Data Driven Approaches For Robust Signal Plans In Urban Transportation Networks
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Data Driven Approaches For Robust Signal Plans In Urban Transportation Networks
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Author : Zahra Amini
language : en
Publisher:
Release Date : 2018
Data Driven Approaches For Robust Signal Plans In Urban Transportation Networks written by Zahra Amini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
In urban transportation networks with signalized intersections a robust pre-timed signal plan is a practical alternative to adaptive control strategies, since it has less complexity and an easier implementation process. Recent advances in technology are making data collection at traffic signals economical and data-driven approaches are likely to benefit from the large traffic data. Data-driven approaches are necessary for designing robust timing plans that can satisfy rapid traffic volume fluctuation and demand growth. This dissertation introduces four data-driven approaches for studying and improving traffic conditions at signalized intersections. Firstly, I discuss the development and testing of two algorithms for checking the quality of traffic data and for estimating performance measures at intersections. The first of these algorithms estimates the systematic error of the detector data at signalized intersections by using flow conservation. According to the ground truth data from a real-world network, the algorithm can reduce the error in the data up to 25%. The second algorithm helps in estimating intersection performance measures in real-time by measuring the number of the vehicles in each approach using high resolution(HR) data. An offset optimization algorithm was developed to adjust signal offsets so as to improve the delay in the system. The performance of three real-world networks using the offsets obtained by the algorithm and those obtained from the widely used Synchro optimization tool, are compared using the VISSIM microscopic simulation model. Simulation results show up to a 30% reduction in the average number of stops and total delay that vehicles experience along the major routes when using the proposed algorithms’ optimized offsets. The fourth algorithm estimates the appropriate switching time between designed timing plans during the day based on the traffic profile of the intersection by using the K-means clustering method. In conclusion, these four algorithms extract useful information from HR data about traffic at signalized intersections. Moreover, the algorithms assist in designing robust timing plans for satisfying demand fluctuations at signalized intersection. Lastly, simulation results from real-world networks illustrate the significant improvements that the proposed data-driven approaches can make in the control systems at urban transportation networks.
Mobility Data Driven Urban Traffic Monitoring
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Author : Zhidan Liu
language : en
Publisher: Springer Nature
Release Date : 2021-05-18
Mobility Data Driven Urban Traffic Monitoring written by Zhidan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-18 with Computers categories.
This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.
Selected Papers From Iikii 2019 Conferences In Symmetry
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Author : Teen-Hang Meen
language : en
Publisher: MDPI
Release Date : 2020-12-15
Selected Papers From Iikii 2019 Conferences In Symmetry written by Teen-Hang Meen and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-15 with Technology & Engineering categories.
The International Institute of Knowledge Innovation and Invention (IIKII, http://www.iikii.org) promotes the exchange of innovations and inventions and establishes a communication platform for international innovations and research. In 2019, IIKII cooperates with the IEEE Tainan Section Sensors Council to hold IEEE conferences, such as IEEE ICIASE 2019, IEEE ECBIOS 2019, IEEE ICKII 2019, ICUSA-GAME 2019, and IEEE ECICE 2019. This Special Issue, entitled "Selected Papers from IIKII 2019 conferences", aims to showcase outstanding papers from IIKII 2019 conferences, including symmetry in physics, chemistry, biology, mathematics, and computer science, etc. It selected 21 outstanding papers from 750 papers presented in IIKII 2019 conferences on the topic of symmetry. The main goals of this Special Issue are to encourage scientists to publish their experimental and theoretical results in as much detail as possible, and to discover new scientific knowledge relevant to the topic of symmetry.
Urban Transportation Networks
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Author : Yosef Sheffi
language : en
Publisher: Prentice Hall
Release Date : 1984
Urban Transportation Networks written by Yosef Sheffi and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Political Science categories.
Data Driven Methods For Improved Estimation And Control Of An Urban Arterial Traffic Network
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Author : Leah Adrian Anderson
language : en
Publisher:
Release Date : 2015
Data Driven Methods For Improved Estimation And Control Of An Urban Arterial Traffic Network written by Leah Adrian Anderson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Transportation is a field which is universal in our society: people from every country, culture or background are familiar with the challenges of getting around in our built environment. Yet what is not always so obvious to the average traveler is how the techniques and tools of designing, observing, and controlling our modern transportation networks are derived. In fact, the theory of traffic engineering has many gaps and unknowns that are the topic of ongoing research efforts in the academic community. This work presents a collection of theoretical and practical methodologies to advance the study of traffic flow modeling, state estimation, and control of signalized roadways in particular. It uses theory from traditional transportation engineering, but also demonstrates the application of new tools from control theory and computer science to the specific application of signalized traffic networks. First, two numerical modeling dynamics representing traffic flows on signalized arterials are presented: the well-known Cell Transmission Model, a discretization of the physical hydrodynamic laws believed to govern vehicle flows, and a new Vertical Cell Model which resembles classical "store-and-forward" models with the addition of transit delays and finite buffer capacities. Each of these models is implemented in a common software framework, which provides an ideal experimental platform for direct comparison of the competing dynamics. A chapter in this dissertation contributes a validation and comparison of the two models against real vehicle trajectory data on an existing signalized road network. Accuracy and confidence in such traffic models requires complimentary methods of observing true traffic conditions to provide initial conditions and real-time state estimates. Yet there are many technological deficiencies in existing urban roadway detection systems that prevent the acquisition of a real-time estimate of arterial link state (or queue length) at signalized intersections. Hence this thesis also contains methodology to improve the estimates obtained from existing hardware by combining data from typical infrastructure sensors with new sources of Lagrangian probe measurements. These are then assimilated into a detailed model of flow dynamics. This technique was previously proposed for continuous-flow (freeway) networks, but required novel adaptions to be applied to an interrupted-flow setting. This dissertation next explores advancements in theoretically optimal control algorithms for statistically-modeled signalized queueing networks. In the context of a large body of previous work on flow-impeding control for vertical queueing networks, the practical challenges of traffic signal control are highlighted. Some of these challenges are tackled in the specific case of the max pressure controller, an algorithm derived from the field of communications networks that has been shown to optimize through-flow in an idealized network model. The lack of adequate measurements or demand-volume data has historically been a major limitation in advancing research on signalized arterial road networks. Yet the current revolution of inexpensive storage and processing of "big data" shows promise for improving daily operations of existing roadways without the need for expensive new hardware systems. One example of this potential appears is the case of traffic signal control. Existing traffic signals are capable of operating more efficiently by changing signal plans based on real-time demand measurements through a traffic responsive plan selection (TRPS) mode of operation (rather than depending on a rigid schedule for plan changes). However, this mode is rarely used in practice because its calibration process is not accessible or intuitive to traffic technicians. This dissertation presents an application of statistical learning techniques to improve the process of calibrating and implementing an existing TRPS mechanism. A proof-of-concept implementation using historical sensor data from a busy urban intersection demonstrates that real operational improvements may be immediately achievable using existing sensing infrastructure.
Data Driven Real Time Traffic Signal Optimization
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Author : Stephen David Boyles
language : en
Publisher:
Release Date : 2020
Data Driven Real Time Traffic Signal Optimization written by Stephen David Boyles and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Logic Driven Traffic Big Data Analytics
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Author : Shaopeng Zhong
language : en
Publisher: Springer
Release Date : 2023-02-03
Logic Driven Traffic Big Data Analytics written by Shaopeng Zhong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-03 with Business & Economics categories.
This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.
Identifying Quantifying And Proving Loss Of Productivity
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Author : American Society of Civil Engineers
language : en
Publisher:
Release Date : 2021
Identifying Quantifying And Proving Loss Of Productivity written by American Society of Civil Engineers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Building failures categories.
"MOP 144 provides guidance and underlying framework for creating consistency across hazards, systems, and sectors in the design of new infrastructure systems and in enhancing the resilience of existing ones"--
International Encyclopedia Of Transportation
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Author :
language : en
Publisher: Elsevier
Release Date : 2021-05-13
International Encyclopedia Of Transportation written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-13 with Law categories.
In an increasingly globalised world, despite reductions in costs and time, transportation has become even more important as a facilitator of economic and human interaction; this is reflected in technical advances in transportation systems, increasing interest in how transportation interacts with society and the need to provide novel approaches to understanding its impacts. This has become particularly acute with the impact that Covid-19 has had on transportation across the world, at local, national and international levels. Encyclopedia of Transportation, Seven Volume Set - containing almost 600 articles - brings a cross-cutting and integrated approach to all aspects of transportation from a variety of interdisciplinary fields including engineering, operations research, economics, geography and sociology in order to understand the changes taking place. Emphasising the interaction between these different aspects of research, it offers new solutions to modern-day problems related to transportation. Each of its nine sections is based around familiar themes, but brings together the views of experts from different disciplinary perspectives. Each section is edited by a subject expert who has commissioned articles from a range of authors representing different disciplines, different parts of the world and different social perspectives. The nine sections are structured around the following themes: Transport Modes; Freight Transport and Logistics; Transport Safety and Security; Transport Economics; Traffic Management; Transport Modelling and Data Management; Transport Policy and Planning; Transport Psychology; Sustainability and Health Issues in Transportation. Some articles provide a technical introduction to a topic whilst others provide a bridge between topics or a more future-oriented view of new research areas or challenges. The end result is a reference work that offers researchers and practitioners new approaches, new ways of thinking and novel solutions to problems. All-encompassing and expertly authored, this outstanding reference work will be essential reading for all students and researchers interested in transportation and its global impact in what is a very uncertain world. Provides a forward looking and integrated approach to transportation Updated with future technological impacts, such as self-driving vehicles, cyber-physical systems and big data analytics Includes comprehensive coverage Presents a worldwide approach, including sets of comparative studies and applications
Data Driven Solutions To Transportation Problems
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Author : Yinhai Wang
language : en
Publisher: Elsevier
Release Date : 2018-12-04
Data Driven Solutions To Transportation Problems written by Yinhai Wang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-04 with Transportation categories.
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. Synthesizes the newest developments in data-driven transportation science Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed Useful for both theoretical and technically-oriented researchers