scholarly journals An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Deepa Devasenapathy ◽  
Kathiravan Kannan
Keyword(s):  
Energy Efficient ◽  
Road Network ◽  
Polynomial Regression ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Traffic Information ◽  
Data Set ◽  
The Road

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

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.

Nodes' Adjacency Relation of Video Sensor Network Based on GIS and GPS

2014 ◽  
Vol 687-691 ◽  
pp. 3675-3678
Author(s):  
Guo Liang Tang ◽  
Zhi Jing Liu ◽  
Jing Xiong
Keyword(s):  
Sensor Network ◽  
Road Network ◽  
Large Scale ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Sorting Algorithm ◽  
The Road ◽  
Video Sensors ◽  
Video Sensor ◽  
Relationship Of

As far as the large-scale video surveillance sensor network in urban road and highway, the relay-surveillance on abnormal behavior or particular targets is one of the hot focuses of in recent researches, while the establishment of adjacency relationship of the neighbor sensor nodes is the basis of the sensor scheduling for the relay-surveillance. The topology of a road network is generated according to the road information, which has already existed in the geographic information systems (GIS) regarding the road intersections as nodes and the section between the two intersections as the edge. The initial topology of relay-adjacency relationship between sensors is built by that each video sensor is deployed at the each intersection and the section of the each two intersections is regarded as the basis of adjacency between the each two sensors. When a new video sensor is to be deployed in a section of a road, the related deployed sensors in same section are searched by using the spatial index of GIS based on its GPS information, and then the adjacency relationship between the new sensor and the related ones is generated by using the sorting algorithm according to their GPS information. By using the road network information that has already existed in the GIS system, the algorithm on establishing the relay-adjacency relationship of video sensors is simple and simpler to implement, and it can be used in the construction of sensor relay-surveillance topology such as automatic real-time tracking on abnormal behavior or the analysis of the escape routes and so on in city roads, highway, smarter cities and smarter planet.

scholarly journals CHAIN-WISE GENERALIZATION OF ROAD NETWORKS USING MODEL SELECTION

2017 ◽  
Vol IV-1/W1 ◽  
pp. 59-66 ◽  
Author(s):  
D. Bulatov ◽  
S. Wenzel ◽  
G. Häufel ◽  
J. Meidow
Keyword(s):  
Model Selection ◽  
Similarity Criteria ◽  
Sensor Data ◽  
Model Complexity ◽  
Complex Data ◽  
Data Set ◽  
The Road ◽  
Straight Line ◽  
Geometric Entity ◽  
Urban Terrain

Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using <ttt>circlePeucker</ttt> and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.

scholarly journals A Study on the Coordination of Urban Traffic Control and Traffic Assignment

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
ZhaoWei Qu ◽  
Yan Xing ◽  
XianMin Song ◽  
YuZhou Duan ◽  
Fulu Wei
Keyword(s):  
Traffic Control ◽  
Network Flow ◽  
Road Network ◽  
Traffic Assignment ◽  
Route Choice ◽  
Flow Distribution ◽  
Urban Traffic ◽  
Network Efficiency ◽  
The Road ◽  
Value Model

The interactions between signal setting and traffic assignment can directly affect the urban road network efficiency. In order to improve the coordination of signal setting with traffic assignment, this paper created a traffic control algorithm considering traffic assignment; meanwhile, the link impedance function and the route choice function were introduced into this paper to study the user's route choice and the road network flow distribution. Then based on the above research, we created a system utility value model. Finally through the VISSIM software to simulate the test network, we verified the superiority of the coordination algorithm and the model and gave the optimal flow of the road network.

scholarly journals Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Lin Dong ◽  
Akira Rinoshika ◽  
Zhixian Tang
Keyword(s):  
Road Network ◽  
Entropy Method ◽  
Urban Traffic ◽  
Traffic Information ◽  
Gated Community ◽  
Entropy Model ◽  
Evaluation Standard ◽  
Traffic System ◽  
Traffic State ◽  
Selection Framework

The opening of a gated community to expand the micro-road network in an urban traffic system is an importance research topic related to urban congestion. To satisfy the demands of opening an early choosing case, this paper proposes a comprehensive selection framework on qualified communities and their appropriate opening times by describing the traffic state at the boundary road network accurately. The traffic entropy model and fuzzy c-means (FCM) method are used in this paper. In the framework, a new opening evaluation entropy model is built using basic theory of the thermodynamic traffic entropy method. The traffic state entropy values of the boundary road network and entropy production are calculated to determinate the opening time. In addition, a specific fuzzy range evaluation standard at a preset gated community is drawn with an FCM algorithm to verify the opening determination. A case study based on the traffic information in a simulated gated community in Shanghai is evaluated and proves that the findings of opening evaluation are in accordance with the actual situation. It is found that the micro-inter-road network of a gated community should be opened as the entropy value reaches 2.5. As the travel time is less than 20 s, the correlation between the opening entropy value and the journey delay time exhibits a good linear correlation, which indicates smooth traffic flow.

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