In recent years, there has been a considerable increase in the amount of construction work on the U.S. national highways. Most of the work undertaken is the reconstruction and rehabilitation of the existing transportation networks. Work zones in the United States are likely to increase in number, duration and length due to emphasis on repair and highway reconstruction as a significant portion of all federal-aid highway funds are now geared toward highway rehabilitation. The challenge of mobility is particularly acute in work zone areas as road repair and construction intensifies traffic issues and concentrates them in specific locations and at specific times. Due to the capacity drop, which is the result of lane closure in work zone area, congestion will occur with a high traffic demand. The congestion increases number and severity of traffic conflicts which raise the potential for accidents; furthermore traffic operational properties of roadway in work zone area become worse. Intelligent Transportation System (ITS) technologies have been developed and are being deployed to improve the safety and mobility of traffic in and around work zones. In several states in the US, the use of Dynamic Merge Controls also known as Dynamic Lane Merge (DLM) system has been initiated to enhance traffic safety and to improve traffic flow in work zone areas. The DLM usually takes two forms; dynamic iii early merge and dynamic late merge. The use of variable speed limit (VSL) systems at work zones is also one of those measures. VSL systems improve safety by helping the driver in determining the maximum speed that drivers should travel. Besides adding improvement to safety, they are also expected to improve mobility at the work zones. The main goal of this study is to evaluate the safety and operational effectiveness of the dynamic merge systems i.e. the dynamic early lane merge and dynamic late lane merge, in the presence of VSL system. More specifically, the VISSIM model is utilized to simulate a twoto-one lane configuration when one out of the two lanes in the work zone is closed for traffic. Six different scenarios were adopted to assess the effectiveness of these scenarios under different traffic demand volumes and different drivers‟ compliance rates to the messages displayed by the systems. These scenarios are;  Work Zone without VSL and without SDLMS or the current Motorist Awareness System (MAS)  Work Zone with VSL and without SDLMS  Work Zone with VSL and Early SDLMS  Work Zone with VSL and Late SDLMS  Work Zone with early SDLMS and without VSL  Work Zone with late SDLMS and without VSL iv An already calibrated and validated VISSIM model for Simplified Dynamic Lane Merge System (SDLMS) in accordance with the real life work zone was modified with a VSL through Vehicle Actuated Programming (VAP) code. Three different logics were coded each for VSL alone, early SDLMS+VSL and late SDLMS+VSL. All these logics were fine tuned with several test runs before finalizing it for the final simulation. It is found through the simulation of above mentioned scenarios that for low and medium volume levels (V0500, V1000 and V1500), there is no significant difference between the Maintenance of Traffic (MOT) plans for mean throughputs. However, for higher volume levels (V2000 and V2500), late SDLMS with and without VSL produced higher mean throughputs for all compliance rates and truck percentages except when the demand volume was 2,500 vph and compliance of 60%, where it produces the significantly lower mean throughputs. In terms of travel time through the work zone, results indicated that there is no significant difference between MOT types for demand levels of V0500 and V1000 when compliance is 40% or less but for compliance of 60% and more, only demand volume level that is not significantly different from other MOT types is V0500. This study revealed that VSL increases travel time through the work zone. This might be due to non-compliant vehicles that follow the compliant vehicle v ahead unless they find a sufficient gap in adjacent lane to pass the compliant vehicle. It is also found out that VSL makes the system safer at higher volumes (2,000 vph and 2,500 vph). This was observed through safety surrogate measures selected for this study. Another outcome of this study is that the addition of VSL to the dynamic merge systems helps in improving the overall safety of the system by lowering speed variances and deceleration means of the vehicles travelling through the work zone. The passage of traffic through the work zone is made safer when a speed control is integrated to a dynamic merge system. It can be inferred from the simulation results that integrated SDLMS and VSL systems have better performance in terms of traffic mobility and safety than existing individual controls and also show that the integrated SDLMS and VSL system has more potential than each individual systems.

Smart environments provide users with a large number of new services that will improve their lives, however, they also have the potential for collecting staggering amounts of personal information, which, if misused, poses a multitude of privacy threats to users ranging from identification, tracking, stalking, monitoring and profiling. Consequently, the users’ right to informational self-determination is at stake in smart environments. Usable Privacy-Enhancing Identity Management (PE-IdM) can re-establish user control by offering users a selection of meaningful privacy preference settings that they could choose from. However, different privacy trade-offs need to be considered and managed for the configuration of the identity management system as well as cultural privacy aspects influencing user's privacy preferences. Guidelines for usable management of privacy settings that address varying end user preferences for control and privacy conflicting goals are needed.   The objective of this thesis is to explore approaches for enforcing usable PE-IdM for smart environments, with a focus on vehicular ad hoc networks (VANETs). To that end, we unravel the technical state of the art regarding the problem space and solutions, as well as investigating users’ privacy preferences cross-culturally in Sweden and South Africa. We elicit requirements for achieving usable PE-IdM, which are based on usable configuration options, offering suitable selectable privacy settings that will cater for the needs and preferences of users with different cultural backgrounds.

No description available.

Security threats to cyber-physical systems are targeting institutions and infrastructure around the world, and the frequency and severity of attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Wireless Sensors Networks, Vehicle-to-everything communication (V2X), Dynamic Message Signs (DMS), and Traffic Signal Controllers are among major Intelligent Transportation Systems (ITS) infrastructure that has already been attacked or remain vulnerable to hacking. ITS has been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in travel demand. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices such as DMS are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is due to their location. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. There may be room for improvement by policymakers and program managers when considering critical infrastructure vulnerabilities. With cybersecurity issues escalating every day, road users' safety has been neglected. This dissertation overcomes these challenges and contributes to the nascent but growing literature of Intelligent Transportation System (ITS) security impact-oriented risk assessment in threefold. • First, I employ a risk-based approach to conduct a threat assessment. This threat assessment performs a qualitative vulnerability-oriented threat analysis. The objective is to scrutinize safety, security, reliability, and operation issues that are prompted by a compromised Dynamic Message Signs (DMS). • Second, I examine the impact of drivers' attitudes and behaviors on compliance, route diversion behavior, and speed change behavior, under a compromised DMS. We aim to assess the determinants that are likely to contribute to drivers' compliance with forged information. To this extent, this dissertation evaluates drivers' behavior under different unauthentic messages to assess in-depth the impact of an adversarial attack on the transportation network. • Third, I evaluate distracted driving under different scenarios to assess the in-depth impact of an adversarial attack on the transportation network. To this extent, this dissertation examines factors that are contributing to the manual, visual, and cognitive distractions when drivers encountering fabricated advisory information at a compromised DMS. The results of this dissertation support the original hypothesis and indicate that with respect to the forged information drivers tend to (1) change their planned route, (2) become involved in distracting activities, and (3) change their choice speed at the presence of a compromised DMS. The main findings of this dissertation are outlined below: 1. The DMS security vulnerabilities and predisposing conditions allow adversaries to compromise ITS functionality. The risk-based approach of this study delivers the impact-likelihood matrix, which maps the adverse impacts of the threat events onto a meaningful, visual, matrix. DMS hacking adverse impacts can be categorized mainly as high-risk and medium-risk clusters. The safety, operational (i.e., monetary losses) and behavioral impacts are associated with a high-risk cluster. While the security, reliability, efficiency, and operational (i.e., congestion) impacts are associated with the medium-risk cluster. 2. Tech friendly drivers are more likely to change their route under a compromised DMS. At the same time, while they are acquiring new information, they need to lowering their speed to respond to the higher information load. Under realistic-fabricated information, about 65% of the subjects would depart from their current route. The results indicate that females and subjects with a higher driving experience are more likely to change their route. In addition, those subjects who are more sensitive to the DMS's traffic-related messages and those who use DMS under congested traffic condition are more likely to divert. Interestingly, individuals with lower education level, Asians, those who live in urban areas, and those with trouble finding their direction in new routes are less likely to pick another route rather the one they planned for. 3. Regardless of the DMS hacking scenarios, drivers would engage in at least one of the distractive activities. Among the distractive activities, cognitive distraction has the highest impact on the distracted driving likelihood. Meaning, there is a high chance that drivers think of something other than driving, look at surrounding traffic and scenery, or talk to other passengers regarding the forged information they saw on the DMS. Drivers who rely and trust in technology, and those who check traffic condition before starting their trips tend to become distracted. In addition, the result identified that at the presence of bogus information, drivers tend to slow down or stop in order to react to the DMS. That is, they would either (1) become involved in activities through the means of their phone, (2) they would mind wander, look around, and talk to a passenger about the sign, and (3) search for extra information by means of their vehicle's radio or internet. 4. Females, black individuals, subjects with a disability, older, and those with high trust in DMS are less likely to ignore the fabricated messages. In contrary, white, those who drive long hours, and those who see driving as a tedious task are more likely to ignore the bogus messages. Drivers who comply with traffic regulations and have a good driving record are likely to slow down under the tampered messages. Furthermore, female drivers and those who live in rural areas are more likely to slow down under fabricated advisory information. Furthermore, this dissertation identifies that planning for alternative route and involvement in distractive activities cause speed variation behaviors under the compromised DMS. This dissertation is the first to investigate the adverse impact of a compromised DMS on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities. Broader impacts of this study include (1) to systematically raising awareness among policy-makers and engineers, (2) motivating further simulations and real-world experiments to investigate this matter further, (3) to systematically assessing the adverse impact of a security breach on transportation reliability and safety, and drivers' behavior, and (4) providing insights for system operators and decision-makers to prioritize the risk of a compromised DMS. Additionally, the outcome can be integrated with the nationwide connected vehicle and V2X implementations and security design. / Doctor of Philosophy / Security threats are targeting institutions and infrastructure around the world, and the frequency and severity of security attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Intelligent Transportation Systems have been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in traffic volume. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices, such as dynamic message signs, are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is that of their location in public. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. This study is the first to investigate the adversarial impact of a compromised message sign on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities.

The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. A cost-effective approach for improving and optimizing transportation-related problems is to unlock hidden knowledge in ever-increasing spatiotemporal and crowdsourced information collected from various sources such as mobile phone sensors (e.g., GPS sensors) and social media networks (e.g., Twitter). Data mining and machine learning techniques are the major tools for analyzing the collected data and extracting useful knowledge on traffic conditions and mobility behaviors. Deep learning is an advanced branch of machine learning that has enjoyed a lot of success in computer vision and natural language processing fields in recent years. However, deep learning techniques have been applied to only a small number of transportation applications such as traffic flow and speed prediction. Accordingly, my main objective in this dissertation is to develop state-of-the-art deep learning architectures for resolving the transport-related applications that have not been treated by deep learning architectures in much detail, including (1) travel mode detection, (2) vehicle classification, and (3) traffic information system. To this end, an efficient representation for spatiotemporal and crowdsourced data (e.g., GPS trajectories) is also required to be designed in such a way that not only be adaptable with deep learning architectures but also contains efficient information for solving the task-at-hand. Furthermore, since the good performance of a deep learning algorithm is primarily contingent on access to a large volume of training samples, efficient data collection and labeling strategies are developed for different data types and applications. Finally, the performance of the proposed representations and models are evaluated by comparing to several state-of-the-art techniques in literature. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application. / PHD / The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. Furthermore, the recent advances in positioning tools (e.g., GPS sensors) and ever-popularity of social media networks have enabled generation of massive spatiotemporal and crowdsourced data. This dissertation aims to leverage the advances in artificial intelligence so as to unlock the rick knowledge in the recorded data and in turn, optimizing the transportation systems in a cost-effective way. In particular, this dissertation seeks for proposing end-to-end frameworks based on deep learning models, as an advanced branch of artificial intelligence, as well as spatiotemporal and crowdsourced datasets (e.g., GPS trajectory and social media) for improving three transportation problems. (1) Travel Mode Detection, which is defined as identifying users’ transportation mode(s) (e.g., walk, bike, bus, car, and train) when traveling around the traffic network. (2) Vehicle Classification, which is defined as identifying the vehicle’s type (e.g., passenger car and truck) while moving in a traffic network. (3) traffic information system based on social media networks, which is defined as detecting traffic events (e.g., crash) and capturing traffic information (e.g., traffic congestion) on a real-time basis from users’ tweets. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application.

Automobile accidents are one of the leading causes of death and claim more than 40,000 lives annually in the US alone. A substantial portion of these accidents occur at road intersections. Stop signs and traffic signals are some of the intersection control devices used to increase safety and prevent collisions. However, these devices themselves can contribute to collisions, are costly, inefficient, and are prone to failure. This thesis proposes an adaptive, decentralized, cooperative collision avoidance (CCA) system that optimizes each vehicle's controls subject to the constraint that no collisions occur. Three major contributions to the field of collision avoidance have resulted from this research. First, a nonlinear 5-state variable vehicle model is expanded from an earlier model developed in [1]. The model accounts for internal engine characteristics and more realistically approximates vehicle behavior in comparison to idealized, linear models. Second, a set of constrained, coupled Extended Kalman Filters (EKF) are used to predict the trajectory of the vehicles approaching an intersection in real-time. The coupled filters support decentralized operation and ensure that the optimization algorithm bases its decisions on good, reliable estimates. Third, a vehicular network based on the new WAVE standard is presented that provides cooperative capabilities by enabling intervehicle communication. The system is simulated against today's common intersection control devices and is shown to be superior in minimizing average vehicle delay. / Master of Science

Yes / Blockchain operates on a highly secured framework, and its decentralized consensus has benefits for supply chain sustainability. Scholars have recognized the growing importance of sustainability in supply chains and studied the potential of blockchain for sustainable supply chain management. However, no study has taken stock of high-quality research in this area. To address this gap, this paper aims to provide a state-of-the-art overview of high-quality research on blockchain for sustainable supply chain management. To do so, this paper conducts a systematic literature review using a bibliometric analysis of 146 high-quality articles on blockchain for sustainable supply chain management that have been published in journals ranked “A*”, “A”, and “B” by the Australian Business Deans Council and retrieved from the Scopus database. In doing so, this paper unpacks the most prominent journals, authors, institutions, and countries that have contributed to three major themes in the field, namely blockchain for sustainable business activities, decision support systems using blockchain, and blockchain for intelligent transportation system. This paper also reveals the use of blockchain for sustainable supply chain management across four major sectors, namely food, healthcare, manufacturing, and infrastructure, and concludes with suggestions for future research in each sector.

Esta dissertação propõe um modelo tecnológico de automação para realização de pesquisas no setor do transporte público, com o objetivo de contribuir para o aprimoramento da coleta de dados, avaliação e manutenção da qualidade dos serviços prestados à população. O trabalho justifica-se pela necessidade de superação de lacunas existentes para obtenção de informações, o que repercute na gestão do sistema de transporte público como um todo. Devido à relevância crescente do tema da mobilidade urbana e os impactos que provoca na qualidade de vida das pessoas, o objeto de estudo escolhido foram os deslocamentos dos passageiros por meio do uso de ônibus na cidade de São Paulo. O modelo proposto integra a tecnologia de identificação por radiofrequência (RFID - Radio Frequency IDentification), em cartões inteligentes, utilizados atualmente para pagar a tarifa, com tecnologias de rastreamento da frota, que, por meio de GPS (Global Position Systems), fornecem informações sobre os locais de circulação dos ônibus. Os resultados obtidos mostram que esta integração pode resolver os problemas da falta de precisão no levantamento de dados sobre os locais onde são iniciadas e finalizadas as viagens de passageiros, bem como tornar sistemáticos os levantamentos de tais dados, sem necessidade de pesquisas manuais, o que representa economia de recursos. Constitui uma proposta inovadora com grande utilidade para ampliar as condições que favorecem a mobilidade urbana e é convergente no desenvolvimento de cidades inteligentes. / This dissertation proposes a technological model for automation for conducting surveys in the public transport sector, in order to contribute to the improvement of data collection, evaluation and maintenance of quality of services rendered to the population. The work is justified by the need to overcome gaps for obtaining information, which affects the management of the public transport system as a whole. Due to the increasing relevance of the issue of urban mobility and its impact on quality of life, the chosen object of study were the passenger movements through the bus use in the city of São Paulo. The proposed model integrates the radio frequency identification technology - RFID, on smart cards currently used to pay the fare, with fleet tracking technologies, which, through GPS (Global Position Systems), provide information on the bus traffic locations. The results show that this integration can solve the problems of lack of precision in data about where passenger trips are initiated and completed as well as make systematic withdrawals of such data without the need for manual searches, saving features. It is an innovative proposal with great use to expand the conditions that improve urban mobility and is convergent to the development of smart cities.

This work presents the implementations of three adaptive broadcast protocols for vehicular ad hoc networks (VANET) using the Network Simulator 3 (Ns-3). Performing real life tests for VANET protocols is very costly and risky, so simulation becomes a viable alternative technique. Ns-3 is one of the most advanced open source network simulators. Yet Ns-3 lacks implementations of broadcast protocols for VANET. We first implement the Distance to Mean (DTM) protocol, which uses the distance to mean to determine if a node should rebroadcast or not. We then implement the Distribution-Adaptive Distance with Channel Quality (DADCQ) protocol, which uses node distribution, channel quality and distance to determine if a node should favor rebroadcasting. The third protocol, Statistical Location-Assisted Broadcast protocol (SLAB), is an improvement of DADCQ which automates the threshold function design using machine learning. Our NS-3 implementations of the three protocols have been validated against their JiST/SWANS implementations. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

A Vehicular Ad-hoc Network (VANET) is a wireless ad-hoc network that provides communications among vehicles with on-board units and between vehicles and nearby roadside units. The success of a VANET relies on the ability of a routing protocol to ful ll the throughput and delivery requirements of any applications operating on the network. Currently, most of the proposed VANET routing protocols focus on urban or highway environments. This dissertation addresses the need for an adaptive routing protocol in VANETs which is able to tolerate low and high-density network tra c with little throughput and delay variation. This dissertation proposes three Geographic Ad-hoc On-Demand Distance Vector (GEOADV) protocols. These three GEOADV routing protocols are designed to address the lack of exibility and adaptability in current VANET routing protocols. The rst protocol, GEOADV, is a hybrid geographic routing protocol. The second protocol, GEOADV-P, enhances GEOADV by introducing predictive features. The third protocol, GEOADV-PF improves optimal route selection by utilizing fuzzy logic in addition to GEOADV-P's predictive capabilities. To prove that GEOADV and GEOADV-P are adaptive their performance is demonstrated by both urban and highway simulations. When compared to existing routing protocols, GEOADV and GEOADV-P lead to less average delay and a higher average delivery ratio in various scenarios. These advantages allow GEOADV- P to outperform other routing protocols in low-density networks and prove itself to be an adaptive routing protocol in a VANET environment. GEOADV-PF is introduced to improve GEOADV and GEOADV-P performance in sparser networks. The introduction of fuzzy systems can help with the intrinsic demands for exibility and adaptability necessary for VANETs. An investigation into the impact adaptive beaconing has on the GEOADV protocol is conducted. GEOADV enhanced with an adaptive beacon method is compared against GEOADV with three xed beacon rates. Our simulation results show that the adaptive beaconing scheme is able to reduce routing overhead, increase the average delivery ratio, and decrease the average delay. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection