Research Article
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Year 2022, , 541 - 555, 16.01.2023
https://doi.org/10.26650/JTL.2022.953913

Abstract

References

  • Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F. M., Preen, D. B., Stewart, L. M., & Fatovich, D. M. (2015). A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. Journal of Biomedical Informatics, 57, 62-73.
  • Alaoui, S. S., Farhaoui, Y., & Aksasse, B. (2017, April). A comparative study of the four well-known classification algorithms in data mining. In International Conference on Advanced Information Technology, Services and Systems (pp. 362-373). Springer, Cham.
  • Aroua, A., & Abdul-Nour, G. (2015). Forecast emergency room visits–a major diagnostic categories-based approach. International Journal of Metrology and Quality Engineering, 6(2), 204.
  • Aydin, N., Celik, E., & Gumus, A. T. (2015). A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul. Transportation Research Part A: Policy and Practice, 77, 61-81.
  • Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006). Bus arrival time prediction using support vector machines.
  • Journal of Intelligent Transportation Systems, 10(4), 151-158.
  • Butler, M. B., Gu, H., Kenney, T., & Campbell, S. G. (2016). P017: Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes. CJEM, 18(S1), S83-S84.
  • Cadenas, E., Jaramillo, O. A., & Rivera, W. (2010). Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method. Renewable Energy, 35(5), 925-930.
  • Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2), 109.
  • Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., & Baracli, H. (2013). An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review, 58, 28-51.
  • Champion, R., Kinsman, L. D., Lee, G. A., Masman, K. A., May, E. A., Mills, T. M., &Williams, R. J. (2007). Forecasting emergency department presentations. Australian Health Review, 31(1), 83-90.
  • Chang, H., Park, D., Lee, S., Lee, H., & Baek, S. (2010). Dynamic multi-interval bus travel time prediction using bus transit data. Transportmetrica A, 6(1), 19-38.
  • Chatfield, C., & Yar, M. (1988). Holt-Winters forecasting: some practical issues. The Statistician, 129-140.
  • Chen, Y., & Tjandra, S. (2014). Daily collision prediction with SARIMAX and generalized linear models on the basis of temporal and weather variables. Transportation Research Record: Journal of the Transportation Research Board, (2432), 26-36.
  • Chen, M., Liu, X., Xia, J., & Chien, S. I. (2004). A dynamic bus‐arrival time prediction model based on APC data. Computer‐Aided Civil and Infrastructure Engineering, 19(5), 364-376.
  • Chien, S. I. J., & Kuchipudi, C. M. (2003). Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering, 129(6), 608-616.
  • Chien, S. I. J., Ding, Y., & Wei, C. (2002). Dynamic bus arrival time prediction with artificial neural networks. Journal of Transportation Engineering, 128(5), 429-438.
  • Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38(9), 107-127.
  • Mai, Q., Aboagye‐Sarfo, P., Sanfilippo, F. M., Preen, D. B., & Fatovich, D. M. (2015). Predicting the number of emergency department presentations in Western Australia: A population‐based time series analysis. Emergency Medicine Australasia, 27(1), 16-21.
  • Papaioannou, G. P., Dikaiakos, C., Dramountanis, A., & Papaioannou, P. G. (2016). Analysis and modeling for short-to medium-term load forecasting using a hybrid manifold learning principal component model and comparison with classical statistical models (SARIMAX, Exponential Smoothing) and artificial intelligence models (ANN, SVM): The case of Greek electricity market. Energies, 9(8), 635.
  • Park, S. H., Jeong, Y. J., & Kim, T. J. (2007). Transit travel time forecasts for location-based queries. Journal of the Eastern Asia Society for Transportation Studies, 7, 1859-1869.
  • Rosychuk, R. J., Youngson, E., & Rowe, B. H. (2016). Presentations to emergency departments for COPD: A time series analysis. Canadian Respiratory Journal, 2016. http://dx.doi.org/10.1155/2016/1382434
  • S.I. Gass, C.M. Harris (Eds.), Encyclopedia of operations research and management science (Centennial edition), Kluwer, Dordrecht, The Netherlands (2000).
  • Serin, F., & Süleyman, M., (2019). Public transportation graph: A graph theoretical model of public transportation network for efficient trip planning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(4), 468-472.
  • Serin, F., Mete, S., Gul, M., & Celik, E. (2020). Deep Learning for Prediction of Bus Arrival Time in Public Transportation. In Logistics 4.0 (pp. 126-135). CRC Press.
  • Serin, F., Mete, S., & Ozceylan, E. (2021). Graph Traversal-based Solutions for Trip Planning in Public Transportation Graph. In 2021 International Conference on Information Technology (ICIT) (pp. 190-194). IEEE.
  • Shalaby, A., & Farhan, A. (2004). Prediction model of bus arrival and departure times using AVL and APC data. Journal of Public Transportation, 7(1), 41-61.
  • Shumway, R. H., & Stoffer, D. S. (2000). Time series analysis and its applications. Studies In Informatics And Control, 9(4), 75-163.
  • Tratar, L. F., Mojškerc, B., & Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162-173.
  • Tripathi, R., Nayak, A. K., Raja, R., Shahid, M., Kumar, A., Mohanty, S., ... & Gautam, P. (2014). Forecasting rice productivity and production of Odisha, India, using autoregressive integrated moving average models. Advances in Agriculture, 2014.
  • Van Hinsbergen, C. I., Van Lint, J. W. C., & Van Zuylen, H. J. (2009). Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C: Emerging Technologies, 17(5), 498-509.
  • Wei, W., Jiang, J., Liang, H., Gao, L., Liang, B., Huang, J., ... & Qin, F. (2016). Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng county, China. PloS one, 11(6), e0156768.
  • Xu, H., & Ying, J. (2017). Bus arrival time prediction with real-time and historic data. Cluster Computing, 20(4), 3099-3106.
  • Xu, Q., Tsui, K. L., Jiang, W., & Guo, H. (2016). A hybrid approach for forecasting patient visits in emergency department. Quality and Reliability Engineering International, 32(8), 2751-2759.
  • Yang, D., Sharma, V., Ye, Z., Lim, L. I., Zhao, L., & Aryaputera, A. W. (2015). Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy, 81, 111-119.
  • Yang, M., Chen, C., Wang, L., Yan, X., & Zhou, L. (2016). Bus arrival time prediction using support vector machine with genetic algorithm. Neural Network World, 26(3), 205-217.
  • Yanık, S., Aktas, E., & Topcu, Y. I. (2017). Traveler satisfaction in rapid rail systems: The case of Istanbul metro. International Journal of Sustainable Transportation, 11(9), 642-658.
  • Yap, M. D., Nijënstein, S., & van Oort, N. (2018). Improving predictions of public transport usage during disturbances based on smart card data. Transport Policy, 61, 84-95.
  • Yu, B., Lam, W. H., & Tam, M. L. (2011). Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C: Emerging Technologies, 19(6), 1157-1170.
  • Yuan, C., Liu, S., & Fang, Z. (2016). Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model. Energy, 100, 384-390.
  • Yucesan, M., Gul, M., & Celik, E. (2018). Performance comparison between ARIMAX, ANN and ARIMAX- ANN hybridization in sales forecasting for furniture industry. Drvna industrija: Znanstveni časopis za pitanja drvne tehnologije, 69(4), 357-370.
  • Zhang, G., Zhang, X., & Feng, H. (2016). Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion. Expert Systems, 33(5), 501-516.
  • Zhang, X., Yan, M., Xie, B., Yang, H., & Ma, H. (2021). An automatic real-time bus schedule redesign method based on bus arrival time prediction. Advanced Engineering Informatics, 48, 101295.
  • Zibners, L. M., Bonsu, B. K., Hayes, J. R., & Cohen, D. M. (2006). Local weather effects on emergency department visits: a time series and regression analysis. Pediatric Emergency Care, 22(2), 104-106.

Predicting the Time of Bus Arrival for Public Transportation by Time Series Models

Year 2022, , 541 - 555, 16.01.2023
https://doi.org/10.26650/JTL.2022.953913

Abstract

Bus arrival time prediction is a key factor in passenger satisfaction and bus usage. Bus arrival time information reduces both passenger anxiety and their waiting time at the bus stop. Therefore, giving passengers accurate bus arrival time information is very important in public transportation. Various time series prediction methods are used for bus arrival time in this paper. Moreover, five different performance measurements are considered to assess the accuracy of the prediction models. A case study is presented using real data from Istanbul, Turkey for the proposed models. The models predict bus arrival time on a route for its different segments. The results of the proposed models are compared according to performance measures. The model with the best accuracy result among the eight prediction models can support service operators and the authorities in obtaining better passenger satisfaction.

References

  • Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F. M., Preen, D. B., Stewart, L. M., & Fatovich, D. M. (2015). A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. Journal of Biomedical Informatics, 57, 62-73.
  • Alaoui, S. S., Farhaoui, Y., & Aksasse, B. (2017, April). A comparative study of the four well-known classification algorithms in data mining. In International Conference on Advanced Information Technology, Services and Systems (pp. 362-373). Springer, Cham.
  • Aroua, A., & Abdul-Nour, G. (2015). Forecast emergency room visits–a major diagnostic categories-based approach. International Journal of Metrology and Quality Engineering, 6(2), 204.
  • Aydin, N., Celik, E., & Gumus, A. T. (2015). A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul. Transportation Research Part A: Policy and Practice, 77, 61-81.
  • Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006). Bus arrival time prediction using support vector machines.
  • Journal of Intelligent Transportation Systems, 10(4), 151-158.
  • Butler, M. B., Gu, H., Kenney, T., & Campbell, S. G. (2016). P017: Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes. CJEM, 18(S1), S83-S84.
  • Cadenas, E., Jaramillo, O. A., & Rivera, W. (2010). Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method. Renewable Energy, 35(5), 925-930.
  • Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2), 109.
  • Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., & Baracli, H. (2013). An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review, 58, 28-51.
  • Champion, R., Kinsman, L. D., Lee, G. A., Masman, K. A., May, E. A., Mills, T. M., &Williams, R. J. (2007). Forecasting emergency department presentations. Australian Health Review, 31(1), 83-90.
  • Chang, H., Park, D., Lee, S., Lee, H., & Baek, S. (2010). Dynamic multi-interval bus travel time prediction using bus transit data. Transportmetrica A, 6(1), 19-38.
  • Chatfield, C., & Yar, M. (1988). Holt-Winters forecasting: some practical issues. The Statistician, 129-140.
  • Chen, Y., & Tjandra, S. (2014). Daily collision prediction with SARIMAX and generalized linear models on the basis of temporal and weather variables. Transportation Research Record: Journal of the Transportation Research Board, (2432), 26-36.
  • Chen, M., Liu, X., Xia, J., & Chien, S. I. (2004). A dynamic bus‐arrival time prediction model based on APC data. Computer‐Aided Civil and Infrastructure Engineering, 19(5), 364-376.
  • Chien, S. I. J., & Kuchipudi, C. M. (2003). Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering, 129(6), 608-616.
  • Chien, S. I. J., Ding, Y., & Wei, C. (2002). Dynamic bus arrival time prediction with artificial neural networks. Journal of Transportation Engineering, 128(5), 429-438.
  • Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38(9), 107-127.
  • Mai, Q., Aboagye‐Sarfo, P., Sanfilippo, F. M., Preen, D. B., & Fatovich, D. M. (2015). Predicting the number of emergency department presentations in Western Australia: A population‐based time series analysis. Emergency Medicine Australasia, 27(1), 16-21.
  • Papaioannou, G. P., Dikaiakos, C., Dramountanis, A., & Papaioannou, P. G. (2016). Analysis and modeling for short-to medium-term load forecasting using a hybrid manifold learning principal component model and comparison with classical statistical models (SARIMAX, Exponential Smoothing) and artificial intelligence models (ANN, SVM): The case of Greek electricity market. Energies, 9(8), 635.
  • Park, S. H., Jeong, Y. J., & Kim, T. J. (2007). Transit travel time forecasts for location-based queries. Journal of the Eastern Asia Society for Transportation Studies, 7, 1859-1869.
  • Rosychuk, R. J., Youngson, E., & Rowe, B. H. (2016). Presentations to emergency departments for COPD: A time series analysis. Canadian Respiratory Journal, 2016. http://dx.doi.org/10.1155/2016/1382434
  • S.I. Gass, C.M. Harris (Eds.), Encyclopedia of operations research and management science (Centennial edition), Kluwer, Dordrecht, The Netherlands (2000).
  • Serin, F., & Süleyman, M., (2019). Public transportation graph: A graph theoretical model of public transportation network for efficient trip planning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(4), 468-472.
  • Serin, F., Mete, S., Gul, M., & Celik, E. (2020). Deep Learning for Prediction of Bus Arrival Time in Public Transportation. In Logistics 4.0 (pp. 126-135). CRC Press.
  • Serin, F., Mete, S., & Ozceylan, E. (2021). Graph Traversal-based Solutions for Trip Planning in Public Transportation Graph. In 2021 International Conference on Information Technology (ICIT) (pp. 190-194). IEEE.
  • Shalaby, A., & Farhan, A. (2004). Prediction model of bus arrival and departure times using AVL and APC data. Journal of Public Transportation, 7(1), 41-61.
  • Shumway, R. H., & Stoffer, D. S. (2000). Time series analysis and its applications. Studies In Informatics And Control, 9(4), 75-163.
  • Tratar, L. F., Mojškerc, B., & Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162-173.
  • Tripathi, R., Nayak, A. K., Raja, R., Shahid, M., Kumar, A., Mohanty, S., ... & Gautam, P. (2014). Forecasting rice productivity and production of Odisha, India, using autoregressive integrated moving average models. Advances in Agriculture, 2014.
  • Van Hinsbergen, C. I., Van Lint, J. W. C., & Van Zuylen, H. J. (2009). Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C: Emerging Technologies, 17(5), 498-509.
  • Wei, W., Jiang, J., Liang, H., Gao, L., Liang, B., Huang, J., ... & Qin, F. (2016). Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng county, China. PloS one, 11(6), e0156768.
  • Xu, H., & Ying, J. (2017). Bus arrival time prediction with real-time and historic data. Cluster Computing, 20(4), 3099-3106.
  • Xu, Q., Tsui, K. L., Jiang, W., & Guo, H. (2016). A hybrid approach for forecasting patient visits in emergency department. Quality and Reliability Engineering International, 32(8), 2751-2759.
  • Yang, D., Sharma, V., Ye, Z., Lim, L. I., Zhao, L., & Aryaputera, A. W. (2015). Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy, 81, 111-119.
  • Yang, M., Chen, C., Wang, L., Yan, X., & Zhou, L. (2016). Bus arrival time prediction using support vector machine with genetic algorithm. Neural Network World, 26(3), 205-217.
  • Yanık, S., Aktas, E., & Topcu, Y. I. (2017). Traveler satisfaction in rapid rail systems: The case of Istanbul metro. International Journal of Sustainable Transportation, 11(9), 642-658.
  • Yap, M. D., Nijënstein, S., & van Oort, N. (2018). Improving predictions of public transport usage during disturbances based on smart card data. Transport Policy, 61, 84-95.
  • Yu, B., Lam, W. H., & Tam, M. L. (2011). Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C: Emerging Technologies, 19(6), 1157-1170.
  • Yuan, C., Liu, S., & Fang, Z. (2016). Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model. Energy, 100, 384-390.
  • Yucesan, M., Gul, M., & Celik, E. (2018). Performance comparison between ARIMAX, ANN and ARIMAX- ANN hybridization in sales forecasting for furniture industry. Drvna industrija: Znanstveni časopis za pitanja drvne tehnologije, 69(4), 357-370.
  • Zhang, G., Zhang, X., & Feng, H. (2016). Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion. Expert Systems, 33(5), 501-516.
  • Zhang, X., Yan, M., Xie, B., Yang, H., & Ma, H. (2021). An automatic real-time bus schedule redesign method based on bus arrival time prediction. Advanced Engineering Informatics, 48, 101295.
  • Zibners, L. M., Bonsu, B. K., Hayes, J. R., & Cohen, D. M. (2006). Local weather effects on emergency department visits: a time series and regression analysis. Pediatric Emergency Care, 22(2), 104-106.
There are 44 citations in total.

Details

Primary Language English
Subjects Operation
Journal Section Research Article
Authors

Süleyman Mete 0000-0001-7631-5584

Erkan Çelik 0000-0003-4465-0913

Muhammet Gül 0000-0002-5319-4289

Publication Date January 16, 2023
Submission Date June 17, 2021
Acceptance Date October 28, 2022
Published in Issue Year 2022

Cite

APA Mete, S., Çelik, E., & Gül, M. (2023). Predicting the Time of Bus Arrival for Public Transportation by Time Series Models. Journal of Transportation and Logistics, 7(2), 541-555. https://doi.org/10.26650/JTL.2022.953913
AMA Mete S, Çelik E, Gül M. Predicting the Time of Bus Arrival for Public Transportation by Time Series Models. JTL. January 2023;7(2):541-555. doi:10.26650/JTL.2022.953913
Chicago Mete, Süleyman, Erkan Çelik, and Muhammet Gül. “Predicting the Time of Bus Arrival for Public Transportation by Time Series Models”. Journal of Transportation and Logistics 7, no. 2 (January 2023): 541-55. https://doi.org/10.26650/JTL.2022.953913.
EndNote Mete S, Çelik E, Gül M (January 1, 2023) Predicting the Time of Bus Arrival for Public Transportation by Time Series Models. Journal of Transportation and Logistics 7 2 541–555.
IEEE S. Mete, E. Çelik, and M. Gül, “Predicting the Time of Bus Arrival for Public Transportation by Time Series Models”, JTL, vol. 7, no. 2, pp. 541–555, 2023, doi: 10.26650/JTL.2022.953913.
ISNAD Mete, Süleyman et al. “Predicting the Time of Bus Arrival for Public Transportation by Time Series Models”. Journal of Transportation and Logistics 7/2 (January 2023), 541-555. https://doi.org/10.26650/JTL.2022.953913.
JAMA Mete S, Çelik E, Gül M. Predicting the Time of Bus Arrival for Public Transportation by Time Series Models. JTL. 2023;7:541–555.
MLA Mete, Süleyman et al. “Predicting the Time of Bus Arrival for Public Transportation by Time Series Models”. Journal of Transportation and Logistics, vol. 7, no. 2, 2023, pp. 541-55, doi:10.26650/JTL.2022.953913.
Vancouver Mete S, Çelik E, Gül M. Predicting the Time of Bus Arrival for Public Transportation by Time Series Models. JTL. 2023;7(2):541-55.



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