Cite this DOI

V David, V. D. (2022). Software Engineering: A Roadmap. *Journal of Science & Technology*, *7*(10), 86–92. https://doi.org/10.46243/jst.2022.v7.i10.pp86-92

@article{vdavid2022software,
  author    = {V David, V David},
  title     = {{Software Engineering: A Roadmap}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {dec},
  volume    = {7},
  number    = {10},
  pages     = {86--92},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i10.pp86-92},
  url       = {https://doi.org/10.46243/jst.2022.v7.i10.pp86-92},
  language  = {en},
  abstract  = {Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light -duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light -duty vehicles and thr ee light -duty trucks as a function of the vehicle‟s instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99 . Given that the models utilize the vehicle's instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including Intelligent Transportation Systems (ITS). The models developed in this study have been incorporated within the INTEGRATION microscopic traffic simulation model to further demonstrate their application and relevance to the transportation profession. Furthermore, these models have been utiliz ed in conjunction with Global Positioning System (GPS) speed measurements to evaluate the energy and environmental impacts of operational-level projects in the field}
}

TY  - JOUR
TI  - Software Engineering: A Roadmap
AU  - V David, V David
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/12/21/
VL  - 7
IS  - 10
SP  - 86
EP  - 92
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light -duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light -duty vehicles and thr ee light -duty trucks as a function of the vehicle‟s instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99 . Given that the models utilize the vehicle's instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including Intelligent Transportation Systems (ITS). The models developed in this study have been incorporated within the INTEGRATION microscopic traffic simulation model to further demonstrate their application and relevance to the transportation profession. Furthermore, these models have been utiliz ed in conjunction with Global Positioning System (GPS) speed measurements to evaluate the energy and environmental impacts of operational-level projects in the field
DO  - 10.46243/jst.2022.v7.i10.pp86-92
UR  - https://doi.org/10.46243/jst.2022.v7.i10.pp86-92
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i10.pp86-92",
    "title": "Software Engineering: A Roadmap",
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    "publisher": "Longman Publishers",
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    "abstract": "Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light -duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light -duty vehicles and thr ee light -duty trucks as a function of the vehicle‟s instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99 . Given that the models utilize the vehicle's instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including Intelligent Transportation Systems (ITS). The models developed in this study have been incorporated within the INTEGRATION microscopic traffic simulation model to further demonstrate their application and relevance to the transportation profession. Furthermore, these models have been utiliz ed in conjunction with Global Positioning System (GPS) speed measurements to evaluate the energy and environmental impacts of operational-level projects in the field",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i10.pp86-92 gives all four in one JSON answer.