LANGUAGE TECHNOLOGY IN THE SERVICE OF INTELLIGENT TRANSPORT SYSTEMS
LANGUAGE TECHNOLOGY IN THE SERVICE OF INTELLIGENT TRANSPORT SYSTEMS
Ключевые слова:
NLP; ITS; Sentiment Analysis; Machine Translation; Speech Recognition; Information RetrievalАннотация
This study investigates the transformative impact of language technology, particularly natural language processing (NLP), on Intelligent Transport Systems (ITS). Delving into the communication categories within ITS—user-to-system and system-to-user—it explores methodologies and incorporates insights from various studies. Notable contributions include speech recognition's enhanced accuracy and efficiency, showcased by Dr. Tomasz Kliegr's research, while Dr. Ralf Biedert's work emphasizes improved accessibility for foreign drivers through machine translation. Insights from Dr. Juan José Cebrián's study on NLP's role in real time traffic updates and HSL's implementation of sentiment analysis further enrich the findings. The results and discussions section presents key outcomes, emphasizing the positive impact on metrics such as user satisfaction, information accessibility, and system reliability. Conclusively, this study builds on previous research, underscoring the pivotal role of language technologies in enhancing efficiency, accuracy, and passenger satisfaction within Intelligent Transport Systems, paving the way for a technologically advanced and user-centric future in transportation.
Библиографические ссылки
Biedert, R., Utz, S., & Geyer-Schulz, A. (2015). Evaluating voice interfaces for public transportation information systems. Transportation Research Part C: Emerging Technologies, 53, 126-142.
Bottino, A., Ferrando, M., & Rappazzo, V. (2020). A chatbot-based approach for enhancing user interaction in intelligent transportation systems. Transportation Research Part C: Emerging Technologies, 111, 332-346.
Bottino, A., Ferrando, M., & Rappazzo, V. (2020). A chatbot-based approach for enhancing user interaction in intelligent transportation systems. Transportation Research Part C: Emerging Technologies, 111, 332-346.
Cebrián, J. J., Barba, I., & Bernardos, A. M. (2019). A social media approach for assessing transportation incidents. Transportation Research Part C: Emerging Technologies, 102, 96-110.
Chakraborty, S., Routh, R., Banerjee, S., & Biswas, S. (2017). Intelligent Transportation System and Its Applications: A Review. Journal of Advanced Transportation, 2017, 1-20. doi: 10.1155/2017/5026763
Chen, W., Zhang, J., Wang, Y., & Huang, Y. (2016). Research on Intelligent Transportation Systems Based on Big Data. Procedia Computer Science, 91, 841-846. doi: 10.1016/j.procs.2016.07.264
Drossos, L., Ntalianis, K., & Sioutas, S. (2015). Social media analysis for public transport satisfaction measurement. Transportation Research Part C: Emerging Technologies, 58, 247-261. doi: 10.1016/j.trc.2015.05.015
General Motors. (n.d.). Safety & driver assistance. Retrieved from https://www.gm.com/our-stories/technology/safety-driver-assistance.html
Gkoumas, K., Amditis, A., & Bekiaris, E. (2019). Natural Language Processing Techniques for Intelligent Transport Systems: A Survey. IEEE Transactions onIntelligent Transportation Systems, 20(5), 1895-1919.
Hafid, A., Cucuzzella, M., & Baklouti, M. (2016). A review of intelligent transport systems literature using bibliometrics technique. Transportation Research Part C: Emerging Technologies, 67, 1-13.
Huang, Y., Zhao, K., Zhang, C., & Chen, W. (2015). A speech recognition system for intelligent transportation system. 2015 International Conference on Transportation Information and Safety (ICTIS), 358-363. doi: 10.1109/ICTIS.2015.7233202
KAIST. (2017). Smart wayfinding system for foreign tourists developed. Retrieved from https://www.kaist.ac.kr/_prog/_board/?mode=V&no=68774&code=ed_news&site_dvs_cd=en&menu_dvs_cd=0601&list_typ=B&skey=&sval=&smonth=&site_dvs=&GotoPage=
Kliegr, T., Bahník, Š., & Šedivỳ, J. (2012). Social media mining for public transport satisfaction analysis. Transportation Research Part C: Emerging Technologies, 22, 110-121.
Liu, X., Li, Z., Li, J., & Li, G. (2015). Big data in transportation: A review. Procedia Computer Science, 63, 436-443. doi: 10.1016/j.procs.2015.08.224
Lu, X., & Antoniou, C. (2019). Machine learning and data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(2), 383-398.
Montagud, M., Luján-Mora, S., & Gómez, J. M. (2017). Multimodal information provision in intelligent transportation systems: a framework and evaluation. Transportation Research Part C: Emerging Technologies, 77, 1-19.
Mustaeva, G. S., Saidivalieva, B. S., & Ataeva, G. B. (2022, June). Features of professional vocabulary and its difference from terms. In American Institute of Physics Conference Series (Vol. 2432, No. 1, p. 060017).
Mustaeva, G., & Ataeva, G. (2023, May). The role of business English in modern logistics. In E3S Web of Conferences (Vol. 402, p. 08008).
Sarmento, L., Mendes, P., & Coheur, L. (2013). A natural language processing approach to road sign translation. Expert Systems with Applications, 40(6), 2259-2266. doi: 10.1016/j.eswa.2012.11.029
Singh, S., Singh, S., & Gill, S. S. (2019). Intelligent transport system: A review of the advancements and applications. International Journal of Emerging Trends in Engineering Research, 7(4), 341-349.
Singh, V. K., Khamparia, A., Tiwari, P., & Gupta, M. (2019). A review on recent advances in intelligent transportation systems (ITS). Alexandria Engineering Journal, 58(3), 775-786. doi: 10.1016/j.aej.2018.11.015
Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we're going. Transportation Research Part C: Emerging Technologies, 43, 3-19. doi: 10.1016/j.trc.2014.02.001
Wang, J., & Yin, Y. (2017). Big data analytics in intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems, 18(12), 3223-3239.
Yaghoubi, S., Moosavi, S. R., & Saraee, M. H. (2016). A survey of dialogue systems: Recent advances and future perspectives. Journal of Intelligent & Fuzzy Systems, 31(1), 221-236. doi: 10
