Fast Spatiotemporal Learning Framework for Traffic Flow Forecasting

Guo, Canyang; Chen, Chi‐Hua; Hwang, Feng-Jang; Chang, Ching-Chun; Chang, Chin‐Chen

doi:10.1109/tits.2022.3224039

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…To enhance road traffic efficiency, scholars have conducted extensive research on traffic flow characteristics and developed numerous models, including the cellular automata model [10,11] and the viscoelastic model [12]. Researchers have also explored intelligent traffic management strategies [13,14], big data analysis [15,16] and traffic flow prediction methods [17,18] to alleviate traffic congestion, yielding notable results. However, the crux of traffic problems lies in the rationality of the road network structure.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow

et al. 2023

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In order to promote the sustainable development of urban traffic systems, improve the accuracy of traffic system analysis in the urban planning stage and reduce the possibility of traffic congestion in the operation stage of road networks, the coupling relationship and evolution mechanism between urban road spatial structure and traffic flow were studied, and a model of the relationship between the metrics was established in this study based on real road network and traffic flow data. First, the road spatial structure model of the study area was established from the perspective of road space, and the spatial syntax method was applied to verify the rationality of the spatial structure of the road network. Secondly, the initial OD matrix was determined by OD backpropagation based on the measured traffic flow data. Thirdly, the coupling rule between the spatial structure and the traffic flow of the road network was explored by loading the increment in the OD matrix to the initial OD matrix step by step based on a simulation experiment. Finally, the relationship between the degree of integration of the spatial syntactic feature parameter and the saturation of the traffic flow feature parameter was modelled on the basis of experimental results and verified by an example. This research shows that the spatial structure of urban roads has a significant impact on the characterisation of the traffic flow distribution of road networks, and a strong correlation can be found between the integration degree and saturation degree. An optimal fit, which can be used as a reference for the design of road spatial structure, was explored in this research.

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