Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach
Abstract
The application of predicting bus travel time with re-al-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effec-tive to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genet-ic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model perfor-mance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various com-binations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC.
References
Dulebenets MA. A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility. International Journal of Production Economics. 2019;212: 236-258. doi: 10.1016/j.ijpe.2019.02.017.
Pasha J, et al. An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations. Advanced Engineering Informatics. 2021;48: 101299. doi: 10.1016/j.aei.2021.101299.
Belokurov V, Spodarev R, Belokurov S. Determining passenger traffic as important factor in urban public transport system. Transportation Research Procedia. 2020;50: 52-58. doi: 10.1016/j.trpro.2020.10.007.
Kağan Albayrak MB, Özcan İÇ, Dobruszkes F. The determinants of air passenger traffic at Turkish airports. Journal of Air Transport Management. 2020;86: 101818. doi: 10.1016/j.jairtraman.2020.101818.
Enoch MP, et al. Future local passenger transport system scenarios and implications for policy and practice. Transport Policy. 2020;90: 52-67. doi: 10.1016/j.tranpol.2020.02.009.
Zhao L, Chien S, Spasovic LN, Liu X. Modeling and optimizing urban bus transit considering headway variation for cost and service reliability analysis. Transportation Planning & Technology. 2018;41(7): 706-723. doi: 10.1080/03081060.2018.1504181.
Zhao L, Chien S. Investigating the impact of stochastic vehicle arrivals to optimal stop spacing and headway for a feeder bus route. Journal of Advanced Transportation. 2015;49(3): 341-357. doi: 10.1002/atr.1270.
Chien S, Ding Y, Wei C. Dynamic bus arrival time prediction with artificial neural networks. Journal of Transportation Engineering. 2002;128(5): 429-438. doi: 10.1061/(ASCE)0733-947X(2002)128:5(429).
Tilahun SL, Ong HC. Bus timetabling as a fuzzy multiobjective optimization problem using preference-based genetic algorithm. Promet – Traffic&Transportation. 2012;24(3): 183-191. doi: 10.7307/ptt.v24i3.311.
Ma W, Lin N, Chen X, Zhang W. A robust optimization approach to public transit mobile real-time information. Promet – Traffic&Transportation. 2018;30(5): 501-512. doi: 10.7307/ptt.v30i5.2609.
Mazloumi E, Rose G, Currie G, Moridpour S. Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction. Engineering Applications of Artificial Intelligence. 2011;24(3): 534-542. doi: 10.1016/j.engappai.2010.11.004.
Zhang J, et al. A real-time passenger flow estimation and prediction method for urban bus transit systems. IEEE Transactions on Intelligent Transportation Systems. 2017;18(11): 3168-3178. doi: 10.1109/TITS.2017.2686877.
Ma J, et al. Bus travel time prediction with real-time traffic information. Transportation Research Part C: Emerging Technologies. 2019;105: 536-549. doi: 10.1016/j.trc.2019.06.008.
Zhou Y, et al. Bus arrival time calculation model based on smart card data. Transportation Research Part C: Emerging Technologies. 2017;74: 81-96. doi: 10.1016/j.trc.2016.11.014.
Dai Z, Ma X, Chen X. Bus travel time modeling using GPS probe and smart card data: A probabilistic approach considering link travel time and station dwell time. Journal of Intelligent Transportation Systems. 2019;23(2): 175-190. doi: 10.1080/15472450.2018.1470932.
Petersen NC, Rodrigues F, and Pereira FC. Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Systems with Applications. 2019;120(4): 426-435. doi: 10.1016/j.eswa.2018.11.028.
Dai D, Mu D. An algorithm for bus trajectory extraction based on incomplete data source. Chinese Journal of Electronics. 2012;021(004): 599-603.
Kumar BA, Vanajakshi L, Subramanian SC. Bus travel time prediction using a time-space discretization approach. Transportation Research Part C: Emerging Technologies. 2017;79: 308-332. doi: 10.1016/j.trc.2017.04.002.
Shabarek A, Chien S, Hadri S. Deep learning framework for freeway speed prediction in adverse weather. Transportation Research Record. 2020;2674(10): 28-41. doi: 10.1177/0361198120947421.
He P, Jiang G, Lam SK, Tang D. Travel-time prediction of bus journey with multiple bus trips. IEEE Transactions on Intelligent Transportation Systems. 2018;20(11): 4192-4205. doi: 10.1109/TITS.2018.2883342.
Serin F, Alisan Y, Kece A. Hybrid time series forecasting methods for travel time prediction. Physica A: Statistical Mechanics and Its Applications. 2021;579: 126134. doi: 10.1016/j.physa.2021.126134.
Zhong S, Hu J, Ke S, Wang X. A hybrid model based on support vector machine for bus travel-time prediction. Promet – Traffic&Transportation. 2015;27(4): 291-300. doi: 10.7307/ptt.v27i4.1577.
Yang M, Chen C, Wang L, Yang X. Bus arrival time prediction using support vector machine with genetic algorithm. Neural Network World Journal. 2016;26(3): 205-217. doi: 10.14311/NNW.2016.26.011.
Peng Z, Jiang Y, Yang X, Zhao Z. Bus arrival time prediction based on PCA-GA-SVM. Neural Network World Journal. 2018;28(1): 87-104. doi: 10.14311/NNW.2018.28.005.
Gal A, Mandelbaum A, Schnitzler F, Senderovich A. Traveling time prediction in scheduled transportation with journey segments. Information Systems. 2017;64: 266-280. doi: 10.1016/j.is.2015.12.001.
Kumar BA, Vanajakshi L, Subramanian SC. Pattern-based time-discretized method for bus travel time prediction. Journal of Transportation Engineering, Part A: Systems. 2017;143(6): 04017012. doi: 10.1061/JTEPBS.0000029.
Chien S, Kuchipudi MC. Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering. 2003;129(6): 608-616. doi: 10.1061/(ASCE)0733-947X(2003)129:6(608).
Jairam R, Kumar BA, Arkatkar SS, Vanajakshi L. Performance comparison of bus travel time prediction models across Indian Cities. Transportation Research Record. 2018;2672(31): 87-98. doi:10.1177/0361198118770175.
Park D, Laurence RR. Forecasting freeway link travel times with a multilayer feedforward neural network. Computer‐Aided Civil and Infrastructure Engineering. 1999;14(5): 357-367. doi: 10.1111/0885-9507.00154.
Jeong R, Rilett LR. Prediction model of bus arrival time for real-time applications. Transportation Research Record. 2005;1927(1): 195-204. doi: 10.3141/1927-23.
Pang J, Huang J, Du Y, Yu H. Learning to predict bus arrival time from heterogeneous measurements via recurrent neural network. IEEE Transactions on Intelligent Transportation Systems. 2018;20(9): 3283-3293. doi: 10.1109/TITS.2018.2873747.
Liu H, Xu H, Yu Y, Cai Z. Bus arrival time prediction based on LSTM and spatial-temporal feature vector. IEEE Access. 2020;8: 11917-11929. doi: 10.1109/ACCESS.2020.2965094.
Irie K, Tüske Z, Alkhouli T, Schlüter R. LSTM, GRU, highway and a bit of attention: An empirical overview for language modeling in speech recognition. Interspeech 2016. 2016. p. 3519-3523. doi: 10.21437/Interspeech.2016-491.
Zhai H, Cui L, Zhang W, Xu X. An improved deep spatial-temporal hybrid model for bus speed prediction. Mathematical Problems in Engineering. 2020(2): 1-11. doi: 10.1155/2020/2143921.
Shen M, Xu Q, Wang K, Tu M. Short-term bus load forecasting method based on cnn-gru neural network. Proceedings of Purple Mountain Forum 2019 -International Forum on Smart Grid Protection and Control. Springer, Singapore; 2020. p. 711-722. doi: 10.1007/978-981-13-9783-7_58.
Xue X, Jia Y, Wang S. Expressway traffic flow prediction model based on Bi-LSTM neural networks. IOP Conference Series: Earth and Environmental Science. 2020;587(1): 012007. doi: 10.1088/1755-1315/587/1/012007.
Shu W, Cai K, Xiong N. A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Transactions on Intelligent Transportation Systems. 2021;7: 1-12. doi: 10.1109/TITS.2021.3094659.
Ding Y, et al. Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing. 2020;403: 348-359. doi: 10.1016/j.neucom.2020.04.110.
Zhao H, Zhang C. An online-learning-based evolutionary many-objective algorithm. Information Sciences. 2020;509: 1-21. doi: 10.1016/j.ins.2019.08.069.
Dulebenets MA. An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal. Information Sciences. 2021;565: 390-421. doi: 10.1016/j.ins.2021.02.039.
Liu ZZ, Wang Y, Huang PQ. AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation. Information Sciences. 2020;509: 400-419. doi: 10.1016/j.ins.2018.06.063.
Pasha J, et al. An optimization model and solution algorithms for the vehicle routing problem with a “factory-in-a-box”. IEEE Access. 2020;8: 134743-134763. doi: 10.1109/ACCESS.2020.3010176.
D'Angelo G, Pilla R, Tascini C, Rampone S. A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees. Soft Computing. 2019;23(22): 11775-11791. doi: 10.1007/s00500-018-03729-y.
Ergen T, Kozat S. Online training of LSTM networks in distributed systems for variable length data sequences. IEEE Transactions on Neural Networks and Learning Systems. 2017;99: 1-7. doi: 10.1109/TNNLS.2017.2770179.
Davis RE. Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. Journal of Physical Oceanography. 1976;6(3): 249-266. doi: 10.1175/1520-0485(1976)006<0249:POSSTA>2.0.CO;2.
Kieu L, Bhaskar A, Chung E. Public transport travel-time variability definitions and monitoring. Journal of Transportation Engineering. 2015;141(1): 04014068. doi: 10.1061/(ASCE)TE.1943-5436.0000724.
Copyright (c) 2022 Ruisen JIANG, Dawei HU, Steven I-Jy CHIEN, Qian SUN, Xue WU
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
