scholarly journals Methods for division of road traffic network for distributed simulation performed on heterogeneous clusters

2013 ◽  
Vol 10 (1) ◽  
pp. 321-348 ◽  
Author(s):  
Tomas Potuzak
Keyword(s):  
Road Traffic ◽  
Distributed Simulation ◽  
Computation Time ◽  
Traffic Network ◽  
Traffic Networks ◽  
Computing Environment ◽  
Heterogeneous Cluster ◽  
Heterogeneous Clusters ◽  
The Road ◽  
Road Traffic Networks

The computer simulation of road traffic is an important tool for control and analysis of road traffic networks. Due to their requirements for computation time (especially for large road traffic networks), many simulators of the road traffic has been adapted for distributed computing environment where combined power of multiple interconnected computers (nodes) is utilized. In this case, the road traffic network is divided into required number of sub-networks, whose simulation is then performed on particular nodes of the distributed computer. The distributed computer can be a homogenous (with nodes of the same computational power) or a heterogeneous cluster (with nodes of various powers). In this paper, we present two methods for road traffic network division for heterogeneous clusters. These methods consider the different computational powers of the particular nodes determined using a benchmark during the road traffic network division.

scholarly journals Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7281
Author(s):  
Răzvan Andrei Gheorghiu ◽  
Valentin Iordache ◽  
Angel Ciprian Cormoș
Keyword(s):  
Road Network ◽  
Road Traffic ◽  
Data Gathering ◽  
Traffic Networks ◽  
Traffic Flows ◽  
Time Data ◽  
Road Vehicles ◽  
The Road ◽  
Big Picture ◽  
Road Traffic Networks

As road traffic networks become more congested and information systems are implemented to manage traffic flows, real-time data gathering becomes increasingly important. Classic detectors are placed in one point of the network and are able to provide information only from that area. As useful as this is, it lacks the big picture of the routes the vehicles usually travel. There are applications developed to help individuals make their way into the road network, but these are no solutions that deal with the cause of traffic; rather, they counteract the effects. It becomes obvious that a proper management system, with knowledge of all the relevant aspects will better serve all travelers. The detection solution proposed in this paper is based on Bluetooth detectors. This system is able to match detected devices in the road network, filter the results, and generate a vehicle count that is proved to follow RADAR detection results.

Influence ranking of road segments in urban road traffic networks

Computing ◽  
2020 ◽  
Vol 102 (11) ◽  
pp. 2333-2360
Author(s):  
Tarique Anwar ◽  
Chengfei Liu ◽  
Hai L. Vu ◽  
Md. Saiful Islam ◽  
Dongjin Yu ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Networks ◽  
Urban Road ◽  
Road Segments ◽  
Road Traffic Networks

scholarly journals On the robust guidance of users in road traffic networks

2015 ◽  
Author(s):  
Nadir Farhi ◽  
Habib Haj-Salem ◽  
Jean-Patrick Lebacque
Keyword(s):  
Road Traffic ◽  
Traffic Networks ◽  
Road Traffic Networks

scholarly journals Regional Patch Detection of Road Traffic Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xia Zhu ◽  
Weidong Song ◽  
Lin Gao
Keyword(s):  
Complex Network ◽  
Road Traffic ◽  
Geographic Distance ◽  
Influence Factors ◽  
Impact Factors ◽  
Traffic Network ◽  
The Road ◽  
Key Points ◽  
Path Distance ◽  
Shortest Path Distance

Road traffic network (RTN) structure plays an important role in the field of complex network analysis. In this paper, we propose a regional patch detection method from RTN via community detection of complex network. Firstly, the refined Adapted PageRank algorithm, which combines with the influence factors of the location property weight, the geographic distance weight and the road level weight, is used to calculate the candidate ranking results of key nodes in the RTN. Secondly, the ranking result and the shortest path distance as two significant impact factors are used to select the key points of the RTN, and then the Adapted K-Means algorithm is applied to regional patch detection of the RTN. Finally, based on the experimental data of Zhangwu road traffic network, the analysis results are as follows: Zhangwu is divided into 9 functional structures with key node locations as the core. Regional patch structure is divided according to key points, and the RTN is actually divided into nine small functional communities. Nine functional regional patches constitute a new network structure, maintaining connectivity between the regional patches can improve the overall efficiency of the RTN.

Stochastic Choice Behavior on Road Traffic Networks under Information Provision

2013 ◽  
Vol 361-363 ◽  
pp. 2173-2184 ◽  
Author(s):  
Jun Wang ◽  
Meng Liu ◽  
Hong Mei Zhou ◽  
Ying En Ge
Keyword(s):  
Individual Differences ◽  
Choice Behavior ◽  
Information Quality ◽  
Road Traffic ◽  
Information Provision ◽  
Traffic Information ◽  
Traffic Networks ◽  
Stochastic Choice ◽  
Information Interpretation ◽  
Road Traffic Networks

This paper attempts to model stochastic choice behavior in simultaneous trip route and departure time decision-making on road traffic networks, taking into account information quality and individual differences in information interpretation among the population of travelers. Different from the traditional stochastic model, the proposed choice behavior model assumes that road users simultaneously select the trip routes and departure times that have the largest probabilities of incurring the least generalized travel costs. This model is applicable in both static and dynamic settings and can be applied to both ordinary travelers as well as operators of emergent vehicles, e.g., the fire engine. The preliminary numerical experiments show that the proposed stochastic choice model can reflect the overreaction phenomena reported in studies of traffic information provision and the impacts of the types of traffic information on the effectiveness of information provision. This model opens a potential way to analyze network equilibrium behavior taking into account individual differences in the ability of information interpretation as well as information quality.

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