Analysis and Dynamic Prediction of Bus Dwell Time Under Rainfall Conditions

Baoyun SUN; Yaping YANG; Lei DONG; Honglin LU; Zimin WANG

doi:10.7307/ptt.v37i1.593

Authors

Baoyun SUN Shenyang Jianzhu University, School of Transportation and Surveying Engineering
Yaping YANG Shenyang Jianzhu University, School of Transportation and Surveying Engineering
Lei DONG Shenyang Jianzhu University, School of Architecture and Urban Planning
Honglin LU Shenyang Jianzhu University, School of Transportation and Surveying Engineering
Zimin WANG Shenyang Jianzhu University, School of Transportation and Surveying Engineering

DOI:

https://doi.org/10.7307/ptt.v37i1.593

Keywords:

rainfall conditions, bus dwell time, influencing factors, correlation, GA-BP model

Abstract

Exploring the degree to which bus stop times are affected by rainfall is necessary for a reasonable formulation of bus-scheduling management schemes under rainy conditions. Although numerous mathematical models have been proposed, the predictive accuracy of existing models is insufficient for the precise formulation of bus policies. This study considered linear bus stops in Shenyang as research targets, and based on field survey data, we analysed the bus dwell time and its influencing factors under varying degrees of rainfall. The Pearson correlation analysis method and SPSS software were used to reveal the degree of influence of parameters, such as the number of passengers boarding and alighting buses, rainfall level, number of berthing spaces, load rate and presence of signalised intersections, on the bus stop time under rainfall conditions. Support vector machine, k-nearest neighbour and backpropagation (BP) prediction models were established, and the BP neural network model, having the best prediction effect, was optimised using a genetic algorithm (GA). The constructed GA-BP prediction model was more realistic than the BP prediction model and can be used to predict bus dwell times under rainfall conditions. The study findings will facilitate bus punctuality and improve customer appeal for bus services.

References

KFH Group. Transit Capacity and Quality of Service Manual. Transportation Research Board. Washington DC, 2013.

Fernandez R. Modelling public transport stops by microscopic simulation. Transportation Research Part C-Emerging Technologies. 2010;18(6):856-868. DOI: 10.1016/j.trc.2010.02.002.

Kostyniuk LP, D’Souza CR. Effect of passenger encumbrance and mobility aid use on dwell time variability in low-floor transit vehicles. Transportation Research Part A-Policy and Practice. 2020;132:872-881. DOI: 10.1016/j.tra.2020.01.002.

Bergel-Hayat R, Debbarh M, Antoniou C, Yannis G. Explaining the road accident risk: Weather effects. Accident Analysis & Prevention. 2013;60:456-465. DOI: 10.1016/j.aap.2013.03.006.

Bocker L, Dijst M, Prillwitz J. Impact of everyday weather on individual daily travel behaviours in perspective: A literature review. Transport Reviews. 2013;33(1):71-91. DOI: 10.1080/01441647.2012.747114 .

Tirachini A. Bus dwell time: The effect of different fare collection systems, bus floor level and age of passengers. Transportmetrica A: Transport Science. 2013;9(1):28-49. DOI: 10.1080/18128602.2010.520277.

Milkovits MN. Modeling the factors affecting bus stop dwell time: Use of automatic passenger counting, automatic fare counting, and automatic vehicle location Data. Transportation Research Record. 2008;2072(1):125-130. DOI: 10.3141/2072-13.

Al-Jumailey SI. Effect of operation policies on dwell time. 2011 International Conference on Electric Technology and Civil Engineering (ICETCE). 2011:1194-1196. DOI: 10.1109/ICETCE.2011.5776416.

Ji Y, et al. How does a static measure influence passengers’ boarding behaviors and bus dwell time? Simulated evidence from Nanjing bus stations. Transportation Research Part A-Policy and Practice. 2018;110:13-25. DOI: 10.1016/j.tra.2018.02.003.

Bladikas A, Tsai FM, Chien SI. Evaluation of bus travel time and schedule adherence under adverse weather. The 90th Transportation Research Board Annual Meeting, 2009.

Stover V, McCormack E. The impact of weather on bus ridership in Pierce County, Washington. Journal of Public Transportation. 2012, 15(1): 95-110.

Glick TB, Figliozzi MA. Analysis and application of log-linear and quantile regression models to predict bus dwell times. Transportation Research Record. 2019;2673(10):118-128. DOI: 10.1177/0361198119848701.

Ding J, Yang M, Cao Y, Kong SL. Dwell time prediction of bus rapid transit using arima-svm hybrid model. Applied Mechanics and Materials. 2014;587-589:1993-1997. DOI: 10.4028/www.scientific.net/amm.587-589.1993.

Rashidi S, Ranjitkar P, Hadas Y. Modeling bus dwell time with decision tree-based methods. Transportation Research Record. 2014;2418(1):74-83. DOI: 10.3141/2418-09.

Liu Y. An improved AHP and BP neural network method for service quality evaluation of city bus. International Journal of Computer Applications in Technology. 2018;58(1):37-44. DOI: 10.1504/ijcat.2018.094060.

Femandez R. An expert system for the preliminary design and location of high capacity bus-stops. Traffic Engineering & Control. 1993;34(11):533-539.

Avanzato R, Beritelli F, Raspanti A, Russo M. Assessment of multimodal rainfall classification systems based on an audio/video dataset. International Journal on Advanced Science, Engineering and Information Technology. 2020;10:1163-1168. DOI: 10.18517/ijaseit.10.3.12130.

González EM, Romana MG, Álvaro OM. Bus dwell-time model of main urban route stops: Case study in Madrid, Spain. Transportation Research Record. 2012;2274(1):126-134. DOI: 10.3141/2274-14.

Yue Y, Yeh AG. Spatiotemporal traffic-flow dependency and short-term traffic forecasting. Environment and Planning B: Planning and Design. 2008;35(5):762-771. DOI: 10.1068/b33090.

Novales M, et al. Increasing boarding lost time at regular bus stops during rainy conditions: A case study. Journal of Public Transportation. 2021;23(1):63-80. DOI: 10.5038/2375-0901.23.1.4.

Panda SK, Jana PK. A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment. International Conference on Electronic Design, Computer Networks & Automated Verification, 2015. DOI: 10.1109/EDCAV.2015.7060544.

Khanlou HM, et al. Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. App Neural Computing and Applications. 2014;25:767-777. DOI: 10.1007/s00521-014-1554-8.

Leshno M, Lin VY, Pinkus A, Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Network. 1993;6(6):861-867. DOI: 10.1016/s0893-6080(05)80131-5.

Islam MM, et al. A new adaptive merging and growing algorithm for designing artificial neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2009;39(3):705-722. DOI: 10.1109/ijcnn.2008.4634073.

Hao PY, Chiang JH, Chen YD. Possibilistic classification by support vector networks. Neural Network. 2022;149:40-56. DOI: 10.1016/j.neunet.2022.02.007.

Zhang S, Li J. KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering. 2021:1-1. DOI: 10.1109/tkde.2021.3119140.

Wei W, et al. Prediction of tool wear based on GA-BP neural network. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2022;236(12):1564-1573. DOI: 10.1177/09544054221078144.

Qi A. Research on prediction model of improved BP neural network optimized by genetic algorithm. Journal of Engineering Research. 2018;150:764-767. DOI: 10.2991/macmc-17.2018.145.

Liu CY, et al. Application of GA-BP neural network optimized by grey Verhulst model around settlement prediction of foundation pit. Geofluids. 2021:1-16. DOI: 10.1155/2021/5595277.

Yi XD. Selection of initial weights and thresholds based on the genetic algorithm with the optimized back-propagation neural network. In: 12th international conference on fuzzy systems and knowledge discovery (FSKD), Zhangjiajie, China, 15–17 Aug 2015; IEEE Publications: New York, 2015; p. 173-177. DOI: 10.1109/fskd.2015.7381935.

Zhang S, Huo Z, Zhai C. Building carbon emission scenario prediction using STIRPAT and GA-BP neural network model. Sustainability. 2022;14(15):9369. DOI: 10.3390/su14159369.

Liu D, Liu C, Tang Y, Gong C. A GA-BP neural network regression model for predicting soil moisture in slope ecological protection. Sustainability. 2022;14(3):1386-1386. DOI: 10.3390/su14031386.