LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration

Yue, Min; Ma, Shenglin

doi:10.3390/app13010637

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…(5) Calculation of network hierarchy Network hierarchy classifies cities by selecting node centrality and network connectedness, analyses the cities of different hierarchical levels, and selects node hierarchy and linkage hierarchy to describe the hierarchy of the UA network structure. The calculation formula is Equation (9).…”

Section: Social Network Analysis (Sna)mentioning

confidence: 99%

“…The 19 distinct UAs in China each display unique developmental characteristics influenced by geographical, demographic, industrial, economic, and other factors [8,9]. These agglomerations encompass well-established international hubs like the Yangtze River Delta (YRD), Beijing-Tianjin-Hebei (BTH), and the Pearl River Delta (PRD), alongside others in the early stages of development, characterized by substantial disparities in their developmental statuses and potentials.…”

Section: Introductionmentioning

confidence: 99%

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Network Structure Characteristics and Influencing Factors of Urban Agglomerations in China under Impact of COVID-19

Wu,

Xu,

Shi

et al. 2024

Applied Sciences

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In the context of COVID-19, the efforts undertaken for epidemic control have imposed limitations on the multifaceted development of China. This manuscript utilizes Baidu migration data from 2019 to 2023 to classify the current developmental status of urban agglomerations (UAs) in China. The explication of network structure is achieved through the computation of metrics that capture network structural connectivity and hierarchical attributes. Additionally, an inquiry into the spatio-temporal differentiation of the UAs’ network structure is carried out, encompassing three phases: before COVID-19, the normalization stage of COVID-19, and after COVID-19. Furthermore, Quantitative Analysis of Patterns (QAP) is employed to assess the impact of diverse influencing factors. The analysis yields several key findings: ① The impact of COVID-19 on the network structure of China’s UAs manifests in two discernible stages—initial impact disruption and subsequent recovery and reconstruction. ② The exploration of pertinent influencing factors during the primary stage of UA development is impeded. ③ The growth stage and the UAs with a high level of development exhibit have a closely intertwined relationship, fostering a more rational hierarchical structure and demonstrating an enhanced capacity for swift recovery. ④ It is discerned that economic development level, medical facility standards, transportation infrastructure capacity, spatial proximity, and innovation accessibility exert a discernible influence on the network structure of UAs. Importantly, the extent of impact varies across different periods and types of UAs.

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Section: Social Network Analysis (Sna)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Structure Characteristics and Influencing Factors of Urban Agglomerations in China under Impact of COVID-19

Wu,

Xu,

Shi

et al. 2024

Applied Sciences

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Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks

Aljanabi,

Borna,

Ghanbari

et al. 2024

Expert Systems

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Multimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi‐modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance for cargo transportation, as it enables SC participants to increase logistics efficiency and quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well‐suited to resolve complex and nonlinear relationships in the collected tracking data. This study designs a deep learning‐powered travel time estimation in multimodal freight transportation networks (DLTTE‐MFTN) technique. The goal of the DLTTE‐MFTN technique is to estimate the travel time using a hyperparameter‐tuned ensemble learning approach. To achieve this, the DLTTE‐MFTN method initially undergoes data pre‐processing to convert the input raw data into a useful format. In addition, the singular value decomposition (SVD) model can be applied for feature dimensionality reduction in multimodal transport data, considerably improving travel time prediction. Besides, the DLTTE‐MFTN method estimates travel time using an ensemble of three DL approaches including one‐dimensional convolutional neural network (1D‐CNN), stacked autoencoder (SAE) attention, and recurrent neural network (RNN). Finally, the hyperparameter tuning of the DL models takes place using the whale optimization algorithm (WOA). The performance analysis of the DLTTE‐MFTN method takes place using the Kaggle dataset. The experimental results stated that the DLTTE‐MFTN technique attains superior performance over other ML and DL models.

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Passenger flow prediction and management method of urban public transport based on SDAE model and improved Bi-LSTM neural network

Xian,

Tian

2023

IFS

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In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field.

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LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration

Cited by 4 publications

References 32 publications

Network Structure Characteristics and Influencing Factors of Urban Agglomerations in China under Impact of COVID-19

Network Structure Characteristics and Influencing Factors of Urban Agglomerations in China under Impact of COVID-19

Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks

Passenger flow prediction and management method of urban public transport based on SDAE model and improved Bi-LSTM neural network

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