Integrating Mobile Vehicle Sensor Diagnostic Procedures into the Intelligent Transportation Network

2018 ◽  
Author(s):  
Nikola M. Slavkovic ◽  
Goran Zajic ◽  
Ana Gavrovska ◽  
Irini S. Reljin ◽  
Branimir D. Reljin ◽  
...  
Keyword(s):  
Transportation Network ◽  
Diagnostic Procedures ◽  
Intelligent Transportation ◽  
Mobile Vehicle ◽  
Vehicle Sensor

Research on Optimal Vehicle Path in Intelligent Transportation

2014 ◽  
Vol 644-650 ◽  
pp. 2615-2618
Author(s):  
Wen Ming Yu
Keyword(s):  
Data Storage ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Rapid Development ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Proposed Model ◽  
Planning Algorithm ◽  
Single Vehicle ◽  
Vehicle Path

The rapid development of economy and science and technology is quite obvious, which makes cars becoming more and more widely into the general family life. With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, there is the basic organizational framework for intelligent transportation, intelligent transportation network proposed model and its data storage structure, and the important influence on optimal path trajectory intelligent transportation planning. This paper analyzes intersection road network in the distribution based on computer graphics language, type and grade roads. The dynamic mathematical model of single vehicle is used vehicle planning algorithm based on period, effectively avoided the traffic road, reduce vehicle travel cost, improve the real-time effect and accuracy of the vehicle dynamic path. We hope the results and researches could combine with reality in order to reduce traffic congestion.

scholarly journals Application of Internet of Things Compressed Sensing and Information Interaction Technology in Intelligent Transportation Layout

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Na Li ◽  
Ze Wu ◽  
Zhongbiao Zhao
Keyword(s):  
Internet Of Things ◽  
Compressed Sensing ◽  
Data Acquisition ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Reconstruction Algorithm ◽  
Intelligent Transportation ◽  
Urban Traffic ◽  
Continuous Development ◽  
Information Interaction

With the continuous development of economy, the number of motor vehicles has increased sharply, and urban traffic congestion has become more and more frequent. Urban traffic congestion has become one of the important reasons that hinder the continuous development of major cities. In order to better solve the problem of urban traffic congestion, it is more urgent to build urban intelligent transportation system. The technology related to Internet of things is becoming more and more mature, which has become a new idea of collecting traffic information, providing convenience for traffic, and injecting vitality into the study of intelligent transportation layout. The traffic system in the city or the main traffic intersection, through the traffic guidance layout road, provides the traffic instruction for the passenger on the highway network, enables the driver to choose the suitable driving road, can provide the travel guidance service for the driver, adjusts the flow distribution, and improves the traffic condition. This paper describes the research status of intelligent transportation layout and uses the basic theory of compressed sensing and information interaction technology to carry out data fusion and reconstruction algorithm in intelligent transportation. Finally, the application of compressed sensing in intelligent transportation layout is summarized to realize data collection in intelligent transportation network. In this paper, compressed sensing theory is applied to the data acquisition of intelligent transportation network to reduce the amount of data and improve the effective data acquisition. The reconstruction algorithm is used to realize the data reconstruction to ensure the accuracy and stability of signal reconstruction and the accuracy of network transmission information.

scholarly journals Bi-Level Optimization as a Tool for Implementation of Intelligent Transportation Systems

2017 ◽  
Vol 17 (2) ◽  
pp. 97-105 ◽  
Author(s):  
Krasimira Stoilova ◽  
Todor Stoilov ◽  
Vladimir Ivanov
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Transportation Network ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Formal Description ◽  
Traffic Flows ◽  
Level Control ◽  
Traffic Lights ◽  
Level Problem

Abstract The Intelligent Transportation System (ITS) is used as a term for integrating requirements and functionalities towards transportation systems, which in urban environment raises complex exploitation and control problems. Important part of the ITS is the control which has to be applied for traffic flows. The control processes are strongly linked with requirements and targets for optimization of the transportation behavior. The paper applies new optimization formal description of control by bi-level optimization. Except the trivial traffic lights control, the bi-level formalization allows additional traffic characteristics to be defined like maximal/minimal values. The paper defines, solves and provides numerical simulations for minimization of the vehicle queues in front of the traffic lights. Such bi-level optimization problem is applied simultaneously for maximization the traffic flows on arterial and important directions of the urban transportation network. The formal description of the bi-level problem is provided. The results of the bi-level control have been compared with the cases of single optimization of the vehicles queues. The simulation results prove that the bi-level problem gives benefits satisfying an additional goal, which improves additional characteristic of the transport behavior. The bi-level optimization formalism can be used as a tool for implementation of integration of ITS control policies.

scholarly journals Digital Twin System of Bridges Group based on Machine Vision Fusion Monitoring of Bridge Traffic Load

2021 ◽  
Author(s):  
Danhui Dan ◽  
Yufeng Ying ◽  
Liangfu Ge
Keyword(s):  
Machine Vision ◽  
Transportation Network ◽  
Physical Space ◽  
Intelligent Transportation ◽  
Transportation Infrastructure ◽  
Traffic Load ◽  
Digital Twin ◽  
Twin System ◽  
Traffic Loads ◽  
Infrastructure System

Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects.

scholarly journals In-vehicle sensor-assisted platoon formation by utilizing vehicular communications

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771875 ◽  
Author(s):  
Dongliang Su ◽  
Sanghyun Ahn
Keyword(s):  
Intelligent Transportation System ◽  
Sensory Information ◽  
Intelligent Transportation ◽  
Fuel Saving ◽  
Vehicular Communications ◽  
Related Information ◽  
Road Capacity ◽  
The Stability ◽  
Vehicle Sensors ◽  
Vehicle Sensor

These days, vehicles are equipped with in-vehicle sensors so that they can collect the situation-related information, such as velocity, heading, and position. From the viewpoint of intelligent transportation system, platooning is getting more attention because of its advantages of increased road capacity, fuel saving, safety, and so on. In this article, we propose a mechanism that allows vehicles to form platoons based on their own and 1-hop neighbors’ sensory information obtained from vehicular communications. Within a platoon, the front vehicle is the leader whose role is leading the platoon, so the stability of the leader is the most critical performance factor of platooning. To achieve this, we propose to use relative mobility information of vehicles computed from the sensory information for the leader election. The performance of our platooning mechanism is evaluated by carrying out ns-3-based simulations, especially from the perspective of leader stability and road capacity.

Tumor Diagnosis

1982 ◽  
Vol 40 ◽  
pp. 262-265
Author(s):  
Bruce Mackay
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Differential Diagnosis ◽  
Light Microscopy ◽  
Clinical Examination ◽  
Ultrastructural Study ◽  
Diagnostic Procedures ◽  
Specific Diagnosis ◽  
Transmission Electron ◽  
Microscopic Diagnosis

The broadest application of transmission electron microscopy (EM) in diagnostic medicine is the identification of tumors that cannot be classified by routine light microscopy. EM is useful in the evaluation of approximately 10% of human neoplasms, but the extent of its contribution varies considerably. It may provide a specific diagnosis that can not be reached by other means, but in contrast, the information obtained from ultrastructural study of some 10% of tumors does not significantly add to that available from light microscopy. Most cases fall somewhere between these two extremes: EM may correct a light microscopic diagnosis, or serve to narrow a differential diagnosis by excluding some of the possibilities considered by light microscopy. It is particularly important to correlate the EM findings with data from light microscopy, clinical examination, and other diagnostic procedures.

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