Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts

This paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and complex road network. Motivated by a collaboration with Regione Lombardia, we focus on a subset of bridges in the region, although we aim at developing a method scalable to all road infrastructures of the regional network, consisting of more than 10,000 tunnels, bridges and overpasses. The final aim of the endeavor is to help decision-makers in prioritizing their interventions for maintaining and repairing infrastructure segments. We develop two different levels of impact assessment, both providing a unique global score for each bridge closure and investigating its spatio-temporal effects on mobility. We take advantage of a functional data analysis approach enhanced by a complex network theory perspective, thus modelling the roads of Lombardy as a network in which weights attributed to the edges are functional data. Results reveal the most critical bridges of Lombardy; moreover, for each bridge closure, the most impactful hours of the day and the most impacted municipalities of the region are identified. The proposed approach develops a flexible and scalable method for monitoring infrastructures of large and complex road networks.

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern.

RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

STRUCTURE OF THE STREET ROAD NETWORK IN THE FREIGHT TRANSPORTATION SYSTEM

The road network constitutes the main part of the transport system for both passenger and freight traffic. Achieving a sustainable road transport system including multimodal terminals, autonomous vehicles, intelligent transport systems, infrastructure are considered to be important design areas. The efficient use of the transport system is essential in terms of economic, environmental and social sustainability. Therefore, it is necessary to develop methods for optimizing the transportation process