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Interfering Spatiotemporal Features and Causes of Bus Bunching using Empirical GPS Trajectory Data

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Abstract

Bus bunching refers to the phenomenon that several buses arrive at a station within a short period. It dramatically increases passengers’ waiting time and reduces the quality of transit service. Evaluating the features of bus bunching and identifying the causes are important to developing countermeasures. The primary of this study was to analyze the temporal-spatial features of bus bunching by conducting an in-depth analysis of empirical bus GPS trajectory data obtained in Nanjing, China. The GPS data were inputted into the ArcGIS to track the spatial map's bus trajectories. A data processing procedure was proposed to analyze the data, including data cleaning, trip cutting, each station's arrival and departure time estimation, and time headway calculation. Then the spatiotemporal trajectory picture was drawn for the bus route where the bus bunching was identified. The study also analyzed the headway features of consecutive buses at the different stations and evaluated the variation of time headway, indicating the severity of bus bunching. The results showed that there are significant differences in the spatiotemporal features of bus bunching between bus stations. When the bus bunching occurred, it persisted on downstream stations for a long time. The bunching severity dramatically increased at downstream stations, reducing bus arrival reliability on the whole bus line. We also identified that the bus bunching was primarily caused by the overlong bus dwelling time at a station and the different travel times of buses between stations. The study fills the gap by developing the methodology to investigate the bus bunching features and causes with point-by-point empirical GPS trajectory data. Findings of the study can also support the real-time prediction and warning of bus bunching in practical applications.

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References

  1. Li, M., Li, Z., Xu, C., Liu, T.: Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories. Accid. Anal. Prev. 1(135), 105345 (2020)

    Article  Google Scholar 

  2. Wang, C., Xu, C., Dai, Y.: A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data. Accid. Anal. Prev. 1(123), 365–373 (2019)

    Article  Google Scholar 

  3. Gu, X., Abdel-Aty, M., Xiang, Q., Cai, Q., Yuan, J.: Utilizing UAV video data for in-depth analysis of drivers’ crash risk at interchange merging areas. Accid. Anal. Prev. 1(123), 159–169 (2019)

    Article  Google Scholar 

  4. Guo, Y., Li, Z., Liu, P., Wu, Y.: Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model. Accid. Anal. Prev. 1(128), 164–174 (2019)

    Article  Google Scholar 

  5. Wang, C., Xu, C., Xia, J., Qian, Z., Lu, L.: A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation. Transp. Res. C Emerg. Technol. 1(90), 281–291 (2018)

    Article  Google Scholar 

  6. Liu, Z., Liu, Y., Meng, Q., Cheng, Q.: A tailored machine learning approach for urban transport network flow estimation. Transp. Res. Emerg. Technol. 1(108), 130–150 (2019)

    Article  Google Scholar 

  7. Li, D., Jin, C.J., Yang, M., Chen, A.: Incorporating multi-level taste heterogeneity in route choice modeling: From disaggregated behavior analysis to aggregated network loading. Travel Behav. Soc. 1(19), 36–44 (2020)

    Article  Google Scholar 

  8. Sun, C., Cheng, L., Zhu, S., Han, F., Chu, Z.: Multi-criteria user equilibrium model considering travel time, travel time reliability and distance. Transp. Res. Part D: Transp. Environ. 1(66), 3–12 (2019)

    Article  Google Scholar 

  9. Liu, Z., Wang, S., Zhou, B., Cheng, Q.: Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics. Transp. Res. C Emerg. Technol. 1(79), 58–72 (2017)

    Article  Google Scholar 

  10. Liu, Z., Chen, X., Meng, Q., Kim, I.: Remote park-and-ride network equilibrium model and its applications. Transp. Res. B Methodol. 1(117), 37–62 (2018)

    Article  Google Scholar 

  11. Ji, Y., Ma, X., He, M., Jin, Y., Yuan, Y.: Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: a case study in Nanjing, China. J. Clean. Prod. 11, 120110 (2020)

    Article  Google Scholar 

  12. Ji, Y., Fan, Y., Ermagun, A., Cao, X., Wang, W., Das, K.: Public bicycle as a feeder mode to rail transit in China: The role of gender, age, income, trip purpose, and bicycle theft experience. Int. J. Sustain. Transp. 11(4), 308–317 (2017)

    Article  Google Scholar 

  13. Guo, Y., Li, Z., Wu, Y., Xu, C.: Exploring unobserved heterogeneity in bicyclists’ red-light running behaviors at different crossing facilities. Accid. Anal. Prev. 1(115), 118–127 (2018)

    Article  Google Scholar 

  14. Guo, Y., Li, Z., Wu, Y., Xu, C.: Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach. Transport. Res. F: Traffic Psychol. Behav. 1(59), 212–221 (2018)

    Article  Google Scholar 

  15. Bai, L., Liu, P., Chan, C.Y., Li, Z.: Estimating level of service of mid-block bicycle lanes considering mixed traffic flow. Transp. Res A Policy Pract. 1(101), 203–217 (2017)

    Article  Google Scholar 

  16. Cheng, L., Chen, X., Yang, S., Cao, Z., De Vos, J., Witlox, F.: Active travel for active ageing in China: The role of built environment. J. Transp. Geogr. 1(76), 142–152 (2019)

    Article  Google Scholar 

  17. Fu, X., Lam, W.H.: Modelling joint activity-travel pattern scheduling problem in multi-modal transit networks. Transportation 45(1), 23–49 (2018)

    Article  Google Scholar 

  18. Wang, C., Sun, Z., Ye, Z.: On-Road Bus Emission Comparison for Diverse Locations and Fuel Types in Real-World Operation Conditions. Sustainability. 12(5), 1798 (2020)

    Article  Google Scholar 

  19. Pan, Y., Chen, S., Qiao, F., Ukkusuri, S.V., Tang, K.: Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees. Sci. Total Environ. 10(660), 741–750 (2019)

    Article  Google Scholar 

  20. Liu, Y., Liu, Z., Jia, R.: DeepPF: A deep learning based architecture for metro passenger flow prediction. Transp. Res C Emerg. Technol. 1(101), 18–34 (2019)

    Article  Google Scholar 

  21. Xu, C., Zhao, J., Liu, P.: A geographically weighted regression approach to investigate the effects of traffic conditions and road characteristics on air pollutant emissions. J. Clean. Prod. 1(239), 118084 (2019)

    Article  Google Scholar 

  22. Newell, GF, Renfrey, B.P.: Maintaining a bus schedule. Australian Road Research Board (ARRB) Conference, 2nd, Melbourne. 2(1) (1964)

  23. Figliozzi, M.A., Wu-chi F., Gerardo L., Wei F.: A study of headway maintenance for bus routes: causes and effects of “Bus Bunching” in extensive and congested service areas. OTREC-RR-12-09. Portland, OR: Transportation Research and Education Center (TREC), (2012). https://doi.org/10.15760/trec.107

  24. Fonzone, A., Schmocker, J., Liu, R.: A Model of Bus Bunching under Reliability-based Passenger Arrival Patterns. Transp. Res. Procedia 7, 276–299 (2015)

    Article  Google Scholar 

  25. Feng, W., Figliozzi, M.A.: Empirical analysis of bus bunching characteristics based on bus AVL/APC Data. Civil and environmental engineering faculty publications and presentations. 315, (2015). http://archives.pdx.edu/ds/psu/16620

  26. Lüthi, M., Weidmann, U., Nash, A.: Passenger arrival rates at public transport stations. In: Transportation research board board 86th annual meeting, 07-0635 (2007)

  27. Fu, L., Hellinga, B.: Delay Variability at Signalized Intersections. Transp. Res. Rec. 1710(1710), 215–221 (2000)

    Article  Google Scholar 

  28. Daganzo, C.F.: A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons. Transp. Res. B Methodol. 43(10), 913–921 (2009)

    Article  Google Scholar 

  29. Daganzo, C.F., Pilachowski, J.: Reducing bunching with bus-to-bus cooperation. Transp. Res. B Methodol. 45(1), 267–277 (2011)

    Article  Google Scholar 

  30. M. Chen, S. Chien, X. Liu, and J. Brickey, "Application of APC/AVL archived data support system," In: TRB 82nd Annual Meeting (CD-ROM), Washington, D.C., 2003.

  31. Chien, S.I., Ding, Y., Wei, C.: Dynamic bus arrival time prediction with artificial neural networks. J. Transp. Eng. ASCE 128(5), 429–438 (2002)

    Article  Google Scholar 

  32. Jeong, Hee, R.: "The prediction of bus arrival time using Automatic Vehicle Location Systems data," Doctoral dissertation, Texas A&M University. vol. 65–12, Section: B, pp 6530 2004.

  33. Yu, H., Chen, D., Wu, Z., Ma, X., Wang, Y.: Headway-based bus bunching prediction using transit smart card data. Transp. Res. C Emerg. Technol. 72, 45–59 (2016)

    Article  Google Scholar 

  34. Padmanaban, R.P.S., Divakar, K., Vanajakshi, L., Subramanian, S.C.: Development of a real-time bus arrival prediction system for Indian traffic conditions. IET Intel. Transport Syst. 4(3), 189–200 (2010)

    Article  Google Scholar 

  35. Lin, Y., Yang, X., Zou, N., Jia, L.: Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China. J. Transp. Eng. ASCE 139(11), 1133–1140 (2013)

    Article  Google Scholar 

  36. Feng, W., Figliozzi, M: Empirical findings of bus bunching distributions and attributes using archived AVL/APC bus data. Proceedings of the 11th international conference of chinese transportation professionals: towards sustainable transportation systems. 4330–4341 (2011)

  37. Moreira-Matias, L., Ferreira, C., Gama, J., Mendes- Moreira, J., de Sousa, J. F.: Bus bunching detection by mining sequences of headway deviations. In advances in data mining. applications and theoretical aspects: 12th industrial conference, industrial conference on data mining, 12, 77–91. Springer, Berlin, Heidelberg (2012)

  38. Nguyen, P., Diab, E., Shalaby, A.: Understanding the factors that influence the probability and time to streetcar bunching incidents. Public Transp. 11(2), 299–320 (2019)

    Article  Google Scholar 

  39. Verbich, D., Diab, E., El-Geneidy, A.: Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times. Public Transp. 8(2), 225–242 (2016)

    Article  Google Scholar 

  40. Diab, E., Bertini, R., & El-Geneidy, A.: Bus transit service reliability: Understanding the impacts of overlapping bus service on headway delays and determinants of bus bunching. In: 95th Annual Meeting of the Transportation Research Board, Washington DC, USA (2016)

  41. Schmöcker, J.D., Sun, W., Fonzone, A., Liu, R.: Bus bunching along a corridor served by two lines. Transp. Res. B Methodol. 93, 300–317 (2016)

    Article  Google Scholar 

  42. Wu, W., Liu, R., Jin, W.: Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour. Transp. Res. B Methodol. 104, 175–197 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Jinling Institute of Technology, 99 Hongjing Road, Nanjing, 211169, China

    Xiaofeng Shan & Chishe Wang

  2. School of Transportation, Southeast University, 2 Si Pai Lou Road, Nanjing, 211196, China

    Dongqin Zhou

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  1. Xiaofeng Shan
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Contributions

Xiaofeng Shan: conception or design of the work, data collection, data analysis and interpretation, drafting the article, critical revision of the article.

Chishe Wang: critical revision of the article.

Dongqin Zhou: data collection, data analysis and interpretation.

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Correspondence to Dongqin Zhou.

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Shan, X., Wang, C. & Zhou, D. Interfering Spatiotemporal Features and Causes of Bus Bunching using Empirical GPS Trajectory Data. J Grid Computing 21, 15 (2023). https://doi.org/10.1007/s10723-023-09652-3

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