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Numerous local incidents occur on road networks daily many of which may lead to congestion and safety hazards. If vehicles can be provided with information about such incidents or traffic conditions in advance, the quality of driving in terms of time, distance, and safety can be improved significantly. Vehicular Ad Hoc Networks (VANETs) have recently emerged as an effective tool for improving road safety through the propagation of warning messages among the vehicles in the network about potential obstacles on the road ahead. This research has presented an effective warning data dissemination scheme which deploys relay strategy and concept of Region of Interest (RoI). A warning data message is characterized as spatio-temporal, implying that both the location and the time of an incident must be considered. Factors such as the type of warning message, the layout of the road network, the traffic density and the capacity of alternative roads are influential in determining the RoI in which the warning message needs to be propagated. In the developed scheme, the type of warning message is taken into account for the determination of the RoI so that the more severe the incident, the wider the RoI. In the selection of the relay point, the border relay area in which the relay point is placed, is adapted to the traffic density so that the higher the traffic density , the narrower the relay area. Traffic statistics are used to calculate the RoI, which is then enclosed in the warning message so that the message is not retransmitted beyond the RoI. Also, the responsibility for retransmitting the message is assigned to the relay node. The data is then disseminated effectively so that vehicles in areas unrelated to the incident are not informed. The primary objective of this research is to provide better understanding of the dissemination of warning data in the context of a vehicular network with the ultimate goal of increasing the possibility of using VANETs for safety applications.

Numerous local incidents occur on road networks daily many of which may lead to congestion and safety hazards. If vehicles can be provided with information about such incidents or traffic conditions in advance, the quality of driving in terms of time, distance, and safety can be improved significantly. Vehicular Ad Hoc Networks (VANETs) have recently emerged as an effective tool for improving road safety through the propagation of warning messages among the vehicles in the network about potential obstacles on the road ahead. This research has presented an effective warning data dissemination scheme which deploys relay strategy and concept of Region of Interest (RoI). A warning data message is characterized as spatio-temporal, implying that both the location and the time of an incident must be considered. Factors such as the type of warning message, the layout of the road network, the traffic density and the capacity of alternative roads are influential in determining the RoI in which the warning message needs to be propagated. In the developed scheme, the type of warning message is taken into account for the determination of the RoI so that the more severe the incident, the wider the RoI. In the selection of the relay point, the border relay area in which the relay point is placed, is adapted to the traffic density so that the higher the traffic density , the narrower the relay area. Traffic statistics are used to calculate the RoI, which is then enclosed in the warning message so that the message is not retransmitted beyond the RoI. Also, the responsibility for retransmitting the message is assigned to the relay node. The data is then disseminated effectively so that vehicles in areas unrelated to the incident are not informed. The primary objective of this research is to provide better understanding of the dissemination of warning data in the context of a vehicular network with the ultimate goal of increasing the possibility of using VANETs for safety applications.

As important components of intelligent transportation system, lane detection and tracking (LDT) and lane departure warning (LDW) systems have attracted great interest from the computer vision community over the past few years. Conversely, lane markings recognition (LMR) systems received surprisingly little attention. This thesis proposed a real-time lane assisting framework for intelligent vehicles, which consists of a comprehensive module and simplified module. To the best of our knowledge, this is the first parallel architecture that considers not only lane detection and tracking, but also lane marking recognition and departure warning. A lightweight version of the Hough transform, PPHT is used for both modules to detect lines. After detection stage, for the comprehensive module, a novel refinement scheme consisting of angle threshold and segment linking (ATSL) and trapezoidal refinement method (TRM) takes shape and texture information into account, which significantly improves the LDT performance. Also based on TRM, colour and edge informations are used to recognize lane marking colors (white and yellow) and shapes (solid and dashed). In the simplified module, refined MSER blobs dramatically simplifies the preprocessing and refinement stage, and enables the simplified module performs well on lane detection and tracking. Several experiments are conducted in highway and urban roads in Ottawa. The detection rate of the LDT system in comprehensive module average 95.9% and exceed 89.3% in poor conditions, while the recognition rate depends on the quality of lane paint and achieves an average accuracy of 93.1%. The simplified module has an average detection rate of 92.7% and exceeds 84.9% in poor conditions. Except the conventional experimental methods, a novel point cluster evaluation and pdf analysis method have been proposed to evaluate the performance of LDT systems, in terms of the stability, accuracy and similarity to Gaussian distribution.

Recently, according to people's requirements for safe and congestion-free driving in the public transportation system, the intelligent transportation system (ITS) has been widely concerned. To achieve a safe and time-saving driving experience in ITS, various data sharing methods are proposed to provide traffic information for drivers to perceive their surrounding driving environment. However, the high dynamic characteristic of the vehicular network (VNET) results in a challenging environment for establishing stable communication among vehicles. To face this challenge, a Cellular network-assisted Reliable Traffic-Aware Routing protocol (CRTAR) is proposed in this thesis to provide support for vehicle’s data routing process in a heterogeneous vehicular-cellular network environment. In the method, city-wide traffic information, i.e., traffic density and data transmission density of the road segments, is introduced into vehicle's data routing process to assist the vehicle in selecting the optimal data transmission route to deliver data packets. To further improve the stability of inter-vehicle communication, the link lifetime between vehicles is also considered to select the next forwarder that can establish relatively robust communication. CRTAR takes advantage of the reliability and low-latency features of the communication technology in the cellular network and combines the cellular network with VNET to achieve real-time and reliable Vehicle-to-Infrastructure (V2I) communication. Meanwhile, it realizes the Vehicle-to-Vehicle (V2V) communication by the Dedicated Short Range Communication (DSRC) to mitigate the overload of backbone resources caused by using the cellular network. To be specific, in the method, vehicles can request city-wide traffic information via the cellular network from a cloud service that is connected to the remote data center located in the traffic management agency without latency. According to the real-time traffic information, the source vehicle can execute the data routing process with a global view of the system to calculate the data transmission route that has sufficient transmission resources to the target vehicle. The source vehicle then transmits data to the target via the vehicles in the calculated transmission route. During the forwarding process, vehicles prefer to forward the data packet to the next vehicle with a longer link lifetime. Furthermore, effective backup and recovery strategies are designed for route maintenance. The effectiveness of CRTAR is further verified by conducting simulation experiments.

Collision avoidance is treated as a game of two players with opposing desiderata. In the application to automated car-like vehicles, we will use a differential game in order to model and assess a worst-case analysis. The end result will be an almost analytic representation of a boundary between a "safe" set and a "unsafe" set. We will generalize the research in [27] to non-identical players and begin the setup of the boundary construction. Then we will consider the advantages and disadvantages of manipulation of the cost function through the solution and control techniques. In particular, we introduce a possible way to incorporate a secondary objective such as sticking to a straight path. We also look a hybrid technique to reduce steering when the opposing player is out of the reach of the vehicle; i.e., is out of the "unsafe" set and less extreme maneuvers may be desired. We first look at a terminal cost formulation and through retrograde techniques may shape this boundary between the "safe" and "unsafe" set. We would like this research, or part thereof, to be assessed and simulated on a simulation vehicle such as that used in the Flexible Low-cost Automated Scaled Highway (FLASH) at the Virginia Tech Transportation Institute (VTTI). In preparation, we briefly look at the sensor demands from this game-theoretic approach. / Master of Science

Traffic state estimation (TSE) aims to estimate the time-varying traffic characteristics (such as flow rate, flow speed, flow density, and occurrence of incidents) of all roads in traffic networks, provided with limited observations in sparse time and locations. TSE is critical to transportation planning, operation and infrastructure design. In this new era of “big data”, massive volumes of sensing data from a variety of source (such as cell phones, GPS, probe vehicles, and inductive loops, etc.) enable TSE in an efficient, timely and accurate manner. This research develops a Bayesian-based theoretical framework, along with statistical inference algorithms, to (1) capture the complex flow patterns in the urban traffic network consisting both highways and arterials; (2) incorporate heterogeneous data sources into the process of TSE; (3) enable both estimation and perdition of traffic states; and (4) demonstrate the scalability to large-scale urban traffic networks. To achieve those goals, a Hierarchical Bayesian probabilistic model is proposed to capture spatio-temporal traffic states. The propagation of traffic states are encapsulated through mesoscopic network flow models (namely the Link Queue Model) and equilibrated fundamental diagrams. Traffic states in the Hierarchical Bayesian model are inferred using the Expectation-Maximization Extended Kalman Filter (EM-EKF). To better estimate and predict states, infrastructure supply is also estimated as part of the TSE process. It is done by adopting a series of algorithms to translate Twitter data into traffic incident information. Finally, the proposed EM-EKF algorithm is implemented and examined on the road networks in Washington DC. The results show that the proposed methods can handle large-scale traffic state estimation, while achieving superior results comparing to traditional temporal and spatial smoothing methods.

For applications that rely on User-Generated Content (UGC), there is a need to find what may motivate the applications user base to consistently contribute with quality content. One category of such applications is Intelligent Transportation Systems (ITS) traffic applications, which serve a specific goal; providing useful traffic-oriented content. By implementing useful incentives into Intelligent Transportation System traffic applications, the applications can better serve their purposes, and at the same time, improve their user's experience. Incentives are intrinsic or extrinsic, i.e., the motivation comes from internal- or external stimuli, which can motivate users in different ways and produce different incentive outcomes. To find the most useful incentives, and gain a better understanding of how to best stimulate active application participation, the research question addressed by this thesis is: Which incentives are useful for increasing quality content in the field of ITS traffic applications? The main method employed to address the research question was a survey. The survey was carried out to investigate what people thought was motivating in ITS traffic applications. In addition to the survey, an interview with the project manager of a Swedish ITS traffic application was done. Previous research concludes that the gain and the incentive for people or organizations hosting UGC are apparent, but the gain for the creators is not as clearly recognized and varies in which area the content is created. The findings of this study showed, from a user perspective, an interest in helping others and monetary gain, as potential incentives for implementation. The authors concluded that intrinsic inclined incentives should work better in-line with the goal of functionality and user long-term engagement, which the authors believe would be preferable for UGC based ITS traffic applications. These findings will be useful for understanding the optimal way to increase motivation for adequate quality UGC in ITS traffic applications.

A safe and efficient highway infrastructure is a critical component and a valuable asset in terms of its monetary value, as well as supporting the way of life and economic activities of the people it serves. In North America, performing maintenance, repair, and expansion of an aging highway infrastructure to a target level of performance while dealing with ever-increasing traffic demands creates a significant challenge in terms of road user safety and mobility. Much of the current highway infrastructure was built several decades ago and it is therefore requiring increasing levels of maintenance and rehabilitation. <p>The cost of delays resulting from traffic congestion induced by work zones is estimated to be more than $6 billion per year. Work zone related traffic fatalities exceed more than 1000 lost lives per year in North America. Work zone related fatalities account for approximately 2.8 percent of highway fatalities in United States and 1.3 percent in Canada. While overall fatal crash rates have been steadily decreasing in both Canada and United States, work zone related fatalities have not been decreasing. <p>Smart Work Zones are an emerging technology designed to improve the safety and mobility within work zones on highways. Smart Work Zones employ various technologies to monitor current traffic conditions and provide relevant information to road managers and road users on current traffic flow conditions and automatically provide guidance to motorists for safer and more efficient navigation of the work zone. <p>This research examined the effects of a Smart Work Zone deployment by modeling traffic flow with and without a Smart Work Zone at the case study site in North Carolina to provide inputs into a performance analysis framework. The quantification of benefits and costs related to the deployment of a Smart Work Zone was developed in a probabilistic analysis framework model. The performance was quantified in economic terms of expected benefit cost ratio and net value realized from the deployment of a Smart Work Zone. The model considers the cost of deployment and potential savings in terms of motorist safety (fatal and injury crash reduction) as well as improvements in traveler mobility including reductions in user delays, vehicle operating costs, and emissions.<p>The model output is a risk profile that provides a range of expected values and associated probabilities of occurrence to quantify the expected benefits while also taking into consideration the uncertainty of the most sensitive input variables. The uncertainty of input variables determined to be the most sensitive were those associated with the amount of user delay and the valuation of user delay. The next most sensitive inputs are those associated with the cost of deploying and operating the Smart Work Zone system. <p>The model developed in this research concurs with the approach and analysis used in other models for the analysis of transportation projects. The model developed in this research provides a tool that can be used for decision making regarding the deployment of a Smart Work Zone and comparison with other transportation project alternatives. The model employs a user definable approach that enables it to be adapted to the specific conditions of a diverse range of field state conditions and has the ability to interface with several traffic flow models. <p>When applied to a case study project on Interstate 95 in North Carolina, the model was found to be capable of providing useful and relevant results that correlated to observed performance. The case study represented one of many operating scenarios on the project, and is not necessarily representative of all the field state conditions occurring over the period of the entire deployment. <p>The model results included a sensitivity analysis that identified the sensitivity of the outcome to uncertainty in the input values and a risk analysis that quantified the uncertainty of the predictions. The findings indicated that, at a 95 percent confidence level, the expected benefit / cost ratio of deploying a Smart Work Zone system was between 1.2 and 11.9 and the net value was between $10,000 and $225,000 per month of operation. Approximately 94 percent of the expected benefits were from savings in user delay and the remainder from savings due to improved safety, reduced emissions, and reduced vehicle operating costs. The results indicate that when applied under appropriate conditions, Smart Work Zones have the potential to provide significant benefits to road users. Under heavily congested conditions, the diversion of even a small amount of traffic to a more efficient route can provide sizable travel time improvements for all traffic.<p>In summary, the model developed in this research was specifically developed to apply to Smart Work Zones, but in its general form could also be applied to other work zone traffic management applications. In the case study the model was applied to a single rural work zone, but the framework could be extended for an integrated analysis of multiple work zones and network analysis in an urban setting. The research provides a fundamental framework and model for the analysis of Smart Work Zones and a method to determine the sensitivity of the uncertainty of input values. The research also identifies areas for continued examination of the effects of Smart Work Zone deployment and the prediction of expected benefits.

A safe and efficient highway infrastructure is a critical component and a valuable asset in terms of its monetary value, as well as supporting the way of life and economic activities of the people it serves. In North America, performing maintenance, repair, and expansion of an aging highway infrastructure to a target level of performance while dealing with ever-increasing traffic demands creates a significant challenge in terms of road user safety and mobility. Much of the current highway infrastructure was built several decades ago and it is therefore requiring increasing levels of maintenance and rehabilitation. <p>The cost of delays resulting from traffic congestion induced by work zones is estimated to be more than $6 billion per year. Work zone related traffic fatalities exceed more than 1000 lost lives per year in North America. Work zone related fatalities account for approximately 2.8 percent of highway fatalities in United States and 1.3 percent in Canada. While overall fatal crash rates have been steadily decreasing in both Canada and United States, work zone related fatalities have not been decreasing. <p>Smart Work Zones are an emerging technology designed to improve the safety and mobility within work zones on highways. Smart Work Zones employ various technologies to monitor current traffic conditions and provide relevant information to road managers and road users on current traffic flow conditions and automatically provide guidance to motorists for safer and more efficient navigation of the work zone. <p>This research examined the effects of a Smart Work Zone deployment by modeling traffic flow with and without a Smart Work Zone at the case study site in North Carolina to provide inputs into a performance analysis framework. The quantification of benefits and costs related to the deployment of a Smart Work Zone was developed in a probabilistic analysis framework model. The performance was quantified in economic terms of expected benefit cost ratio and net value realized from the deployment of a Smart Work Zone. The model considers the cost of deployment and potential savings in terms of motorist safety (fatal and injury crash reduction) as well as improvements in traveler mobility including reductions in user delays, vehicle operating costs, and emissions.<p>The model output is a risk profile that provides a range of expected values and associated probabilities of occurrence to quantify the expected benefits while also taking into consideration the uncertainty of the most sensitive input variables. The uncertainty of input variables determined to be the most sensitive were those associated with the amount of user delay and the valuation of user delay. The next most sensitive inputs are those associated with the cost of deploying and operating the Smart Work Zone system. <p>The model developed in this research concurs with the approach and analysis used in other models for the analysis of transportation projects. The model developed in this research provides a tool that can be used for decision making regarding the deployment of a Smart Work Zone and comparison with other transportation project alternatives. The model employs a user definable approach that enables it to be adapted to the specific conditions of a diverse range of field state conditions and has the ability to interface with several traffic flow models. <p>When applied to a case study project on Interstate 95 in North Carolina, the model was found to be capable of providing useful and relevant results that correlated to observed performance. The case study represented one of many operating scenarios on the project, and is not necessarily representative of all the field state conditions occurring over the period of the entire deployment. <p>The model results included a sensitivity analysis that identified the sensitivity of the outcome to uncertainty in the input values and a risk analysis that quantified the uncertainty of the predictions. The findings indicated that, at a 95 percent confidence level, the expected benefit / cost ratio of deploying a Smart Work Zone system was between 1.2 and 11.9 and the net value was between $10,000 and $225,000 per month of operation. Approximately 94 percent of the expected benefits were from savings in user delay and the remainder from savings due to improved safety, reduced emissions, and reduced vehicle operating costs. The results indicate that when applied under appropriate conditions, Smart Work Zones have the potential to provide significant benefits to road users. Under heavily congested conditions, the diversion of even a small amount of traffic to a more efficient route can provide sizable travel time improvements for all traffic.<p>In summary, the model developed in this research was specifically developed to apply to Smart Work Zones, but in its general form could also be applied to other work zone traffic management applications. In the case study the model was applied to a single rural work zone, but the framework could be extended for an integrated analysis of multiple work zones and network analysis in an urban setting. The research provides a fundamental framework and model for the analysis of Smart Work Zones and a method to determine the sensitivity of the uncertainty of input values. The research also identifies areas for continued examination of the effects of Smart Work Zone deployment and the prediction of expected benefits.