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Author: Tejswaroop Reddy Geetla Publisher: ISBN: Category : Languages : en Pages : 116
Book Description
Intelligent transportation sensors are widely used in transportation systems to improve emergency response system and provide real-time traffic and weather related information to road users and traffic managers. These ITS sensors are increasingly equipped with wireless networking capabilities that help in data assimilation and apply data fusion techniques. The goal of this dissertation is to improve the current state of the transportation system by applying operations research techniques to control intelligent transportation sensors. In the first part of this dissertation, we consider the use of stationary and mobile ITS sensors from different classes in motor vehicle crash detection and characterization. For the class of stationary sensors, placement plays an important role in crash detection, due to the constraints on sensor detection radius. Operations research techniques formulate this placement of sensors as a quadratic maximal coverage location problem. To solve this formulation we use an explicit-implicit-simulation based hybrid heuristic designed for the sensor placement problem. The mathematical model approximates the real-road sensor behavior. To understand the real-world behavior of sensor placement we developed a simulation model using real-road data available from 2004-2009. Simulation results prove that the solution generated using a hybrid explicit-implicit-simulation-based optimization model yield good solutions to the sensor placement problem. A principal goal of the first part of the dissertation is to quantify the use of mobile and stationary sensors in incident detection. The second part of the dissertation addresses a near future scenario, where hundreds of ITS sensors that have wireless networking capabilities are deployed in the road network system. To handle this large sensor network we propose the formation of clusters in the wireless sensor network. Cluster formation helps in network stabilization, avoids data duplication and avoids the formation of data bottlenecks. In the wireless sensor network literature, this problem is typically handled using rule based heuristics that select a clusterhead and form clusters. However, this approach, though practical, ignores the information regarding sensor movement. We use a mathematical programming based approach to clustering. The mathematical formulation developed for the clustering problem is NP-Hard. We propose heuristic solutions to find feasible initial solutions and improve the CPLEX performance using parameter tuning and warm starts. The mathematical formulation developed for the clustering problem is an approximate model. To understand the real-world behavior of the solution, we develop a simulation module that evaluates the sensor coverage possible through cluster formation.
Author: Tejswaroop Reddy Geetla Publisher: ISBN: Category : Languages : en Pages : 116
Book Description
Intelligent transportation sensors are widely used in transportation systems to improve emergency response system and provide real-time traffic and weather related information to road users and traffic managers. These ITS sensors are increasingly equipped with wireless networking capabilities that help in data assimilation and apply data fusion techniques. The goal of this dissertation is to improve the current state of the transportation system by applying operations research techniques to control intelligent transportation sensors. In the first part of this dissertation, we consider the use of stationary and mobile ITS sensors from different classes in motor vehicle crash detection and characterization. For the class of stationary sensors, placement plays an important role in crash detection, due to the constraints on sensor detection radius. Operations research techniques formulate this placement of sensors as a quadratic maximal coverage location problem. To solve this formulation we use an explicit-implicit-simulation based hybrid heuristic designed for the sensor placement problem. The mathematical model approximates the real-road sensor behavior. To understand the real-world behavior of sensor placement we developed a simulation model using real-road data available from 2004-2009. Simulation results prove that the solution generated using a hybrid explicit-implicit-simulation-based optimization model yield good solutions to the sensor placement problem. A principal goal of the first part of the dissertation is to quantify the use of mobile and stationary sensors in incident detection. The second part of the dissertation addresses a near future scenario, where hundreds of ITS sensors that have wireless networking capabilities are deployed in the road network system. To handle this large sensor network we propose the formation of clusters in the wireless sensor network. Cluster formation helps in network stabilization, avoids data duplication and avoids the formation of data bottlenecks. In the wireless sensor network literature, this problem is typically handled using rule based heuristics that select a clusterhead and form clusters. However, this approach, though practical, ignores the information regarding sensor movement. We use a mathematical programming based approach to clustering. The mathematical formulation developed for the clustering problem is NP-Hard. We propose heuristic solutions to find feasible initial solutions and improve the CPLEX performance using parameter tuning and warm starts. The mathematical formulation developed for the clustering problem is an approximate model. To understand the real-world behavior of the solution, we develop a simulation module that evaluates the sensor coverage possible through cluster formation.
Author: Lawrence A. Klein Publisher: CRC Press ISBN: 1351800973 Category : Technology & Engineering Languages : en Pages : 574
Book Description
An intelligent transportation system (ITS) offers considerable opportunities for increasing the safety, efficiency, and predictability of traffic flow and reducing vehicle emissions. Sensors (or detectors) enable the effective gathering of arterial and controlled-access highway information in support of automatic incident detection, active transportation and demand management, traffic-adaptive signal control, and ramp and freeway metering and dispatching of emergency response providers. As traffic flow sensors are integrated with big data sources such as connected and cooperative vehicles, and cell phones and other Bluetooth-enabled devices, more accurate and timely traffic flow information can be obtained. The book examines the roles of traffic management centers that serve cities, counties, and other regions, and the collocation issues that ensue when multiple agencies share the same space. It describes sensor applications and data requirements for several ITS strategies; sensor technologies; sensor installation, initialization, and field-testing procedures; and alternate sources of traffic flow data. The book addresses concerns related to the introduction of automated and connected vehicles, and the benefits that systems engineering and national ITS architectures in the US, Europe, Japan, and elsewhere bring to ITS. Sensor and data fusion benefits to traffic management are described, while the Bayesian and Dempster–Shafer approaches to data fusion are discussed in more detail. ITS Sensors and Architectures for Traffic Management and Connected Vehicles suits the needs of personnel in transportation institutes and highway agencies, and students in undergraduate or graduate transportation engineering courses.
Author: United States. Congress. Senate. Committee on Appropriations. Subcommittee on Dept. of Transportation and Related Agencies Appropriations Publisher: ISBN: Category : Political Science Languages : en Pages : 2018
Author: Pradeep Kumar Singh Publisher: Springer Nature ISBN: 9811911428 Category : Technology & Engineering Languages : en Pages : 906
Book Description
This book features selected research papers presented at the Third International Conference on Computing, Communications, and Cyber-Security (IC4S 2021), organized in Krishna Engineering College (KEC), Ghaziabad, India, along with Academic Associates; Southern Federal University, Russia; IAC Educational, India; and ITS Mohan Nagar, Ghaziabad, India, during October 30–31, 2021. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues.
Author: Jiadi Yu Publisher: Springer ISBN: 3319897705 Category : Computers Languages : en Pages : 81
Book Description
This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.
Author: United States. Congress. House Publisher: ISBN: Category : Legislation Languages : en Pages : 1912
Book Description
Some vols. include supplemental journals of "such proceedings of the sessions, as, during the time they were depending, were ordered to be kept secret, and respecting which the injunction of secrecy was afterwards taken off by the order of the House."
Author: Zubayer Islam Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: Tschangho John Kim Publisher: EOLSS Publications ISBN: 1905839812 Category : Languages : en Pages : 292
Book Description
Transportation Engineering and Planning is a component of Encyclopedia of Physical Sciences, Engineering and Technology Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. The Theme on Transportation Engineering and Planning presents the readers with diverse sources of information and knowledge about transportation engineering and planning, to help ensure that informed actions are compatible with sustainable world development. It begins with a historical analysis of transportation development, since an understanding of how transportation technologies developed is a prerequisite for understanding issues involved in transportation systems, and for developing sound policy analysis. Next, the various chapters analyze transportation problems, discusses the state of public policy addressing those problems, considers the causes and effects of changes in demand for mobility as the socio-economic environment changes, and then deals with the fundamental questions related to transportation. These two volumes are aimed at the following a wide spectrum of audiences from the merely curious to those seeking in-depth knowledge: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.
Author: Tejswaroop Reddy Geetla
Author: Tejswaroop Reddy Geetla
Author: Lawrence A. Klein
Author: United States. Congress. Senate. Committee on Appropriations. Subcommittee on Dept. of Transportation and Related Agencies Appropriations
Author: Pradeep Kumar Singh
Author: Jiadi Yu
Author: United States. Congress. House
Author: 
Author: Zubayer Islam
Author: United States
Author: Tschangho John Kim