Research Article
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Year 2020, Volume: 1 Issue: 2, 1202 - , 31.12.2020

Buket Çapalı Halim Ceylan

Abstract

References

  • Yu, B., Yang, Z., & Li, S. (2012). Real-time partway deadheading strategy based on transit service reliability assessment. Transportation Research Part A: Policy and Practice, 46(8), 1265-1279. https://doi.org/10.1016/j.tra.2012.05.009
  • 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. https://doi.org/10.1061/(ASCE)0733947X(2002)128:5
  • 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. https://doi.org/10.1111/j.1467-8667.2004.00363.x
  • Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006). Bus arrival time prediction using support vector machines. Journal of Intelligent Transportation Systems, 10(4), 151-158. https://doi.org/10.1080/15472450600981009
  • Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. Ieee Transactions on Intelligent Transportation Systems, 5(4), 276-281. https://doi.org/10.1109/TITS.2004.837813
  • Smith, B. L., and Demetsky, M. J. (1995). Short-term traffic flow prediction: Neural network approach. Transportation Research Record 1453, 98–104.
  • Hellinga, B. R., & Fu, L. (2002). Reducing bias in probe-based arterial link travel time estimates. Transportation Research Part C: Emerging Technologies, 10(4), 257-273. https://doi.org/10.1016/S0968-090X(02)00003-7
  • Cathey, F. W., & Dailey, D. J. (2003). A prescription for transit arrival/departure prediction using automatic vehicle location data. Transportation Research Part C: Emerging Technologies, 11(3-4), 241-264. https://doi.org/10.1016/S0968-090X(03)00023-8
  • Yu, B., Ye, T., Tian, X. M., Ning, G. B., & Zhong, S. Q. (2014). Bus travel-time prediction with a forgetting factor. Journal of Computing in Civil Engineering, 28(3), 06014002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000274
  • Mazloumi, E., Moridpour, S., Currie, G., & Rose, G. (2012). Exploring the value of traffic flow data in bus travel time prediction. Journal of Transportation Engineering, 138(4), 436-446. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000329
  • Chang, G. L., Vasudevan, M., & Su, C. C. (1996). Modelling and evaluation of adaptive bus-preemption control with and without automatic vehicle location systems. Transportation Research Part A: Policy and Practice, 30(4), 251-268. https://doi.org/10.1016/0965-8564(95)00026-7
  • Strathman, J. G., Dueker, K. J., Kimpel, T., Gerhart, R., Turner, K., Taylor, P., ... & Hopper, J. (1999). Automated bus dispatching, operations control, and service reliability: Baseline analysis. Transportation Research Record, 1666(1), 28-36. https://doi.org/10.3141/1666-04
  • Kimpel, T. J. (2002). Time point-level analysis of transit service reliability and passenger demand. Ph.D. dissertation, Portland State Univ., Portland, OR.
  • Strathman, J. G., Kimpel, T. J., Dueker, K. J., Gerhart, R. L., & Callas, S. (2002). Evaluation of transit operations: Data applications of Tri-Met's automated bus dispatching system. Transportation, 29(3), 321-345. https://doi.org/10.1023/A:1015633408953
  • Sun, A. (2005). AVL-based transit operations control. Ph.D. dissertation, Univ. of Arizona, Tucson, AZ. https://repository.arizona.edu/bitstream/handle/10150/194895/azu_etd_1039_sip1_m.pdf?sequence=1
  • Pangilinan, C., Wilson, N., & Moore, A. (2008). Bus supervision deployment strategies and use of real-time automatic vehicle location for improved bus service reliability. Transportation Research Record, 2063(1), 28-33. https://doi.org/10.3141%2F2063-04
  • Shalaby, A., & Farhan, A. (2004). Prediction model of bus arrival and departure times using AVL and APC data. Journal of Public Transportation, 7(1), 3. http://doi.org/10.5038/2375-0901.7.1.3
  • Patnaik, J., Chien, S., & Bladikas, A. (2004). Estimation of bus arrival times using APC data. Journal of Public Transportation, 7(1), 1. http://doi.org/10.5038/2375-0901.7.1.1
  • Sun, D., Luo, H., Fu, L., Liu, W., Liao, X., & Zhao, M. (2007). Predicting bus arrival time on the basis of global positioning system data. Transportation Research Record, 2034(1), 62-72. https://doi.org/10.3141/2034-08
  • Chen, M., Yaw, J., Chien, S. I., & Liu, X. (2007). Using automatic passenger counter data in bus arrival time prediction. Journal of Advanced Transportation, 41(3), 267-283. https://doi.org/10.1002/atr.5670410304
  • Mishalani, R. G., McCord, M. R., & Forman, S. (2008). Schedule-based and autoregressive bus running time modeling in the presence of driver-bus heterogeneity. In Computer-aided Systems in Public Transport (pp. 301-317). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73312-6_15
  • 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. https://doi.org/10.3141/1855-03
  • Lin, W. H., & Zeng, J. (1999). Experimental study of real-time bus arrival time prediction with GPS data. Transportation Research Record, 1666(1), 101-109. https://doi.org/10.3141/1666-12
  • Sinn, M., Yoon, J. W., Calabrese, F., & Bouillet, E. (2012, September). Predicting arrival times of buses using real-time GPS measurements. In 15th International IEEE Conference on Intelligent Transportation Systems (pp. 1227-1232). IEEE. https://doi.org/10.1109/ITSC.2012.6338767
  • Jeong, R. H. (2004). The prediction of bus arrival time using automatic vehicle location systems data. A Ph.D. Dissertation at Texas A&M University. https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/1458/etd-tamu-2004C-CVEN-Jeong.pdf?sequence=1&isAllowed=y
  • Altinkaya, M., & Zontul, M. (2013). Urban bus arrival time prediction: A review of computational models. International Journal of Recent Technology and Engineering (IJRTE), 2(4), 164-169.
  • Hagan, M., Demuth, H., & Beale, M. (1996). Neural Network Design. PWS, Boston.
  • Özdamar, K. (2003). Modern bilimsel araştırma yöntemleri. Eskişehir: Kaan Kitabevi

Effect of walking and waiting times on travel time

Year 2020, Volume: 1 Issue: 2, 1202 - , 31.12.2020

Buket Çapalı Halim Ceylan

Abstract

Real-time travel time estimation has nowadays been becoming increasingly important for advanced passenger information systems, traffic management systems, route guidance systems that are part of Intelligent Transportation Systems (ITS). The widespread use of ITSs around the world has increased access to large amounts of historical and real-time status data. The estimation of bus travel time attracts the attention of many researchers in the literature. The aim of this study is to develop a model for estimating public transport travel. It is seen that passenger waiting times are generally considered when bus travel time models are examined in the literature. This study, unlike other studies, the walking time of the passenger to the bus stops are taken into account. The proposed model is determined with five public transport routes data in the city of Isparta, Turkey. As a result, walking time appears to be effect for travel time. A simple regression model for travel time modeling is presented in the study. Results showed that It is better to include walking time to estimate travel times at least using simple regression models.

Keywords

transportation travel time estimation walking and dwell time

References

  • Yu, B., Yang, Z., & Li, S. (2012). Real-time partway deadheading strategy based on transit service reliability assessment. Transportation Research Part A: Policy and Practice, 46(8), 1265-1279. https://doi.org/10.1016/j.tra.2012.05.009
  • 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. https://doi.org/10.1061/(ASCE)0733947X(2002)128:5
  • 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. https://doi.org/10.1111/j.1467-8667.2004.00363.x
  • Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006). Bus arrival time prediction using support vector machines. Journal of Intelligent Transportation Systems, 10(4), 151-158. https://doi.org/10.1080/15472450600981009
  • Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. Ieee Transactions on Intelligent Transportation Systems, 5(4), 276-281. https://doi.org/10.1109/TITS.2004.837813
  • Smith, B. L., and Demetsky, M. J. (1995). Short-term traffic flow prediction: Neural network approach. Transportation Research Record 1453, 98–104.
  • Hellinga, B. R., & Fu, L. (2002). Reducing bias in probe-based arterial link travel time estimates. Transportation Research Part C: Emerging Technologies, 10(4), 257-273. https://doi.org/10.1016/S0968-090X(02)00003-7
  • Cathey, F. W., & Dailey, D. J. (2003). A prescription for transit arrival/departure prediction using automatic vehicle location data. Transportation Research Part C: Emerging Technologies, 11(3-4), 241-264. https://doi.org/10.1016/S0968-090X(03)00023-8
  • Yu, B., Ye, T., Tian, X. M., Ning, G. B., & Zhong, S. Q. (2014). Bus travel-time prediction with a forgetting factor. Journal of Computing in Civil Engineering, 28(3), 06014002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000274
  • Mazloumi, E., Moridpour, S., Currie, G., & Rose, G. (2012). Exploring the value of traffic flow data in bus travel time prediction. Journal of Transportation Engineering, 138(4), 436-446. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000329
  • Chang, G. L., Vasudevan, M., & Su, C. C. (1996). Modelling and evaluation of adaptive bus-preemption control with and without automatic vehicle location systems. Transportation Research Part A: Policy and Practice, 30(4), 251-268. https://doi.org/10.1016/0965-8564(95)00026-7
  • Strathman, J. G., Dueker, K. J., Kimpel, T., Gerhart, R., Turner, K., Taylor, P., ... & Hopper, J. (1999). Automated bus dispatching, operations control, and service reliability: Baseline analysis. Transportation Research Record, 1666(1), 28-36. https://doi.org/10.3141/1666-04
  • Kimpel, T. J. (2002). Time point-level analysis of transit service reliability and passenger demand. Ph.D. dissertation, Portland State Univ., Portland, OR.
  • Strathman, J. G., Kimpel, T. J., Dueker, K. J., Gerhart, R. L., & Callas, S. (2002). Evaluation of transit operations: Data applications of Tri-Met's automated bus dispatching system. Transportation, 29(3), 321-345. https://doi.org/10.1023/A:1015633408953
  • Sun, A. (2005). AVL-based transit operations control. Ph.D. dissertation, Univ. of Arizona, Tucson, AZ. https://repository.arizona.edu/bitstream/handle/10150/194895/azu_etd_1039_sip1_m.pdf?sequence=1
  • Pangilinan, C., Wilson, N., & Moore, A. (2008). Bus supervision deployment strategies and use of real-time automatic vehicle location for improved bus service reliability. Transportation Research Record, 2063(1), 28-33. https://doi.org/10.3141%2F2063-04
  • Shalaby, A., & Farhan, A. (2004). Prediction model of bus arrival and departure times using AVL and APC data. Journal of Public Transportation, 7(1), 3. http://doi.org/10.5038/2375-0901.7.1.3
  • Patnaik, J., Chien, S., & Bladikas, A. (2004). Estimation of bus arrival times using APC data. Journal of Public Transportation, 7(1), 1. http://doi.org/10.5038/2375-0901.7.1.1
  • Sun, D., Luo, H., Fu, L., Liu, W., Liao, X., & Zhao, M. (2007). Predicting bus arrival time on the basis of global positioning system data. Transportation Research Record, 2034(1), 62-72. https://doi.org/10.3141/2034-08
  • Chen, M., Yaw, J., Chien, S. I., & Liu, X. (2007). Using automatic passenger counter data in bus arrival time prediction. Journal of Advanced Transportation, 41(3), 267-283. https://doi.org/10.1002/atr.5670410304
  • Mishalani, R. G., McCord, M. R., & Forman, S. (2008). Schedule-based and autoregressive bus running time modeling in the presence of driver-bus heterogeneity. In Computer-aided Systems in Public Transport (pp. 301-317). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73312-6_15
  • 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. https://doi.org/10.3141/1855-03
  • Lin, W. H., & Zeng, J. (1999). Experimental study of real-time bus arrival time prediction with GPS data. Transportation Research Record, 1666(1), 101-109. https://doi.org/10.3141/1666-12
  • Sinn, M., Yoon, J. W., Calabrese, F., & Bouillet, E. (2012, September). Predicting arrival times of buses using real-time GPS measurements. In 15th International IEEE Conference on Intelligent Transportation Systems (pp. 1227-1232). IEEE. https://doi.org/10.1109/ITSC.2012.6338767
  • Jeong, R. H. (2004). The prediction of bus arrival time using automatic vehicle location systems data. A Ph.D. Dissertation at Texas A&M University. https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/1458/etd-tamu-2004C-CVEN-Jeong.pdf?sequence=1&isAllowed=y
  • Altinkaya, M., & Zontul, M. (2013). Urban bus arrival time prediction: A review of computational models. International Journal of Recent Technology and Engineering (IJRTE), 2(4), 164-169.
  • Hagan, M., Demuth, H., & Beale, M. (1996). Neural Network Design. PWS, Boston.
  • Özdamar, K. (2003). Modern bilimsel araştırma yöntemleri. Eskişehir: Kaan Kitabevi
There are 28 citations in total.

Details

Primary Language English
Subjects Transportation Engineering
Journal Section Research Articles
Authors

Buket Çapalı 0000-0003-1917-1654

Halim Ceylan 0000-0002-4616-5439

Publication Date December 31, 2020
Submission Date October 26, 2020
Acceptance Date December 7, 2020
Published in Issue Year 2020 Volume: 1 Issue: 2

Cite

APA Çapalı, B., & Ceylan, H. (2020). Effect of walking and waiting times on travel time. Journal of Innovative Transportation, 1(2), 1202.
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