The optimization effect of traffic speed prediction on travel path based on improved LSTM

Abstract

The high-speed growth of big data and various cloud computing technologies has accelerated the process of various intelligent transportations. How to more comprehensively alleviate traffic congestion has become a key issue in urban construction. In view of this, this study proposes a traffic speed prediction and travel plan optimization method built on improved long short-term memory. In the process, an improved memory network model is established to predict the driving speed at the future end of the road section, and the improved ant colony optimization algorithms is integrated to optimize and re-plan the route of the traffic congestion travel plan. The results showed that when testing different datasets, the research method had a maximum fitness value of 97.21 when the number of training sessions was 82. When the number of iterations of the system is 85, the average absolute percentage error of the research method on the test set begins to approach 0. At this time, the error values of the three algorithms in the literature, the improved graph convolutional neural network, and the improved long short-term memory model are 0.018%, 0.034%, and 0.035%, respectively, which are significantly greater than the research methods. From the perspective of overall traffic speed prediction, the research method can achieve a fit of 95.42% when predicting the next 10 minutes speed time. All the above results show that the research method has a high adaptability to traffic speed prediction, can achieve dynamic programming of travel plan congestion paths, and provides reliable technical support for urban congestion mitigation methods.

Keywords

LSTM model short-term speed prediction travel planning path planning smart transportation traffic congestion ant colony algorithm

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References

Nguyen

Kieu

Wen

, et al. Deep learning methods in transportation domain: a review. IET Intell Transp Syst 2018; 12(9): 998–1004.

Zhou

, et al. Critical segments identification for link travel speed prediction in urban road network. J Adv Transport 2020; 2020: 8845804–8845810.

George

Santra

. An improved long short‐term memory networks with Takagi‐Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation. Comput Intell 2020; 36(3): 964–993.

Zhang

, et al. MVDLSTM: MultiView deep LSTM framework for online ride-hailing order prediction. J Supercomput 2022; 78(6): 8531–8559.

Liu

Wang

Zhu

. Short‐term traffic speed forecasting based on attention convolutional neural network for arterials. Computer aided Civil Eng 2018; 33(11): 999–1016.

Yao

Wang

. Crude oil price prediction based on LSTM network and GM (1,1) model. Grey Syst 2021; 11(1): 80–94.

Chen

Zhao

. Investigating long‐term vehicle speed prediction based on BP‐LSTM algorithms. IET Intell Transp Syst 2019; 13(8): 1281–1290.

Peng

Wan

Wang

, et al. Deep learning-based recommendation method for Top-K tasks in software crowdsourcing systems. J Ind Manag Optim 2023; 19(9): 6478–6499.

Guan

Han

, et al. Short-term holiday travel demand prediction for urban tour transportation: a combined model based on STC-LSTM deep learning approach. KSCE J Civ Eng 2022; 26(9): 4086–4102.

10.

Mao

Qin

, et al. Analysis of road traffic speed in Kunming plateau mountains: a fusion PSO-LSTM algorithm. Int J Urban Sci 2022; 26(1): 87–107.

11.

Gültekin

Acarman

. Vehicle travel time estimation using sequence prediction. Promet-ZAGREB 2020; 32(1): 1–12.

12.

Mai

Gao

, et al. Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm. Transport Res B-Meth 2021; 151: 42–58.

13.

Zhai

Cui

Zhang

, et al. An improved deep spatial-temporal hybrid model for bus speed prediction. Math Probl Eng 2020; 2020: 2143921–2144011.

14.

Shabarek

Chien

Hadri

. Deep learning framework for freeway speed prediction in adverse weather. Transport Res Rec 2020; 2674(10): 28–41.

15.

Lee

Kho

Kim

, et al. Travel time prediction using gated recurrent unit and spatio-temporal algorithm. P I Civil Eng-Munic 2021; 174(2): 88–96.

16.

Sun

, et al. Will the order be canceled? order cancellation probability prediction based on deep residual model. Transport Res Rec 2023; 2677(6): 142–160.

17.

Zhang

, et al. Graph attention temporal convolutional network for traffic speed forecasting on road networks. Transport Bus 2021; 9(1): 153–171.

18.

Muthukumaran

Natarajan

Kaladevi

, et al. Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features. Acta Geophys 2022; 70(6): 2979–2990.

19.

Aghdam

Tabbakh

SRK

Chabok

SJM

, et al. Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM). J Big Data 2021; 8(1): 54.

20.

Xie

, et al. Graph sequence neural network with an attention mechanism for traffic speed prediction. ACM T Intel Syst Tec 2022; 13(2): 20–24.

21.

Tang

Zeng

Wang

, et al. Traffic flow prediction on urban road network based on license plate recognition data: combining attention-LSTM with genetic algorithm. Transportmetrica 2021; 17(4): 1217–1243.