• Computers and Concrete
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• v.32 no.4
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• pp.411-423
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• 2023

The rapid development of road transport has increased the number of bridges that require widening. A critical issue in the construction of bridge widening is the influence of vibrations of the old bridge on the casting of wet joint concrete between the old and new bridges owing to the running traffic. Typically, the bridge is closed to traffic during the pouring of wet joint concrete, which negatively affects the existing transportation network. In this study, a newly developed microscopic traffic load modeling approach and the vehicle-bridge interaction theory are incorporated to develop a refined numerical framework for the analysis of random traffic-bridge coupled dynamics. This framework was used to investigate traffic-induced vibrations at the wet joint of a widened bridge. Based on an experimental study on the vibration resistance of wet joint concrete, traffic control strategies were proposed to ensure the construction performance of cast-in-site wet joint concrete under random traffic without interruption. The results show that the vibration displacement and frequency of the old bridge, estimated by the proposed framework, were comparable with those obtained from field measurements. Based on the target peak particle velocity and vibration amplitude of the wet joint concrete, it was found that traffic control measures, such as limiting vehicle gross weight and limiting traffic volume by closing an additional traffic lane, could ensure the construction performance of the wet joint concrete.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.161-170
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• 2013

Deployment of green transportation in reverse logistics is a key issue for low carbon technologies. To cope with such logistic innovation, this paper proposes a hybrid approach to solve practical vehicle routing problem (VRP) of pickup type that is common when considering the reverse logistics. Noticing that transportation cost depends not only on distance traveled but also on weight loaded, we propose a hierarchical procedure that can design an economically efficient reverse logistics network even when the scale of the problem becomes very large. Since environmental concerns are of growing importance in the reverse logistics field, we need to reveal some prospects that can reduce $CO_2$ emissions from the economically optimized VRP in the same framework. In order to cope with manifold circumstances, the above idea has been deployed by extending the Weber model to the generalized Weber model and to the case with an intermediate destination. Numerical experiments are carried out to validate the effectiveness of the proposed approach and to explore the prospects for future green reverse logistics.

• Journal of Korean Society of Transportation
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• v.14 no.4
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• pp.91-106
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• 1996

This paper deals with on the efficiency questions of the left-turn prohibit at an isolated intersection and a corridor with 5-phase signal system. Its objectives are three-fold ; (1) to analyze the efficiency of the left-turn prohibit with the use of an imaginary network, (2) to evaluate various factors under consideration in decision making on the left-turn prohibit, (3) to provide a framework for estimating and evaluating overall impacts of the left-turn prohibit in traffic network. the major findings using an imaginary network and computer packages such as MINUTP, TRANSYT-7F and STATGRAPH are followings. First, left-turn prohibit reduces cycle length by 33 seconds and delay time per vehicle by 36 seconds at an isolated intersection, and cycle length by 31 seconds and delay time per veicle by 43 seconds along a corridor. Second, total vehicle mile of travel and total travel time at an isolated intersection seem up to increase 38.85 miles(57.36km), 14.4 hour on the average, Regarding to a corridor, total vehicle mile of travel is increased by 50.14 miles(80.22km), but total travel time is decreased by129.9 hours. Third, the efficiency of left-turn prohibit are affected the following eight factors including left-turn volume(veh/hr) and ratio(%), average delay time per vehicle(sec/veh) and others. Finally, several simple and multiple regression models to evaluate the impacts on the left-turn prohibit are formulated from the above eight factors. It can be expected that these models will take an important role in decision-making of left-turn prohibit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1533-1556
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• 2017

The concept of trust in vehicular ad hoc networks (VANETs) is usually utilized to assess the trustworthiness of the received data as well as that of the sending entities. The quality of safety applications in VANETs largely depends on the trustworthiness of exchanged data. In this paper, we propose a self-organized distributed trust computing framework (DTCF) for VANETs to compute the trustworthiness of each vehicle, in order to filter out malicious nodes and recognize fully trusted nodes. The proposed framework is solely based on the investigation of the direct experience among vehicles without using any recommendation system. A tier-based dissemination technique for data messages is used to filter out non authentic messages and corresponding events before even going farther away from the source of the event. Extensive simulations are conducted using Omnet++/Sumo in order to investigate the efficiency of our framework and the consistency of the computed trust metrics in both urban and highway environments. Despite the high dynamics in such networks, our proposed DTCF is capable of detecting more than 85% of fully trusted vehicles, and filtering out virtually all malicious entities. The resulting average delay to detect malicious vehicles and fraudulent data is showed to be less than 1 second, and the computed trust metrics are shown to be highly consistent throughout the network.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.67-77
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• 2021

Space development has been led by the state due to huge investment, risks of development failure, and restrictions on international cooperation on technology development and transfer. For this reason, it has been developed mainly by some advanced countries and industrial network formed among them. However, Korean space industry is being promoted by the successful launch of 'KSLV-I' and 'CAS 500-I' and the space launch vehicle "KSLV-II" under development. Recently, Korea and U.S. agreed to end bilateral missile guidelines. Therefore it is expected that development of Korean space industry will be accelerated due to the disappearance of the constraining factors for the development of space launch vehicles. Accordingly, this study examined the development and formation of the Korean space industry through the framework of network analysis. Based on this, the effect of structural position in the industrial network as a resource on the performance of Korean space companies was examined. Panel analysis was applied. Through this study, ideas for fostering the domestic space industry and implications for national policy related with building an industrial ecosystem are derived. It contributes to the development of space industry in Korea.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.12-20
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• 2008

Recent wireless communication technologies are envisioned as an innovative alternative to solve transportation problems. On ad hoc networks, as a wireless communication technology, nodes can communicate data without any infrastructure. In particular, vehicular ad hoc networks (VANETs), a specific ad hoc network applied to vehicles, enable vehicles equipped with a communication device to form decentralized traffic information systems in which vehicles share traffic information they experienced. This study investigated traffic information dissemination in a VANET-based traffic information system. For this study, an integrated transportation and communications simulation framework was developed, and experiments were conducted with real highway networks and traffic demands. The results showed that it took 3 minutes in the low traffic density situations (10 vehicle/lane.km) and 43 seconds in the high traffic density condition (40 vehicle/lane.km) to deliver traffic information of 5km away with 10% market penetration rate. In uncongested traffic conditions, information seems to be disseminated via equipped vehicles in the opposite direction. In congested traffic conditions, the sufficient availability of equipped vehicles traveling in the same direction reduces the chance to use vehicles in the opposing direction even though it is still possible.