Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network

Jin, Guangyin; Cui, Yan; Zeng, Lijiang; Tang, Hanbo; Feng, Yanghe; Huang, Jincai

doi:10.1016/j.trc.2020.102665

Cited by 92 publications

(44 citation statements)

References 26 publications

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“…The use of grid matrices to represent graph convolutional neural networks have been applied in many previous studies (Jin et al, 2020 ). The data model of the grid matrix can better represent non-Euclidean structural features and capture local spatial features.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

et al. 2022

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Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the road network has high nonlinearity and complexity, and also, the traffic flow is dynamic due to the continuous changing of the traffic environment. Thus, it is very important to model the spatial relation and temporal dependence simultaneously to simulate the true traffic conditions. Most of the existing destination prediction methods have limited ability to model large-scale spatial data that changes dynamically with time, so they cannot obtain satisfactory prediction results. This paper proposes a human-in-loop Spatial-Temporal Attention Mechanism with Graph Convolutional Network (STAGCN) model to explore the spatial-temporal dependencies for destination prediction. The main contributions of this study are as follows. First, the traffic network is represented as a graph network by grid region dividing, then the spatial-temporal correlations of the traffic network can be learned by convolution operations in time on the graph network. Second, the attention mechanism is exploited for the analysis of features with loop periodicity and enhancing the features of key nodes in the grid. Finally, the spatial and temporal features are combined as the input of the Long-Short Term Memory network (LSTM) to further capture the spatial-temporal dependences of the traffic data to reach more accurate results. Extensive experiments conducted on the large scale urban real dataset show that the proposed STAGCN model has achieved better performance in urban car-hailing destination prediction compared with the traditional baseline models.

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Section: Proposed Methodsmentioning

confidence: 99%

“…We use the reciprocal of the center distance to mark the dependency weight between the two regions. In this way, the closer the distance, the higher the weight value and the stronger the dependence (Jin et al, 2020 ). The weight equation is shown in Equation (4), the region adjacency matrix is shown in Equation (5).…”

Section: Proposed Methodsmentioning

confidence: 99%

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

et al. 2022

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“…A method called Deep Transport is proposed in [64] which combines convolutional neural network (CNN) and recurrent neural network (RNN) architectures equipped with an attention mechanism to predict traffic volume. Recently, graph neural networks (GNNs), a class of DL networks performing inference over arbitrary graphs are proven to yield superior performance in predicting trafficrelated parameters [65,66,67,68,69].…”

Section: Related Work On Network-level Analysismentioning

confidence: 99%

Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study

Chen¹,

Wang²,

Razi³

et al. 2022

Preprint

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Driving safety analysis has recently witnessed unprecedented results due to advances in computation frameworks, connected vehicle technology, new generation sensors, and artificial intelligence (AI). Particularly, the recent advances performance of deep learning (DL) methods realized higher levels of safety for autonomous vehicles and empowered volume imagery processing for driving safety analysis. An important application of DL methods is extracting driving safety metrics from traffic imagery. However, the majority of current methods use safety metrics for micro-scale analysis of individual crash incidents or near-crash events, which does not provide insightful guidelines for the overall network-level traffic management. On the other hand, large-scale safety assessment efforts mainly emphasize spatial and temporal distributions of crashes, while not always revealing the safety violations that cause crashes. To bridge these two perspectives, we define a new set of network-level safety metrics for the overall safety assessment of traffic flow by processing imagery taken by roadside infrastructure sensors. An integrative analysis of the safety metrics and crash data reveals the insightful temporal and spatial correlation between the representative network-level safety metrics and the crash frequency. The analysis is performed using two video cameras in the state of Arizona along with a 5-year crash report obtained from the Arizona Department of Transportation. The results confirm that network-level safety metrics can be used by the traffic management teams to equip traffic monitoring systems with advanced AI-based risk analysis, and timely traffic flow control decisions.

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“…It has proven to be successful in the areas, such as pattern recognition and classification in uniformly structured data, such as images, audio, and texts [35]. Additionally, applications of various deep learning models are becoming increasingly used in the analysis of spatio-temporal predictions, commonly seen in the field of traffic and transportation [36][37][38], ecology [39], and economics [40]. Yet, most natural phenomena or decentralized systems are heterogeneous, and they are represented by irregularly structured graphs, such as social network graphs and traffic trajectories [41].…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

A New Graph-Based Fractality Index to Characterize Complexity of Urban Form

Seipel

Brandt

et al. 2022

IJGI

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Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index have been proposed to measure this complexity. However, as these indicators are statistical rather than spatial, they result in an inability to characterize the spatial complexity of urban forms, such as building footprints. To overcome this problem, this paper proposes a graph-based fractality index (GFI), which is based on a hybrid of fractal theory and deep learning techniques. First, to quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Next, building footprints in London were used to test the method, where the results showed that the proposed framework performed better than the traditional indices, i.e., the index is capable of differentiating complex patterns. Another advantage is that it seems to assure that the trained deep learning is objective and not affected by potential biases in empirically selected training datasets Furthermore, the possibility to connect fractal theory and deep learning techniques on complexity issues opens up new possibilities for data-driven GIS science.

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Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network

Cited by 92 publications

References 26 publications

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study

A New Graph-Based Fractality Index to Characterize Complexity of Urban Form

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