Applying Ollivier-Ricci curvature to indicate the mismatch of travel demand and supply in urban transit network

Wang, Yaoli; Huang, Zhou; Yin, Ganmin; Li, Haifeng; Liu, Yang; Su, Yuelong; Yu, Liu; Shan, Xv

doi:10.1016/j.jag.2021.102666

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Ollivier considers the relation between the transport distance between nodes and the average transport distance between neighbors of nodes and defines the Ollivier-Ricci curvature [10] based on the optimal transport theory. Numerous works show that Ollivier-Ricci curvature can be used to reveal the community structures in complex networks [27], the fragility of road network topology [28], the supply-demand mismatch in transportation networks [29], and the network congestion phenomenon [30], etc. Forman defines the Forman-Ricci curvature [31] based on the theoretical framework of CW complex, which can be applied to such fields as network clustering, network extrapolation [32], and image processing [33].…”

Section: Connection Between Discrete Ricci Curvature and Network Prop...mentioning

confidence: 99%

Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

et al. 2023

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Traffic flow forecasting is a basic function of intelligent transportation systems, and the accuracy of prediction is of great significance for traffic management and urban planning. The main difficulty of traffic flow predictions is that there is complex underlying spatiotemporal dependence in traffic flow; thus, the existing spatiotemporal graph neural network (STGNN) models need to model both temporal dependence and spatial dependence. Graph neural networks (GNNs) are adopted to capture the spatial dependence in traffic flow, which can model the symmetric or asymmetric spatial relations between nodes in the traffic network. The transmission process of traffic features in GNNs is guided by the node-to-node relationship (e.g., adjacency or spatial distance) between nodes, ignoring the spatial dependence caused by local topological constraints in the road network. To further consider the influence of local topology on the spatial dependence of road networks, in this paper, we introduce Ollivier–Ricci curvature information between connected edges in the road network, which is based on optimal transport theory and makes comprehensive use of the neighborhood-to-neighborhood relationship to guide the transmission process of traffic features between nodes in STGNNs. Experiments on real-world traffic datasets show that the models with Ollivier–Ricci curvature information outperforms those based on only node-to-node relationships between nodes by ten percent on average in the RMSE metric. This study indicates that by utilizing complex topological features in road networks, spatial dependence can be captured more sufficiently, further improving the predictive ability of traffic forecasting models.

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Section: Connection Between Discrete Ricci Curvature and Network Prop...mentioning

confidence: 99%

Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

et al. 2023

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“…These big geo-data include mobile phone positioning data, smart card data, taxi trajectory data, etc (Bao et al, 2021; Wang et al, 2022). Compared with traditional questionnaires, big geo-data has the advantages of wide-coverage, low-cost, high-resolution and has been widely used in human mobility, traffic optimization, urban planning (Chen et al, 2018; Liu et al, 2015; Wang et al, 2020, 2022; Yin et al, 2022), which also provides us with a new perspective for studying the attractiveness of commercial agglomerations. The most intuitive way to measure the attractiveness of a thing is to see how people react to it.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Huang

Yin

Xia

et al. 2023

Environment and Planning B: Urban Analytics and City Science

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Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning.

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“…The interaction between infrastructure supply and travel origins/destination demand determines public transportation travel experiences [2]. Public transit services become inefficient upon urbanisation for three main reasons: 1) demand fluctuates over time, 2) supply is relatively stagnant over extended periods, and 3) output cannot be stored [3,4]. Hence, it is important to study transit ridership to keep up with the transformations in urban planning and highly dynamic passenger demand.…”

Section: Introductionmentioning

confidence: 99%

Correlation analysis and regression of transit ridership by means of public buses – A preliminary data analysis

Karunakaran,

Yuen,

Chuah

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Given ever-increasing private transportation ownership, a rising population, and unceasing mobility, it is crucial to ensure the usage and improvement of public transportation services. Therefore, it is important to review and understand relationships between variables affecting ridership to boost them. This paper acts as a preliminary data analysis in which correlation and multiple linear regression (MLR) analyses were constructed to examine bus transit ridership. The main goal of this study is to identify the variables that influence transit ridership and the degree to which they do so during a) peak hours and b) off-peak hours. Independent variables such as bus commercial speed, service frequency, in-vehicle travel time, bus stop distance, and rainfall were employed in this study. Results show that all the independent variables are significant and correlated with ridership. However, the regression models show that distance and travel time are not statistically significant during peak and off-peak hours, respectively. The findings of this study recommend the creation of relevant policy initiatives that can assist transit agencies in increasing ridership by analysing significant predictive factors. Additionally, the output from this study can be used in future works on designing efficient bus routes, stop locations, and even bus schedules.

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Applying Ollivier-Ricci curvature to indicate the mismatch of travel demand and supply in urban transit network

Cited by 4 publications

References 24 publications

Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Correlation analysis and regression of transit ridership by means of public buses – A preliminary data analysis

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