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

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

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.

scholarly journals Macroscopic dynamics and the collapse of urban traffic

2018 ◽  
Vol 115 (50) ◽  
pp. 12654-12661 ◽  
Author(s):  
Luis E. Olmos ◽  
Serdar Çolak ◽  
Sajjad Shafiei ◽  
Meead Saberi ◽  
Marta C. González
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Spatial Dynamics ◽  
Urban Traffic ◽  
Nonequilibrium Phase Transition ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Road Capacity ◽  
Total Vehicle ◽  
The Individual

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

scholarly journals Determination of Reserves for Reducing Delays and Increasing the Capacity of the Street-Road Network in Gomel as a Factor of Reducing Losses in Traffic

2021 ◽  
pp. 65-72
Author(s):  
D. P. Khodoskin
Keyword(s):  
Road Network ◽  
Urban Traffic ◽  
Economic Losses ◽  
Value Assessment ◽  
Large Cities ◽  
The Road ◽  
Car Traffic ◽  
Traffic Capacity ◽  
Real Objects ◽  
Time Expenditures

Purpose. Often, the existing level of traffic capacity of road network facilities in large cities is insufficient. This is often due to the fact that urban growth is significantly ahead of the reconstruction and renovation of the corresponding infrastructure. As a result, traffic delays of various kinds occur on city roads, accompanied, first of all, by economic losses. Therefore, the search for reserves to reduce various types of losses associated with insufficient traffic capacity of the road network when organizing urban traffic is the purpose of this work. Methodology To determine the reserves for increasing the traffic capacity of the road network and reducing various kinds of delays, the method of deterministic analysis was used, the method for calculating the cycle according to F. Webster, based on the use of phase coefficients and time lost in the cycle (as the sum of transient intervals), the method for measuring the intensity of car traffic in the traffic flow, as well as the methodology for calculating economic losses arising from delays in the movement of vehicles. Findings. A study of delays and time expenditures and the corresponding economic losses that occur at typical objects of the city's street-road network (regulated intersections) has been carried out. The reserves of their reduction, and as a consequence, the increase in the capacity of both individual sections and the city's road network as a whole, have been determined. Originality. The use of this method on real objects of the road network allows developing the scientific interpretation of the methods used and expanding the scope of their application. Practical value. Assessment of emerging problems of traffic capacity and associated losses (including economic ones) makes it possible to determine the most promising ways to determine the traffic capacity reserves and, as a result, reduce economic losses.

Taxi Global Positioning System Data in Urban Road Network: A Methodology to Identify Key Road Clusters Based on Travel Speed–Traffic Volume Correlation

2021 ◽  
pp. 036119812110366
Author(s):  
Yi Li ◽  
Weifeng Li ◽  
Qing Yu ◽  
Han Yang
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Travel Speed ◽  
Detection Algorithm ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Mitigation Strategies ◽  
Operating Characteristics ◽  
The Road ◽  
Road Segments

Urban traffic congestion is one of the urban diseases that needs to be solved urgently. Research has already found that a few road segments can significantly influence the overall operation of the road network. Traditional congestion mitigation strategies mainly focus on the topological structure and the transport performance of each single key road segment. However, the propagation characteristics of congestion indicate that the interaction between road segments and the correlation between travel speed and traffic volume should also be considered. The definition is proposed for “key road cluster” as a group of road segments with strong correlation and spatial compactness. A methodology is proposed to identify key road clusters in the network and understand the operating characteristics of key road clusters. Considering the correlation between travel speed and traffic volume, a unidirectional-weighted correlation network is constructed. The community detection algorithm is applied to partition road segments into key road clusters. Three indexes are used to evaluate and describe the characteristic of these road clusters, including sensitivity, importance, and IS. A case study is carried out using taxi GPS data of Shanghai, China, from May 1 to 17, 2019. A total of 44 key road clusters are identified in the road network. According to their spatial distribution patterns, these key road clusters can be classified into three types—along with network skeletons, around transportation hubs, and near bridges. The methodology unveils the mechanism of congestion formation and propagation, which can offer significant support for traffic management.

Role of road network features in the evaluation of incident impacts on urban traffic mobility

2018 ◽  
Vol 117 ◽  
pp. 101-116 ◽  
Author(s):  
Chenshuo Sun ◽  
Xin Pei ◽  
Junheng Hao ◽  
Yewen Wang ◽  
Zuo Zhang ◽  
...  
Keyword(s):  
Road Network ◽  
Urban Traffic

The Study of Road Network Capacity Based on Unblocked Reliability

2014 ◽  
Vol 505-506 ◽  
pp. 548-553
Author(s):  
Gui Bao Lin ◽  
Rong Guo Ma ◽  
Han Yue Deng ◽  
Guo Hua Liang
Keyword(s):  
Road Network ◽  
Network Capacity ◽  
Network Control ◽  
Urban Traffic ◽  
Reserve Capacity ◽  
Urban Network ◽  
Structural Capacity ◽  
Simulation Results ◽  
New Perspective ◽  
Capacity Constrained

Recently, to keep the network unblocked becomes more and more important, because that the urban traffic has the serious congestion. The model of network capacity is demonstrated with the concept unblocked reliability and reserve capacity. The simulation results shows that actual network capacity is less than its structural capacity constrained by the network control, which gives a new perspective for the future urban network planning.

scholarly journals Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4093 ◽  
Author(s):  
Hao Lu ◽  
Kaize Shi ◽  
Yifan Zhu ◽  
Yisheng Lv ◽  
Zhendong Niu
Keyword(s):  
Single Channel ◽  
Low Cost ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Social Signals ◽  
Fusion Model ◽  
Online Data ◽  
Traffic Incidents ◽  
Traffic Incident

Social sensors perceive the real world through social media and online web services, which have the advantages of low cost and large coverage over traditional physical sensors. In intelligent transportation researches, sensing and analyzing such social signals provide a new path to monitor, control and optimize transportation systems. However, current research is largely focused on using single channel online social signals to extract and sense traffic information. Clearly, sensing and exploiting multi-channel social signals could effectively provide deeper understanding of traffic incidents. In this paper, we utilize cross-platform online data, i.e., Sina Weibo and News, as multi-channel social signals, then we propose a word2vec-based event fusion (WBEF) model for sensing, detecting, representing, linking and fusing urban traffic incidents. Thus, each traffic incident can be comprehensively described from multiple aspects, and finally the whole picture of unban traffic events can be obtained and visualized. The proposed WBEF architecture was trained by about 1.15 million multi-channel online data from Qingdao (a coastal city in China), and the experiments show our method surpasses the baseline model, achieving an 88.1% F1 score in urban traffic incident detection. The model also demonstrates its effectiveness in the open scenario test.

scholarly journals Optimization of ITS Construction Scheme for Road Network under the Restriction of Different Transports’ Passenger Person-Kilometers

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ming-wei Li
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Intelligent Transportation System ◽  
Urban Traffic ◽  
Negative Effects ◽  
Application Efficiency ◽  
Construction Scheme ◽  
Transport Modes ◽  
Personal Transportation ◽  
Research Challenge

Diversified transport modes and increased personal transportation demands have increased in urban traffic problems such as traffic congestion and environmental pollution. To cope with traffic problems, advanced transportation technologies are being developed as intelligent transportation system (ITS). There is a growing trend to coordinate varying kinds of transportation modes. However, the effective construction and application of ITS in urban traffic can be affected by many factors, such as transport mode. Therefore, how to reasonably construct ITS by consideration of different transport modes’ characteristics and requirements is an important research challenge. Additionally, both costs and negative effects must be minimized and application efficiency is required to be optimal in the construction process. To address these requirements, a multiobjective optimization model and a fuzzy selecting optimum model were combined to study the construction scheme based on optimization results. The empirical analysis of Beijing, China, suggested several considerations for improvements to future road network ITS construction with controlled costs. Finally, guidelines are proposed to facilitate ITS construction, improve ITS application efficiency, and transform and innovate strategies to cope with urban traffic.

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