There are still many challenges in the field of VANETs that encouraged researchers to conduct further investigation in this field to meet these challenges. The issue pertaining to routing protocols such as delivering the warning messages to the vehicles facing Non-Line of Sight (NLOS) situations without causing the storm problem and channel contention, is regarded as a serious dilemma which is required to be tackled in VANET, especially in congested environments. This requires the designing of an efficient mechanism of routing protocol that can broadcast the warning messages from the emergency vehicles to the vehicles under NLOS, reducing the overhead and increasing the packet delivery ratio with a reduced time delay and channel utilisation. The main aim of this work is to develop the novel routing protocol for a high-density environment in VANET through utilisation of its high mobility features, aid of the sensors such as Global Positioning System (GPS) and Navigation System (NS). In this work, the cooperative approach has been used to develop the routing protocol called the Co-operative Volunteer Protocol (CVP), which uses volunteer vehicles to disseminate the warning message from the source to the target vehicle under NLOS issue; this also increases the packet delivery ratio, detection of NLOS and resolution of NLOS by delivering the warning message successfully to the vehicle under NLOS, thereby causing a direct impact on the reduction of collisions between vehicles in normal mode and emergency mode on the road near intersections or on highways. The cooperative approach adopted for warning message dissemination reduced the rebroadcast rate of messages, thereby decreasing significantly the storm issue and the channel contention. A novel architecture has been developed by utilising the concept of a Context-Aware System (CAS), which clarifies the OBU components and their interaction with each other in order to collect data and take the decisions based on the sensed circumstances. The proposed architecture has been divided into three main phases: sensing, processing and acting. The results obtained from the validation of the proposed CVP protocol using the simulator EstiNet under specific conditions and parameters showed that performance of the proposed protocol is better than that of the GRANT protocol with regard to several metrics such as packet delivery ratio, neighbourhood awareness, channel utilisation, overhead and latency. It is also successfully shown that the proposed CVP could detect the NLOS situation and solves it effectively and efficiently for both the intersection scenario in urban areas and the highway scenario.

Recent years have shown a growing concern over increasing traffic volume worldwide. The insufficient road capacity and the resulting congestions have become major problems in many urban areas. Congestions negatively impact the economy, the environment and the health of the population as well as the drivers satisfaction. Current solutions to this topical and timely problem rely on the exploitation of Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management involves the collection and processing of a large amount of geographically distributed information to control distributed infrastructure and individual vehicles. The distributed nature of the problem prompted the development of a novel, scalable ITS-Cloud platform. The ITS-Cloud organises the processing and manages distributed data sources to provide traffic management methods with more accurate information about the state of the traffic. A new approach to service allocation, derived from the existing cloud and grid computing approaches, was created to address the unique needs of ITS traffic management. The ITS-Cloud hosts the collection of software services that form the Cloud based Traffic Management System (CTMS). CTMS combines intersection control algorithms with intersection approach advices to the vehicles and dynamic routing. The CTMS contains a novel Two-Step traffic management method that relies on the ITS-Cloud to deliver a detailed traffic simulation image and integrates an adaptive intersection control algorithm with a microscopic prediction mechanism. It is the first method able to perform simultaneous adaptive intersection control and intersection approach optimization. The Two-Step method builds on a novel pressure based adaptive intersection control algorithm as well as two new traffic prediction schemes. The developed traffic management system was evaluated using a new microscopic traffic simulation tool tightly integrated with the ITS-Cloud. The novel traffic management approaches were shown to outperform benchmark methods for a realistic range of traffic conditions and road network configurations. Unique to the work was the investigation of interactions between ITS components.

Changing speed limit leads to proportional changes in average speeds which may affect the number of traffic accident occurrences. It is however critical and challenging to evaluate the impact of a speed limit alteration on the number and severity of accidents due primarily to the unavailability of adequate data and the inherent limitations of existing approaches. Although speed is regarded as one of the main contributory factors in traffic accident occurrences, research findings are inconsistent. Independent of the robustness of their statistical approaches, accident frequency models typically use accident grouping concepts based on spatial criteria (e.g. accident counts by link termed as a link-based approach). In the link-based approach, the variability of accidents is explained by highly aggregated average measures of explanatory variables that may be inappropriate, especially for time-varying variables such as speed and volume. This thesis re-examines accident-speed relationships by developing a new accident data aggregation method that enables improved representation of the road conditions just before accident occurrences in order to evaluate the impact of a potential speed limit increase on the UK motorways (e.g. from 70 mph to 80 mph). In this work, accidents are aggregated according to the similarity of their pre-accident traffic and geometric conditions, forming an alternative accident count dataset termed as the condition-based approach. Accident-speed relationships are separately developed and compared for both approaches (i.e. link-based and condition-based) by employing the reported annual accidents that occurred on the Strategic Road Network of England in 2012 along with traffic and geometric variables. Accident locations were refined using a fuzzy-logic-based algorithm designed for the study area with 98.9% estimated accuracy. The datasets were modelled by injury severity (i.e. fatal and serious or slight) and by number of vehicles involved (i.e. single-vehicle and multiple-vehicle) using the multivariate Poisson lognormal regression, with spatial effects for the link-based model under a full Bayesian inference method. The results of the condition-based models imply that single-vehicle accidents of all severities and multiple-vehicle accidents with fatal or serious injuries increase at higher speed conditions, particularly when these are combined with lower volumes. Multiple-vehicle slight injury accidents were not found to be related with higher speeds, but instead with congested traffic. The outcomes of the link-based model were almost the opposite; suggesting that the speed-accident relationship is negative. The differences between the results reveal that data aggregation may be crucial, yet so far overlooked in the methodological aspect of accident data analyses. By employing the speed elasticity of motorway accidents that was derived from the calibrated condition-based models it has been found that a 10 mph increase in UK motorway speed limit (i.e. from 70 mph to 80 mph) would result in a 6-12% increase in fatal and serious injury accidents and 1-3% increase in slight injury accidents.

In both developed and emerging-economies, major cities continue to experience increasing traffic congestion. To address this issue, complex Traffic Management Systems (TMS) are employed in recent years to help manage traffic. These systems fuse traffic-surveillance-related information from a variety of sensors deployed across traffic networks. A TMS requires real-time information to make effective control decisions and to deliver trustworthy information to users, such as travel time, congestion level, etc. There are three fundamental inputs required by TMS, namely, traffic volume, vehicular speed, and traffic density. Using conventional traffic loop detectors one can directly measure flow and velocity. However, traffic density is more difficult to measure. The situation becomes more difficult for multi-lane motorways due to drivers lane-change behaviour. This research investigates statistical modelling and analysis of traffic flow. It contributes to the literature of transportation and traffic management and research in several aspects. First, it takes into account lane-changes in traffic modelling through incorporating a Markov chain model to describe the drivers lane-change behaviour. Secondly, the lane change probabilities between two adjacent lanes are not assumed to be fixed but rather they depend on the current traffic condition. A discrete choice model is used to capture drivers lane choice behaviour. The drivers choice probabilities are modelled by several traffic-condition related attributes such as vehicle time headway, traffic density and speed. This results in a highly nonlinear state equation for traffic density. To address the issue of high nonlinearity of the state space model, the EKF and UKF is used to estimate the traffic density recursively. In addition, a new transformation approach has been proposed to transform the observation equation from a nonlinear form to a linear one so that the potential approximation in the EKF & UKF can be avoided. Numerical studies have been conducted to investigate the performance of the developed method. The proposed method outperformed the existing methods for traffic density estimation in simulation studies. Furthermore, it is shown that the computational cost for updating the estimate of traffic densities for a multi-lane motorway is kept at a minimum so that online applications are feasible in practice. Consequently the traffic densities can be monitored and the relevant information can be fed into the traffic management system of interest.