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TOPLU ULAŞIM ARAÇLARINDA ULAŞIM SÜRESİNİN TAHMİNİ

Year 2021, Volume: 4 Issue: 1, 119 - 128, 31.08.2021

Abstract

Günümüzde toplu ulaşımda, otobüsün ulaşım süresinin tahmini, bilgiye kolayca erişebilen ve günlük aktivitelerini planladıkları gibi yolculuklarını da planlamak isteyen yolcular için oldukça önemlidir. Büyük şehirlerde otobüslerin varış süresi bazı öngörülemeyen dış faktörler nedeniyle çeşitlilik göstermektedir. Bu nedenle bu çalışma, GPS cihazları ile toplanan veriyi kullanarak, güçlü ancak sade bir Makine Öğrenmesi tekniği sunmaktadır. Teknik, geçmiş veriden öğrenerek ve hava durumu, yoğun saatler, haftanın yoğun günleri ve yıllın yoğun günleri gibi etkenleri göz önünde bulundurarak her durak aralığı için gelecek verisini tahmin eden Çoklu Doğrusal Regrasyon algoritmasını kullanmaktadır. Tekniği doğrulamak amacı ile bir simulasyon modeli oluşturulmuştur. Simulasyon modeli geçmiş verinin ortalaması ve gerçek veri ile kıyaslanarak modelin doğruluğu ölçülmüştür. Sonuçlar tahmin tekniğinin ortalama modeline göre daha iyi performans gösterdiğini ve gerçek veriye en yakın tahmini yaptığını göstermiştir.

Thanks

Dr Ali Boyacı

References

  • Achar, A., Bharathi, D., Kumar, B. A., & Vanajakshi, L. (2020) Bus Arrival Time Prediction: A Spatial Kalman Filter Approach. IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1298-1307, doi: 10.1109/TITS.2019.2909314.
  • Choudhary, R., Khamparia, A., & Gahier, A. K. (2016) Real time prediction of bus arrival time: A review. 2016 2nd International Conference on Next GenerationComputing Technologies (NGCT), Dehradun, pp. 25-29, doi: 10.1109/NGCT.2016.7877384.
  • Hapsari, I., Surjandari, I., & Komarudin. (2018) Visiting Time Prediction Using Machine Learning Regression Algorithm. 2018 6th International Conference on Information and Communication Technology (ICoICT), Bandung, pp. 495-500, doi: 10.1109/ICoICT.2018.8528810.
  • He, P., Jiang, G., Lam, S., & Tang, D. (2019) Travel-Time Prediction of Bus Journey With Multiple Bus Trips. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 11, pp. 4192-4205, doi: 10.1109/TITS.2018.2883342.
  • Kwak, S., & Geroliminis, N. (2020) Travel Time Prediction for Congested Freeways With a Dynamic Linear Model. IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3006910.
  • Lin, D., Tsao, W., Yu, C., Liu, H., & Chang, Y. (2019) The Travel Time Prediction by Machine Learning Methods with Traffic Data in Chiayi City, Taiwan. 2019 4th International Conference on Electromechanical Control Technology and Transportation (ICECTT), Guilin, China, pp. 257-260, doi: 10.1109/ICECTT.2019.00065.
  • Pan, J., Dai, X., Xu, X., & Li, Y. (2012) A Self-learning algorithm for predicting bus arrival time based on historical data model. 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, Hangzhou, pp. 1112-1116, doi: 10.1109/CCIS.2012.6664555.
  • Rice, J., & van Zwet, E. (2001) A simple and effective method for predicting travel times on freeways. ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), Oakland, CA, pp. 227-232, doi: 10.1109/ITSC.2001.948660.
  • Tan, C., Park, S., Liu, H., Xu, Q., & Lau, P. (2008) Prediction of Transit Vehicle Arrival Time for Signal Priority Control: Algorithm and Performance. IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 4, pp. 688-696, doi: 10.1109/TITS.2008.2006799.
  • URL1 : Weather for 243 countries of the world, http://rp5.ru
  • Yang, J. S. (2005) Travel time prediction using the GPS test vehicle and Kalman filtering techniques. Proceedings of the 2005, American Control Conference, 2005, Portland, OR, USA, pp. 2128-2133 vol. 3, doi: 10.1109/ACC.2005.1470285.
  • Yu, H., Xiao, R., Du, Y., & He, Z. (2013) A Bus-Arrival Time Prediction Model Based on Historical Traffic Patterns. 2013 International Conference on Computer Sciences and Applications, Wuhan, pp. 345-349, doi: 10.1109/CSA.2013.87.

TRAVEL TIME PREDICTION IN PUBLIC TRANSPORTATION

Year 2021, Volume: 4 Issue: 1, 119 - 128, 31.08.2021

Abstract

Today, travel time prediction is essential for passengers who can easily access information and want to be able to plan their journeys as well as their daily activities. Travel time varies due to some unpredictable external factors especially in big cities. Therefore this paper proposes a powerful but simple Machine Learning (ML) model by using data collected by GPS devices. The model uses a Multiple Linear Regression algorithm that learns from historic data and predicts future data for each bus stop interval by considering external factors such as; weather condition, peak hours, busy week days and busy days of year. A simulation model was developed to validate the model. Then the simulation model was compared to average of historic data and real data. Results show that the prediction model outperforms the average model and calculates closest travel times to the real data.

References

  • Achar, A., Bharathi, D., Kumar, B. A., & Vanajakshi, L. (2020) Bus Arrival Time Prediction: A Spatial Kalman Filter Approach. IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1298-1307, doi: 10.1109/TITS.2019.2909314.
  • Choudhary, R., Khamparia, A., & Gahier, A. K. (2016) Real time prediction of bus arrival time: A review. 2016 2nd International Conference on Next GenerationComputing Technologies (NGCT), Dehradun, pp. 25-29, doi: 10.1109/NGCT.2016.7877384.
  • Hapsari, I., Surjandari, I., & Komarudin. (2018) Visiting Time Prediction Using Machine Learning Regression Algorithm. 2018 6th International Conference on Information and Communication Technology (ICoICT), Bandung, pp. 495-500, doi: 10.1109/ICoICT.2018.8528810.
  • He, P., Jiang, G., Lam, S., & Tang, D. (2019) Travel-Time Prediction of Bus Journey With Multiple Bus Trips. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 11, pp. 4192-4205, doi: 10.1109/TITS.2018.2883342.
  • Kwak, S., & Geroliminis, N. (2020) Travel Time Prediction for Congested Freeways With a Dynamic Linear Model. IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3006910.
  • Lin, D., Tsao, W., Yu, C., Liu, H., & Chang, Y. (2019) The Travel Time Prediction by Machine Learning Methods with Traffic Data in Chiayi City, Taiwan. 2019 4th International Conference on Electromechanical Control Technology and Transportation (ICECTT), Guilin, China, pp. 257-260, doi: 10.1109/ICECTT.2019.00065.
  • Pan, J., Dai, X., Xu, X., & Li, Y. (2012) A Self-learning algorithm for predicting bus arrival time based on historical data model. 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, Hangzhou, pp. 1112-1116, doi: 10.1109/CCIS.2012.6664555.
  • Rice, J., & van Zwet, E. (2001) A simple and effective method for predicting travel times on freeways. ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), Oakland, CA, pp. 227-232, doi: 10.1109/ITSC.2001.948660.
  • Tan, C., Park, S., Liu, H., Xu, Q., & Lau, P. (2008) Prediction of Transit Vehicle Arrival Time for Signal Priority Control: Algorithm and Performance. IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 4, pp. 688-696, doi: 10.1109/TITS.2008.2006799.
  • URL1 : Weather for 243 countries of the world, http://rp5.ru
  • Yang, J. S. (2005) Travel time prediction using the GPS test vehicle and Kalman filtering techniques. Proceedings of the 2005, American Control Conference, 2005, Portland, OR, USA, pp. 2128-2133 vol. 3, doi: 10.1109/ACC.2005.1470285.
  • Yu, H., Xiao, R., Du, Y., & He, Z. (2013) A Bus-Arrival Time Prediction Model Based on Historical Traffic Patterns. 2013 International Conference on Computer Sciences and Applications, Wuhan, pp. 345-349, doi: 10.1109/CSA.2013.87.
There are 12 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Betül Boylu

Ali Boyacı 0000-0002-2553-1911

Publication Date August 31, 2021
Submission Date January 18, 2021
Published in Issue Year 2021 Volume: 4 Issue: 1

Cite

APA Boylu, B., & Boyacı, A. (2021). TRAVEL TIME PREDICTION IN PUBLIC TRANSPORTATION. İstanbul Ticaret Üniversitesi Teknoloji Ve Uygulamalı Bilimler Dergisi, 4(1), 119-128.