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001 9781003217367
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006 m o d
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008 220302s2022 xx eo 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9781003217367
_q(electronic bk.)
020 _a1003217362
_q(electronic bk.)
020 _a9781000555981
_q(electronic bk. : EPUB)
020 _a1000555984
_q(electronic bk. : EPUB)
020 _a9781000555905
_q(electronic bk. : PDF)
020 _a1000555909
_q(electronic bk. : PDF)
020 _z9781032108513
020 _z9781032228044
024 7 _a10.1201/9781003217367
_2doi
035 _a(OCoLC)1301430967
035 _a(OCoLC-P)1301430967
050 4 _aTE228.3
072 7 _aCOM
_x091000
_2bisacsh
072 7 _aTEC
_x009140
_2bisacsh
072 7 _aUB
_2bicssc
082 0 4 _a388
_223/eng/20220103
100 1 _aSathiyaraj, R.,
_eauthor.
245 1 0 _aAdvanced intelligent predictive models for urban transportation /
_cDr. R. Sathiyaraj Assistant Professor, Department of CSE, School of Engineering and Technology, CMR University, Bangalore, Karnataka, India, Dr. A. Bharathi Professor, Department of IT, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India, Dr. B. Balamurugan Professor, School of CSE, Galgotias University, Greater Noida, Uttar Pradesh, India.
250 _aFirst edition.
264 1 _a[Place of publication not identified] :
_bChapman and Hall/CRC,
_c2022.
300 _a1 online resource (xii, 132 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aPreface--Authors--1 Overview--2 Related Works--3 Smart Traffic Prediction and Congestion Avoidance System (S-TPCA) UsingGenetic Predictive Models for Urban Transportation--4 Short-Term Traffic Prediction Model (STTPM)--5 An Efficient Intelligent Traffic Light Control and Deviation System--6 IoT-Based Intelligent Transportation System (IoT-ITS)--7 Intelligent Traffic Light Control and Ambulance Control System--8 Conclusions and Future Research--Bibliography--Index--
520 _a"Advanced intelligent predictive models for urban transportation emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. It illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: provides a smart traffic congestion avoidance system with an integrated fuel consumption model, predicts traffic in short-term and regular (this is illustrated with a case study), explores efficient traffic light controller and deviation system in accordance with the traffic scenario, considers IoT based Intelligent Transport Systems in a Global perspective and intelligent Traffic Light Control System and Ambulance Control System. The text also provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. In addition to that, this book focuses on advanced predictive models along with offering an efficient solution for smart traffic management systems. It is a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. Advanced intelligent predictive models for urban transportation is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aIntelligent transportation systems.
650 0 _aTraffic estimation
_xMathematical models.
650 0 _aUrban transportation
_xPlanning
_xStatistical methods.
700 1 _aBharathi, A.,
_eauthor.
700 1 _aBalusamy, Balamurugan,
_eauthor.
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003217367
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c5079
_d5079