambia frecuentemente y la duración de los enlaces de comunicación puede ser de unos pocos segundos. Estas redes tienen una amplia área de aplicación, pudiendo tener comunicación entre los mismos nodos (V2V) o entre los vehículos y una infraestructura fija (V2I). Uno de los principales desafíos existentes en las VANET es la seguridad vial donde el gobierno y fabricantes de automóviles han centrado principalmente sus esfuerzos. Gracias a la rápida evolución de las tecnologías de comunicación inalámbrica los investigadores han logrado introducir las redes vehiculares dentro de las comunicaciones diarias permitiendo una amplia variedad de servicios para ofrecer. Las ciudades inteligentes son ahora una realidad y tienen una relación directa con las redes vehiculares. Con la ayuda de la infraestructura existente, como semáforos, se propone un sistema de análisis de densidad de tráfico y mensajes de alerta. Con esto, los semáforos ayudan a la red vehicular en la toma de decisiones. Así se logrará disponer de calles menos congestionadas para hacer una circulación más fluida (lo cual disminuye la contaminación). Además, sería posible que el protocolo de encaminamiento de datos elija vehículos en calles con suficientes vecinos para incrementar la posibilidad de entregar los paquetes al destino (minimizando pérdidas de información). El compartir información actualizada, confiable y en tiempo real sobre el estado del tráfico, clima o alertas de seguridad, aumenta la necesidad de algoritmos de difusión de la información que consideren los principales beneficios y restricciones de estas redes. Así mismo, considerar servicios críticos que necesiten un nivel de calidad y servicio es otro desafío importante. Por todo esto, un protocolo de encaminamiento para este tipo de redes tiene la difícil tarea de seleccionar y establecer enlaces de transmisión para enviar los datos desde el origen hacia el destino vía múltiples nodos utilizando vehículos intermedios de una manera eficiente. El principal objetivo de esta tesis es ofrecer mejoras en los sistemas de comunicación vehicular que mejoren la toma de decisiones en el momento de realizar el envío de la información, con lo cual se mejora el intercambio de información para poder ofrecer comunicación oportuna que minimice accidentes, reduzca atascos, optimice los recursos destinados a emergencias, etc. Así mismo, incluimos más inteligencia a los coches en el momento de tomar decisiones de encaminamiento de paquetes. Haciéndolos conscientes de la presencia de edificios y otros obstáculos en los entornos urbanos. Así como tomar la decisión de guardar paquetes durante un tiempo máximo de modo que se encuentre otros nodos vecinos para encaminar paquetes de información antes de descartarlo. Para esto, proponemos un protocolo basado en múltiples métricas (MMMR, A Multimetric, Map-aware Routing Protocol ) que es un protocolo geográfio basado en el conocimiento del entorno y localización de los vehículos. Las métricas consideradas son la distancia, la densidad de vehículos en el área de transmisión, el ancho de banda disponible y la trayectoria futura de los nodos vecinos. Esto nos permite tener una visión completa del escenario vehicular y anticiparnos a los posibles cambios que puedan suceder. Así, un nodo podrá seleccionar aquel nodo entre todos sus vecinos posibles que sea la mejor opción para incrementar la posibilidad de entrega exitosa de paquetes, minimizando tiempos y ofreciendo un cierto nivel de calidad y servicio. De la misma manera, conscientes del incremento de información que circula por medios inalámbricos, se analizó la posibilidad de servicios de anonimato. Incluimos pues un mecanismo de anonimato en protocolos de encaminamiento basado en el algoritmo Crowd, que se basa en la idea de ocultar la fuente original de un paquete. Esto nos permitió añadir cierto nivel de anonimato que pueden ofrecer los protocolos de encaminamiento. El modelado analítico del ancho de banda disponible entre nodos de una VANET, el uso de la infraestructura de la ciudad de una manera inteligente, la adecuada toma de decisiones de encaminamiento de datos por parte de los vehículos y la disposición de anonimato en las comunicaciones, son problemas que han sido abordados en este trabajo de tesis doctoral que ofrece contribuciones a la mejora de las comunicaciones en redes vehiculares en entornos urbanos aportando beneficios en el desarrollo de la vida diaria de la población

Enhancing Energy Efficiency in Connected Vehicles Via Access to Traffic Signal Information

This dissertation expounds on algorithms that can deterministically or proba-bilistically predict the future Signal Phase and Timing (SPAT) of a traffic signal by relying on real-time information from numerous vehicles and traffic infrastructure, historical data, and the computational power of a back-end computing cluster. When made available on an open server, predictive information about traffic signals’ states can be extremely valuable in enabling new fuel efficiency and safety functionalities in connected vehicles: Predictive Cruise Control (PCC) can use the predicted timing plan to calculate globally optimal velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and reduce emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance is another functionality that can benefit from the prediction. We start by exploring a globally optimal velocity planning algorithm through the use of Dynamic Programming (DP), and provide to it three levels of traffic signal information - none, real-time only, and full-future information. The no-information case represents the average driver today, and is expected to provide an energy efficiency minimum or baseline. The full-information case represents a driver with full and exact knowledge of the future red and green times of all the traffic signals along their route, and is expected to provide an energy efficiency maximum. We propose a probabilistic method that seeks to optimize fuel efficiency when only real-time only information is available with the goal of obtaining fuel efficiency as close to the full-future knowledge example as possible. We used Monte-Carlo simulations to evaluate whether the fuel efficiency gains found were merely the result of lucky case studies or whether they were statistically significant; we found in related case studies that up to 16% gains in fuel economy were possible. While these results were promising, the delivery of relevant and accurate future traffic signal phase and timing information remained an unsolved problem. The next step we took was towards building The next step we took was towards building traffic signal prediction models. We took several prescient techniques from the data mining and machine learning fields, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. signal prediction models. We took several prescient techniques from the data mining and machine learning fields, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. Finally, we evaluated the influence of providing SPaT data to vehicles. To that end, we investigated both smartphone and in-vehicle proof-of-concepts. An in-vehicle velocity recommendation application has been tested in two cities: San Jose, California and San Francisco, California. The two test locations used two different data sources: data directly from a TMC, and data crowdsourced from public transit bus routes, respectively. A total of 14 test drivers were used to evaluate the effectiveness of the algorithm. In San Jose, the algorithm was found to produce a 8.4% improvement in fuel economy. In San Francisco, traffic conditions were not conducive to testing as the driver was unable to significantly vary his speed to follow the recommendation algorithm, and a negligible difference in fuel economy was observed. However, it did provide an opportunity to evaluate the quality of data coming from the crowdsourced data algorithms. Predicted phase timing com-pared to camera-recorded ground truth data indicated an RMS difference (error) in prediction of approximately 4.1 seconds

Connected Vehicles at Signalized Intersections: Traffic Signal Timing Estimation and Optimization

Summary: While traffic signals ensure safety of conflicting movements at intersections, they also cause much delay, wasted fuel, and tailpipe emissions. Frequent stops and goes induced by a series of traffic lights often frustrates passengers. However, the connectivity provided by connected vehicles applications can improve this situation. A uni-directional traffic signal to vehicle communication can be used to guide the connected vehicles to arrive at green which increases their energy efficiency; and in the first part of the dissertation, we propose a traffic signal phase and timing estimator as a complementary solution in situations where timing information is not available directly from traffic signals or a city’s Traffic Management Center. Another approach for improving the intersection flow is optimizing the timing of traditional traffic signals informed by uni-directional communication from connected vehicles. Nevertheless, one can expect further increase in energy efficiency and intersection flow with bi-directional vehicle-signal communication where signals adjust their timings and vehicles their speeds. Autonomous vehicles can further benefit from traffic signal information because they not only process the incoming information rather effortlessly but also can precisely control their speed and arrival time at a green light. The situation can get even better with 100%penetration of autonomous vehicles since a physical traffic light is not needed anymore. However, the optimal scheduling of the autonomous vehicle arrivals at such intersections remains an open problem. The second part of the dissertation attempts to address the scheduling problem formulation and to show its benefits in microsimulation as well as experiments. Intellectual Merit: In the first part of this research, we study the statistical patterns hidden in the connected vehicle historical data stream in order to estimate a signal’s phase and timing (SPaT). The estimated SPaT data communicated in real-time to connected vehicles can help drivers plan over time the best vehicle velocity profile and route of travel. We use low-frequency probe data streams to show what the minimum achievable is in estimating SPaT. We use a public feed of bus location and velocity data in the city of San Francisco as an example data source. We show it is possible to estimate, fairly accurately, cycle times and duration of reds for pre-timed traffic lights traversed by buses using a few days worth of aggregated bus data. Furthermore, we also estimate the start of greens in real-time by monitoring movement of buses across intersections. The results are encouraging, given that each bus sends an update only sporadically (≈ every 200 meters) and that bus passages are infrequent (every 5-10 minutes). The accuracy of the SPaT estimations are ensured even in presence of queues; this is achieved by extending our algorithms to include the influence of queue delay. A connected vehicle test bed is implemented in collaboration with industry. Our estimated SPaT information is communicated uni-directionally to a connected test vehicle for those traffic signals which are not connected. In the second part of the dissertation, another test bed, but with bi-directional communication capability, is implemented to transfer the connected vehicle data to an intelligent intersection controller through cellular network. We propose a novel intersection control scheme at the cyber layer to encourage platoon formation and facilitate uninterrupted intersection passage. The proposed algorithm is presented for an all autonomous vehicle environment at an intersection with no traffic lights. Our three key contributions are in communica-tion, control, and experimental evaluation: i) a scalable mechanism allowing a large number of vehicles to subscribe to the intersection controller, ii) reducing the vehicle-intersection coordination problem to a Mixed Integer Linear Program (MILP), and iii) a Vehicle-in-the-Loop (VIL) test bed with a real vehicle interacting with the intersection control cyber-layer and with our customized microsimulations in a virtual road network environment. The proposed MILP-based controller receives information such as location and speed from each subscribing vehicle and advises vehicles of the optimal time to access the intersection. The access times are computed by periodically solving a MILP with the objective of minimizing intersection delay, while ensuring intersection safety and considering each vehicle’s desired velocity. In order to estimate the fuel consumption reduction potential of the implemented system, a new method is proposed for estimating fuel consumption using the basic engine diagnostic information of the vehicle-in-the-loop car. Broader Impacts: This research can transform not only the way we drive our vehicles at signalized intersec-tions but also the way intersections are managed. As we evaluated in a connected test vehicle in the first part of the dissertation, our SPaT estimations in conjunction with the SPaT information available directly from Traffic Management Centers, enables the drivers to plan over time the best vehicle velocity profile to reduce idling at red lights. Other fuel efficiency and safety functionalities in connected vehicles can also benefit from such information about traffic signals’ phase and timing. For example, advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red, and intersection collision avoidance and active safety systems could foresee potential signal violations at signalized intersections. In addition, as shown in the second part of the dissertation, when a connected traffic signal or intersection con-troller is available, intelligent control methods can plan in real-time the best timings and the lengths of signal phases in response to prevailing traffic conditions with the use of connected vehicle data. Our MILP-based intersection control is proposed for an all autonomous driving environment; and right now, it can be utilized in smart city projects where only autonomous vehicles are allowed to travel. This is expected to transform driving experience in the sense that our linear formulations minimizes the intersection delay and number of stops significantly compared to pre-timed intersections