Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data

Gao, Yuan; Chiang, Yao‐Yi; Zhang, Xiaoxi; Zhang, Min

doi:10.1111/tgis.12975

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…The free flow capacity determines the normal capacity of the road, while the queue discharge rate determines the recovery capacity of the road after congestion, and the results can be used for road planning and road control. Current research considers road capacity to be a non-fixed value whose value changes over time [ 23 ]. For queue emission rates, the average value is generally used to describe how many vehicles exit the congestion queue by multiplying the average value over a period of time by the duration [ 24 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Study of medium and long-term free flow capacity and queue discharge rates on roads

Rong,

Xue

2024

PLoS ONE

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With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city’s main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

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Section: Literature Reviewmentioning

confidence: 99%

Study of medium and long-term free flow capacity and queue discharge rates on roads

Rong,

Xue

2024

PLoS ONE

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“…More and more, people choose to travel in their leisure time to come into contact with new things and improve their quality of life, so the tourism industry is booming [2][3]. At the same time, the continuous rise of passenger flow has also caused problems such as traffic congestion and scenic spot crowding, and the traditional tourism management methods can no longer cope with this situation, which has brought great trouble to the management of cities and scenic spots [4][5][6]. Seasonality, attractions and groups of different tourist attractions in the region are the typical characteristics of tourist flow, but with the increasing improvement of people's living standards, casualization of tourists has become a new trend [7][8].…”

Section: Introductionmentioning

confidence: 99%

Research on the Differentiation of Tourist Flow Network of Different Tourist Attractions in the Region

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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Tourist flow research is an important part of tourism research, providing the basis for the development of tourism. This paper takes different scenic spots in nine regions of M as the research object, takes social network analysis as the primary research method, and evaluates the node and overall network structure characteristics of tourist flow in M through the social node and overall network structure indicators. Using the “Octopus Collector” software to collect data, integrating 685 online travelogues about this region on the platform of related tourism websites, and using related software to process and analyze the data, it is found that there is a great deal of variability between different tourist attractions in M. From the viewpoint of node network structure, M1 scenic spot is in the center position between M tourist attractions, and among the 38 different scenic spots investigated in M area, there are only 6 attractions with extremely strong competitiveness, which can play the role of guiding the flow direction of tourist streams, and the others basically rely on the driving of the tourist volume of these tourist nodes in order to develop. From the overall network structure, the outward value, inward value, outward value and inward value close to the center potential of the degree center potential in the tourist flow network of M is greater than 30%, the overall scenic nodes of M are not closely connected, and the difference between the core scenic spots and the marginal scenic spots in terms of tourist flow is large.

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MobilityDL: a review of deep learning from trajectory data

Graser,

Jalali,

Lampert

et al. 2024

Geoinformatica

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Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).

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Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data

Cited by 5 publications

References 47 publications

Study of medium and long-term free flow capacity and queue discharge rates on roads

Study of medium and long-term free flow capacity and queue discharge rates on roads

Research on the Differentiation of Tourist Flow Network of Different Tourist Attractions in the Region

MobilityDL: a review of deep learning from trajectory data

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