Road crashes, occurring at a high annual rate for many years, demand improvements in transportation systems to provide a high level of on-road safety. Implanting smart sensors, communication capabilities, memory storage and information processing units in vehicles are important components of Intelligent Transportation Systems (ITS). ITS should enable the communication between vehicles and allow cooperative driving and early warnings of sudden breaks and accidents ahead. The prompt availability of the emergency information will provide the driver a time to react in order to avoid possible accidents ahead. Hence, information delivery delay is an importance quality-of-service (QoS) metric in such applications. In this thesis, we focus on modeling the delay for emergency messaging in vehicular ad hoc networks (VANETs). VANETs consist of nodes moving with very high speeds, resulting in frequent topological changes. As a result, many existing models and packet forwarding schemes designed for general purpose mobile ad hoc networks (MANETs) cannot be directly applied to VANETs. In our system model, we consider mobility and traffic density of vehicles. We focus on studying the effect of the traffic flow density on the delay of emergency message dissemination. Hence, traffic flow theories developed by civil engineers form the base of our modeling. The common way of emergency message dissemination in VANETs is broadcasting. To overcome the broadcasting storm problem and improve scalability of such large networks, we adopt a node cluster based broadcasting mechanism. This research provides a realistic mathematical model for the broadcasting delay, which accounts for the randomness in user mobility and matches the highly dynamic nature of VANETs. An investigation on the minimum cluster size that achieves acceptable message delivery latency is provided. It is shown that network control and performance parameters are dependent on the traffic density. Experimental measurement data are used to demonstrate the accuracy of the mathematical modeling.

Road crashes, occurring at a high annual rate for many years, demand improvements in transportation systems to provide a high level of on-road safety. Implanting smart sensors, communication capabilities, memory storage and information processing units in vehicles are important components of Intelligent Transportation Systems (ITS). ITS should enable the communication between vehicles and allow cooperative driving and early warnings of sudden breaks and accidents ahead. The prompt availability of the emergency information will provide the driver a time to react in order to avoid possible accidents ahead. Hence, information delivery delay is an importance quality-of-service (QoS) metric in such applications. In this thesis, we focus on modeling the delay for emergency messaging in vehicular ad hoc networks (VANETs). VANETs consist of nodes moving with very high speeds, resulting in frequent topological changes. As a result, many existing models and packet forwarding schemes designed for general purpose mobile ad hoc networks (MANETs) cannot be directly applied to VANETs. In our system model, we consider mobility and traffic density of vehicles. We focus on studying the effect of the traffic flow density on the delay of emergency message dissemination. Hence, traffic flow theories developed by civil engineers form the base of our modeling. The common way of emergency message dissemination in VANETs is broadcasting. To overcome the broadcasting storm problem and improve scalability of such large networks, we adopt a node cluster based broadcasting mechanism. This research provides a realistic mathematical model for the broadcasting delay, which accounts for the randomness in user mobility and matches the highly dynamic nature of VANETs. An investigation on the minimum cluster size that achieves acceptable message delivery latency is provided. It is shown that network control and performance parameters are dependent on the traffic density. Experimental measurement data are used to demonstrate the accuracy of the mathematical modeling.