88 lines
3.9 KiB
BibTeX
88 lines
3.9 KiB
BibTeX
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@Article{ai_in_transport,
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AUTHOR = {Abduljabbar, Rusul and Dia, Hussein and Liyanage, Sohani and Bagloee, Saeed Asadi},
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TITLE = {Applications of Artificial Intelligence in Transport: An Overview},
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JOURNAL = {Sustainability},
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VOLUME = {11},
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YEAR = {2019},
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NUMBER = {1},
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ARTICLE-NUMBER = {189},
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URL = {https://www.mdpi.com/2071-1050/11/1/189},
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ISSN = {2071-1050},
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ABSTRACT = {The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.},
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DOI = {10.3390/su11010189}
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}
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@misc{ztm_dataset,
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title={ZTM timetables public data},
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year={2023},
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url={https://www.ztm.poznan.pl/pl/dla-deweloperow/index}
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}
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@misc{go_csv,
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title={Go CSV parser},
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url={https://pkg.go.dev/encoding/csv}
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}
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@article{scikit_learn,
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title={Scikit-learn: Machine Learning in {P}ython},
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author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
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and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
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and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
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Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
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journal={Journal of Machine Learning Research},
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volume={12},
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pages={2825--2830},
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year={2011}
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}
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@misc{tensorflow2015-whitepaper,
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title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
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url={https://www.tensorflow.org/},
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note={Software available from tensorflow.org},
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author={
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Mart\'{i}n~Abadi and
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Ashish~Agarwal and
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Paul~Barham and
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Eugene~Brevdo and
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Zhifeng~Chen and
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Craig~Citro and
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Greg~S.~Corrado and
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Andy~Davis and
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Jeffrey~Dean and
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Matthieu~Devin and
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Sanjay~Ghemawat and
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Ian~Goodfellow and
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Andrew~Harp and
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Geoffrey~Irving and
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Michael~Isard and
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Yangqing Jia and
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Rafal~Jozefowicz and
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Lukasz~Kaiser and
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Manjunath~Kudlur and
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Josh~Levenberg and
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Dandelion~Man\'{e} and
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Rajat~Monga and
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Sherry~Moore and
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Derek~Murray and
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Chris~Olah and
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Mike~Schuster and
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Jonathon~Shlens and
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Benoit~Steiner and
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Ilya~Sutskever and
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Kunal~Talwar and
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Paul~Tucker and
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Vincent~Vanhoucke and
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Vijay~Vasudevan and
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Fernanda~Vi\'{e}gas and
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Oriol~Vinyals and
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Pete~Warden and
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Martin~Wattenberg and
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Martin~Wicke and
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Yuan~Yu and
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Xiaoqiang~Zheng},
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year={2015},
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}
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