scholarly journals The Model and Algorithm of the Traffic Flow Assignment in Road Network

2012 ◽  
Author(s):  
Yanxia LIU ◽  
Qinzhen LI ◽  
Kai YIN
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Flow Assignment

scholarly journals Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7341
Author(s):  
Xueying Song ◽  
Zheng Yang ◽  
Tao Wang ◽  
Chaoyang Li ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Assignment ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Urban Road ◽  
Assignment Model ◽  
Flow Assignment ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

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 Traffic Flow Pattern in Road Network Using Clustering

2020 ◽  
Vol 5 (8) ◽  
pp. 229-230
Author(s):  
Ganga Gudi ◽  
Dr. Hanumanthappa M
Keyword(s):  
Traffic Flow ◽  
Global Positioning System ◽  
Road Network ◽  
Location Based Services ◽  
Positioning System ◽  
Specific Time ◽  
Real World Problem ◽  
Time Period ◽  
Global Positioning ◽  
Enormous Amount

Wireless communication has become important in location-based services. The enormous amount of data is extracted for useful information to solve the real world problem. Global positioning system, is used to captures the position of an object at specific time period. The scheme is finding the congested route by considering the number of vehicles in a road segment. It consists of two methods, firstly it finds the group of points based on consistency of route points and second it arranges the groups in sequence of values for each route

scholarly journals SCALE-FREE NETWORK THEORY IN STUDYING THE STRUCTURE OF THE ROAD NETWORK IN POLAND

2014 ◽  
Vol 26 (3) ◽  
pp. 235-242 ◽  
Author(s):  
Katarzyna KOCUR-BERA
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Network Theory ◽  
Road Network ◽  
Scale Free Network ◽  
Scale Free ◽  
The Road ◽  
Normal Character ◽  
Free Network ◽  
The Relationship

This paper discusses the issue of statistical analysis of traffic flow in different regions of Poland. Such analysis allows us to identify “valuable (sensitive) areas” whose damage or blockage may provoke considerable disturbances or even a stoppage of traffic flow in the examined road network. The results of the studies indicate that the road network in Poland has the properties of a scale-free network. The distribution of the examined variables does not have a normal character, whereas the relationship between the number of nodes and the number of connections is a power-law feature. 

Steady-state solution of traffic flow on a simple road network

2021 ◽  
Vol 33 (5) ◽  
pp. 950-957
Author(s):  
Yu-pei Lyu ◽  
Ming-min Guo ◽  
Peng Zhang ◽  
Rui Fang ◽  
Zhi-yang Lin ◽  
...  
Keyword(s):  
Steady State ◽  
Traffic Flow ◽  
Road Network ◽  
Steady State Solution ◽  
State Solution
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