"Drivers' "illegal maneuver" and "unsafe behavior" contribute to a large number of traffic accidents every year, which are now receiving great attention from both government regulators and car manufacturers. Indeed, many research efforts have been dedicated to understanding and recognizing dangerous driving conditions to prevent crashes and injuries. In addition to the features that are already installed in the vehicles, enhancing driving safety via mobile sensing techniques (e.g., smartphones and wearables) is becoming increasingly successful with the deep penetration of smart computing. The mobile device today is equipped with numerous sensors, which has become a very effective platform to facilitate various safety applications. In this thesis, we leverage off-the-shelf mobile sensing platforms (i.e., smartphones and wrist-worn devices) to detect and analyze dangerous driving events. Our purpose is to use real-time alerts and long-term feedbacks to increase drivers' awareness of dangerous behaviors, which could help them shape good driving habits and promote safety. Specifically, two studies are presented: 1. SafeCam - analyzing intersection-related driver behaviors using smartphone sensors, and 2. SafeDrive - monitoring distracted driving behaviors using wrist-worn devices (e.g., smartwatch). The first study focuses on the intersection safety which is a critical issue in current roadway systems. In the United States, nearly one-quarter of traffic fatalities and half of all traffic injuries are attributed to intersections. We design SafeCam that uses embedded sensors (i.e., inertial sensors and cameras) on the smartphone to track vehicle dynamics while at the same time adopts computer vision algorithms to recognize traffic control information (e.g., traffic lights and stop signs). The system is able to detect dangerous driving events not only on roads but also at intersections including speeding, lane waving, unsafe turns, running stop signs and running red lights. Our second study addresses the distracted driving problem that has been considered as a major threat to the traffic safety. It is estimated that roughly 30% of vehicle fatalities involve distracted drivers, which cause thousands of injuries and deaths every year in the United States. SafeDrive is a driving safety system that leverages the wrist-worn (i.e.,smartwatch) sensors to prevent driver distractions. By tracking driver's hand motion and utilizing machine learning algorithms, SafeDrive can detect five most common distracting activities including fiddling with the control (e.g., infotainment systems), drinking/eating, using smartphones, searching items at the passenger side and reaching back seats. In the evaluation, we conduct extensive real-road experiments using different types of vehicles (e.g., sedan, minivan, and SUV) and recruiting multiple participants (15 for SafeCam and 20 for SafeDrive). The experiment results demonstrate that both SafeCam and SafeDrive are robust to real-driving environments, which could detect critical driving events and have great potential to educate drivers on how to safely operate the vehicle." --

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems.

An emerging trend in the automobile industry is its convergence with information technology (IT). Indeed, it has been estimated that almost 90% of new automobile technologies involve IT in some form. Smart driving technologies that improve safety as well as green fuel technologies are quite representative of the convergence between IT and automobiles. The smart driving technologies include three key elements: sensing of driving environments, detection of objects and potential hazards and the generation of driving control signals including warning signals. Although radar-based systems are primarily used for sensing the driving environments, the camera has gained importance in advanced driver assistance systems (ADAS). This book covers system-on-a-chip (SoC) designs—including both algorithms and hardware—related with image sensing and object detection by using the camera for smart driving systems. It introduces a variety of algorithms such as lens correction, super resolution, image enhancement and object detections from the images captured by low-cost vehicle camera. This is followed by implementation issues such as SoC architecture, hardware accelerator, software development environment and reliability techniques for automobile vision systems. This book is aimed for the new and practicing engineers in automotive and chip-design industries to provide some overall guidelines for the development of automotive vision systems. It will also help graduate students understand and get started for the research work in this field.

This book discusses the application of different machine learning techniques to the sub-concepts of smart cities such as smart energy, transportation, waste management, health, infrastructure, etc. The focus of this book is to come up with innovative solutions in the above-mentioned issues with the purpose of alleviating the pressing needs of human society. This book includes content with practical examples which are easy to understand for readers. It also covers a multi-disciplinary field and, consequently, it benefits a wide readership including academics, researchers, and practitioners.

Road accidents caused by impaired and distracted driving as well as traffic congestion are on the rise, with the numbers increasing dramatically every day. Intelligent transportation systems (ITS) aim to improve the efficiency and safety of traveling by consolidating vehicle operations, managing vehicle traffic, and notifying drivers with alerts and safety messages in real time. Vehicular Cloud Computing for Traffic Management and Systems provides innovative research on the rapidly advancing applications of vehicle-to-vehicle and vehicle-to-infrastructure communication. It also covers the need to fully utilize vehicular ad-hoc network (VANET) resources to provide updated and dynamic information about the conditions of road traffic so that the number of road accidents can be minimized. Featuring research on topics such as identity management, computational architecture, and resource management, this book is ideally designed for urban planners, researchers, policy makers, graduate-level students, transportation engineers, and technology developers seeking current research on vehicle computational design, architecture, security, and privacy.

The importance of sensing technologies in the field of transportation is ever increasing. Rapid improvements of cloud computing, Internet of Vehicles (IoV), and intelligent transport system (ITS) enables fast acquisition of sensor data with immediate processing. Machine learning algorithms provide a way to classify or predict outcomes in a selective and timely fashion. High accuracy and increased volatility are the main features of various learning algorithms. In this dissertation, we aim to use infrastructure- and vehicle-based sensors to improve safety and mobility of urban transportation systems. Smartphone sensors were used in the first study to estimate vehicle trajectory using lane change classification. It addresses the research gap in trajectory estimation since all previous studies focused on estimating trajectories at roadway segments only. Being a mobile application-based system, it can readily be used as on-board unit emulators in vehicles that have little or no connectivity. Secondly, smartphone sensors were also used to identify several transportation modes. While this has been studied extensively in the last decade, our method integrates a data augmentation method to overcome the class imbalance problem. Results show that using a balanced dataset improves the classification accuracy of transportation modes. Thirdly, infrastructure-based sensors like the loop detectors and video detectors were used to predict traffic signal states. This system can aid in resolving the complex signal retiming steps that is conventionally used to improve the performance of an intersection. The methodology was transferred to a different intersection where excellent results were achieved. Fourthly, magnetic vehicle detection system (MVDS) was used to generate traffic patterns in crash and non-crash events. Variational Autoencoder was used for the first time in this study as a data generation tool. The results related to sensitivity and specificity were improved by up to 8% as compared to other state-of-the-art data augmentation methods.

This book contains a collection of high-quality papers describing the results of relevant investigations and cutting-edge technologies, aimed at improving key aspects of real life, including major challenges such as the development of smart cities, smart buildings, smart grids, and the reduction of the impact of human activities on the environment. Sustainability requires the use of green technologies and techniques and good practices. Artificial intelligence seems to be an appropriate approach to optimize the use of resources. The main focus of this book is the dissemination of novel and innovative technologies, techniques and applications of artificial intelligence, computing and information and communications technologies, and new digital services such as digital marketing, smart tourism, smart agriculture, green and renewable energy sources. Besides, this book focuses on nurturing energy trends including renewable energies, smart grids, human activity impact, communication, behaviour, and social development, and quality of life improvement fields based on the innovative use of sensors, big data and the Internet of things (IoT), telecommunications and machine learning.