Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning

Chen, Tao; Fang, Jie; Xu, Mengyun; Tong, Yingfang; Chen, Wentian

doi:10.1061/jtepbs.0000653

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Many scholars support the existence of spatial effects when considering the factors influencing road traffic volume [52]. Specifically, this can be reflected in the spatial correlation of road traffic volume within cities [53] and the spatial spillover effect across cities and provinces [54]. The spatial effect may also be present in the processes of the recovery of road traffic volume from the impact of COVID-19.…”

Section: Spatial Spillover Effectmentioning

confidence: 99%

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

et al. 2022

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The impact of COVID-19 on traffic volume makes it essential to study the spatial heterogeneity and impact mechanisms of the recovery of road traffic volume to promote the sustainability of related industries. As the research method, this study used a principal component analysis to evaluate the recovery of road traffic volume in China quantitatively, and further conducted an empirical study using a spatial autocorrelation index and a dynamic spatial panel model. The results show that income has a negative impact on the recovery of road traffic volume, while climate suitability has a positive impact. Economic development and COVID-19 can play moderating and mediating effects, respectively. From the aspect of spatial heterogeneity, the recovery of road traffic volume has a positive spatial spillover effect on the surrounding provinces, while the spread of COVID-19 has a negative short-term indirect spatial spillover effect. Corresponding practical insights are provided for the stakeholders based on the above findings. The results of this study will contribute to the development of effective policies to facilitate the recovery of road traffic volume from the impact of COVID-19 and the revitalization of the transportation industry.

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Section: Spatial Spillover Effectmentioning

confidence: 99%

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

et al. 2022

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“…The above methods have made some positive progress in the predicting performance, but there is still room for improvement in the accuracy of the bus-passenger-flow prediction methods. There are numerous variables that affect the bus passenger flow, such as the time of day, the traffic conditions, and the weather, which make the bus passenger flow nonstationary and unpredictable [13]. In the case of time-series bus-passenger-flow data containing a mix of linear and nonlinear information, neither traditional statistical models nor deep learning models can accomplish satisfactory prediction results by themselves.…”

Section: Introductionmentioning

confidence: 99%

Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM

Pei,

Ran,

Wang

et al. 2023

Sustainability

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The prediction of the bus passenger flow is crucial for efficient resource allocation, frequency setting, and route optimization in bus transit systems. However, it remains challenging for a single model to simultaneously capture the time-series data of the bus passenger flow with periodicity, correlation, and nonlinearity. Aiming at the complex volatility possessed by the time-series data of the bus passenger flow, a new hybrid-strategy bus-passenger-flow prediction model based on wavelet packet decomposition, an attention mechanism, and bidirectional long–short-term memory is proposed to improve the accuracy of bus-passenger-flow prediction. The differences between this study and the existing studies are as follows: Firstly, this model combines decomposition strategies and deep learning. Wavelet packet decomposition can decompose the original data into a series of smoother data components, allowing the model to be more adequate in capturing the temporal characteristics of passenger-flow data. And the model can consider the information after the predicted moment via backward computation. In addition, the model is equipped with the ability to focus on important features by incorporating an attention mechanism to minimize the interference of irrelevant information. Bus-passenger-flow prediction experiments are conducted using the Harbin bus-passenger-flow dataset as an example. The experimental results show that the model proposed in this paper can obtain more accurate bus-passenger-flow prediction results than the five baseline models can obtain.

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Spatio-Temporal Factors Affecting Short-Term Public Transit Passenger Demand Prediction: A Review

Nithin,

Mulangi

2023

Lecture Notes in Civil Engineering

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Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning

Cited by 8 publications

References 18 publications

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM

Spatio-Temporal Factors Affecting Short-Term Public Transit Passenger Demand Prediction: A Review

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