Online prediction of network-level public transport demand based on principle component analysis

Zhong, Cheng; Wu, Penglong; Zhang, Qi; Ma, Zhenliang

doi:10.1016/j.commtr.2023.100093

Cited by 30 publications

(5 citation statements)

References 26 publications

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“…Furthermore, some researchers have integrated points of interest (POI) with GPS trajectories to develop urban heavy truck mobile networks [42]. Research has also found that integrating various methods and data sources, such as Chat GPT, NLP, and RL models, along with traffic flow data, public transportation data, and travel survey data [43][44][45], can further uncover the dynamic characteristics and evolving trends of urban street transportation systems. These findings offer new insights for our research on urban streets.…”

Section: Discussionmentioning

confidence: 99%

Research on Urban Street Network Structure Based on Spatial Syntax and POI Data

Yang,

Jin,

2024

Sustainability

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The rapid development of cities has led to increasingly problems in the road network structure of urban streets. Combining emerging big data technology with traditional street network analysis methods has become a new way to tackle it. Guilin is a famous international tourist city, and the “Two Rivers and Four Lakes” scenic area is an iconic symbol of Guilin’s scenery. Its streets connect various tourist spots. This study focused on the street’s layout of the “Two Rivers and Four Lakes” scenic area, and used a combination of spatial syntax and POI big data to analyse their spatial structure. The research results indicated that: (1) there was a positive correlation between the global integration value of the street and the POI value; (2) by combining functional density indicators with global integration analysis, streets that significantly deviate from the overall trend can be identified, and classified according to their characteristics to reveal the reasons for their contradictions; (3) we needed to propose three plans for optimizing the proportion of high street, enhancing street functions, and “improving street space” for different types of streets to ultimately realize the purpose of sustainable development of streets and cities.

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Section: Discussionmentioning

confidence: 99%

Research on Urban Street Network Structure Based on Spatial Syntax and POI Data

Yang,

Jin,

2024

Sustainability

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“…With modest modification, the model can also account for the more realistic nonlinear charging profile of batteries. Meanwhile, further extensions may involve the scheduling of BEB fleets dispatched from multiple terminals considering battery capacity degradation, stochastic travel times (Szeto et al., 2011; Yu et al., 2018; Zhong et al, 2023), and penalty due to service delay (Ghosh‐Dastidar & Adeli, 2006; X. Jiang & Adeli, 2003). Additionally, we also explore control strategy on BEBs to improve the transit reliability (Adeli & Ghosh‐Dastidar, 2004; M. Li et al., 2011; Han et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

On the role of time‐of‐use electricity price in charge scheduling for electric bus fleets

Zhang,

Wang,

et al. 2023

Computer aided Civil Eng

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As electric buses become increasingly popular, it is imperative to optimize the schedules of electric buses with explicit consideration of their charging requirements. Unfortunately, existing studies failed to properly model the impacts of essential operating factors, including the time‐of‐use (TOU) electricity price, partial charging, and limited chargers. Our paper proposes a mixed‐integer nonlinear program to minimize the total operating cost of an electric bus fleet for fulfilling a group of timetabled trips, considering the above realistic features simultaneously. To effectively solve the problem of global optimality, we convert this model to an equivalent set partitioning model and develop a specialized branch‐and‐price approach subsequently. Our approach's computational efficiency is verified via extensive numerical experiments. The model is applied to a real‐world case study in Nanjing, China. Results show that incorporating the TOU electricity price into the electric bus scheduling problem can produce a sizeable cost saving of up to 22%. Managerial insights unveiled by the numerical results are discussed. These insights inform the practitioners of how the operations of electric buses can be both cost‐effective and grid‐friendly.

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“…Passenger flow prediction has yielded numerous significant results after years of continuous research and practice, such as the accuracy of prediction under the conditions of poor quality passenger flow data learning 32 . proposed a PRP‐PCA model that exhibits high robustness in adapting to data quality problems.In addition, owing to breakthroughs in deep learning, an increasing number of researchers have applied it to traffic prediction.…”

Section: Related Studiesmentioning

confidence: 99%

A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

Xi,

Yan,

Jia

et al. 2024

Concurrency and Computation

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SummaryPassenger flow prediction is an important part of daily metro operation, and its accuracy affects the deployment of train resources management. Due to the complex spatiotemporal correlation characteristics of metro passenger flow, it is necessary to describe it to improve the accuracy of passenger flow prediction. However, the existing models mainly construct the weight matrix based on the static graph and the similarity between stations when describing the spatial correlation of station passenger flow but ignore the time‐varying characteristics of the spatial correlation of station passenger flow. To address this problem, this study introduces a dynamic multi‐graph and multidimensional attention spatiotemporal model. Specifically, the Graph Convolutional Neural Network combined with dynamic multigraph extracts spatial features and the Gated Recurrent Unit extracts temporal features of passenger flow. The multidimensional attention can obtain the spatiotemporal correlation of passenger flow data by assigning weights to them. Finally, this model has been used to conduct experiments on Beijing metro passenger flow datasets with time granularity of 10 and 15 min. The result indicates that the DGMANN model outperforms state‐of‐the‐art other deep learning methods in passenger flow prediction. In addition, the effectiveness of its key submodules has been verified through ablation experiments.

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Online prediction of network-level public transport demand based on principle component analysis

Cited by 30 publications

References 26 publications

Research on Urban Street Network Structure Based on Spatial Syntax and POI Data

Research on Urban Street Network Structure Based on Spatial Syntax and POI Data

On the role of time‐of‐use electricity price in charge scheduling for electric bus fleets

A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

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