RHMX: Bus Arrival Time Prediction via Mixed Model
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Keywords

IoT
Big data
Bus arrival time prediction
Long short-term memory
Kalman filtering

DOI

10.26689/jera.v5i6.2708

Abstract

With the widespread use of information technologies such as IoT and big data in the transportation business, traditional passenger transportation has begun to transition and upgrade into intelligent transportation, providing passengers with a better riding experience. Giving precise bus arrival times is a critical link in achieving urban intelligent transportation. As a result, a mixed model-based bus arrival time prediction model (RHMX) was suggested in this work, which could dynamically forecast bus arrival time based on the input data. First, two sub-models were created: bus station stopping time prediction and interstation running time prediction. The former predicted the stopping time of a running bus at each downstream station in an iterative manner, while the latter projected its running time on each downstream road segment (stations as the break points). Using the two models, a group of time series data on interstation running time and bus station stopping time may be predicted. Following that, the time series data from the two sub-models was fused using long short-term memory (LSTM) to generate an approximate bus arrival time. Finally, using Kalman filtering, the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival time. The experimental findings showed that the suggested model's accuracy and stability were both improved by 35% and 17%, respectively, over AutoNavi and Baidu.

References

Gong J, Liu M, Zhang S, 2018, Hybrid Dynamic Prediction Model of Bus Arrival Time Based on Weighted of Historical and Real-Time GPS Data, Control & Decision Conference. IEEE.

Marko C, Marjan L, 2018, Bus-Arrival Time Prediction Using Bus Network Data Model and Time Periods. Future Generation Computer Systems, 3-5.

Chen G, Yang X, Zhang D, et al., 2017, Historical Travel Time Based Bus-Arrival-Time Prediction Model. ICCTP 2011: Towards Sustainable Transportation Systems.

Yu B, Lam WHK, Tam ML, 2011, Bus Arrival Time Prediction at Bus Stop with Multiple Routes. Transportation Research Part C Emerging Technologies, 19(6): 1157-1170.

Van Eekelen R, McLernon DJ, Van Wely M, et al., 2018, External Validation of A Dynamic Prediction Model for Repeated Predictions of Natural Conception over Time. Pubmed, 2268-2275.

Bachu A, Subramanian SC, 2016, Pattern-Based Bus Travel Time Prediction under Heterogeneous Traffic Conditions. 93rd Annual Meeting-Transportation Research Boards.

Kong He, et al., 2021, Kalman Filtering Under Unknown Inputs and Norm Constraints. Automatica, 133.

Li X, Gao W, Zhang J, 2020, A Novel Hybrid Unscented Particle Filter based on Firefly Algorithm for Tightly-Coupled Stereo Visual-Inertial Vehicle Positioning. Cambridge University Press, 73(3): 121-55.

Dhivyabharathi B, Kumar BA, Vanajakshi L, 2016, Real Time Bus Arrival Time Prediction System under Indian Traffic Condition. IEEE International Conference on Intelligent Transportation Engineering. IEEE, 18-22.

Dhivyabharathi B, Kumar BA, Vanajakshi L, et al., 2017, Particle Filter for Reliable Bus Travel Time Prediction Under Indian Traffic Conditions. Transportation in Developing Economies, 3(2): 1-11.

Hans E, Chiabaut N, Leclercq L, et al., 2018, Real-time Bus Route State Forecasting Using Particle Filter: An Empirical Data Application. Transportation Research Procedia. 2018.

Dhivyabharathi B, Kumar BA, Vanajakshi L, et al., 2017, Particle Filter for Reliable Bus Travel Time Prediction Under Indian Traffic Conditions. Transportation in Developing Economies, (2).

Huang YP, et al., 2021, Bus Arrival Time Prediction and Reliability Analysis: An Experimental Comparison of Functional Data Analysis and Bayesian Support Vector Regression. Applied Soft Computing Journal, 111.

Zhou Y, Yao L, et al., 2017, Bus Arrival Time Calculation Model Based on Smart Card Data. Transportation Research Part C, 74: 81-96.

Deng F, Deng F, Su P, et al., 2020, Travel Time Prediction of Urban Road based on BP Neural Network, Journal of Physics: Conference Series, 1651(1).

Xu W, Rong W, 2021, Neural Network Model based on Travel Planning for Travel Time Prediction, Journal of Physics: Conference Series, 1883(1).

Petersen NC, Rodrigues F, Pereira FC, 2019, Multi-output Bus Travel Time Prediction with Convolutional LSTM neural Network. Expert Systems with Applications, 120: 426-435.

Xie ZY, He YR, Chen CC, et al., 2021, Multistep Prediction of Bus Arrival Time with the Recurrent Neural Network, Mathematical Problems in Engineering, 76:72-97.