• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권11호
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• pp.1891-1908
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• 2011

Cyber-physical systems (CPSs) are an emerging discipline that involves engineered computing and communicating systems interfacing the physical world. The widespread applications of CPSs still face enormous challenges because of the lack of theoretical foundations. In this technical survey, we review state-of-the-art design techniques from various angles. The aim of this work is to provide a better understanding of this emerging multidisciplinary methodology. The features of CPSs are described, and the research progress is analyzed using the following aspects: energy management, network security, data transmission and management, model-based design, control technique, and system resource allocation. We focus on CPS resource optimization, and propose a system performance optimization model with resource constraints. In addition, some classic applications (e.g., integrating intelligent road with unmanned vehicle) are provided to show that the prospects of CPSs are promising. Furthermore, research challenges and suggestions for future work are outlined in brief.

• ETRI Journal
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• 제44권4호
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• pp.641-653
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• 2022

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane-change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane-change trajectories for autonomous vehicles. When comparing this generated trajectory with a man-generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane-change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane-change trajectory.

• 한국터널공학회:학술대회논문집
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• 한국터널공학회 2005년도 학술발표회 논문집
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• pp.348-358
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• 2005

Although the evaluation of the mechanical properties and behavior of discontinuous rock masses is very important for the design of underground openings, it has always been considered the most difficult problem. One of the difficulties in describing the rock mass behavior is assigning the appropriate constitutive model. This limitation may be overcome with the progress in discrete element software such as PFC, which does not need the user to prescribe a constitutive model for rock mass. Instead, the micro-scale properties of the intact rock and joints are defined and the macro-scale response results from those properties and the geometry of the problem. In this paper, a $30m{\times}30m{\times}30m$ jointed rock mass of road tunnel site was analyzed. A discrete fracture network was developed from the joint geometry obtained from core logging and surface survey. Using the discontinuities geometry from the DFN model, PFC simulations were carried out, starting with the intact rock and systematically adding the joints and the stress-strain response was recorded for each case. With the stress-strain response curves, the mechanical properties of discontinuous rock masses were determined and compared to the results of empirical methods such as RMR, Q and GSI. The values of Young's modulus, Poisson's ratio and peak strength are almost similar from PFC model and Empirical methods. As expected, the presence of joints had a pronounced effect on mechanical properties of the rock mass. More importantly, the mechanical response of the PFC model was not determined by a user specified constitutive model.

• International Journal of Highway Engineering
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• 제11권3호
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• pp.129-139
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• 2009

The transportation network is a critical infrastructure in the event of natural and human caused disasters such as rainfall, snowfall, and terror and so on. Particularly, the transportation network in an urban area where a large number of population live is subject to be negatively affected from such events. Therefore, efficient traffic operation plans are required to assist rapid evacuation and effective detour of vehicles on the network as soon as possible. Recently, ubiquitous communication and sensor network technology is very useful to improve data collection and connection related emergency information. In this study, we develop a specific algorithm to provide evacuation route and detour information only for vehicles under emergency situations. Our algorithm is based on shortest path search technique and dynamic traffic assignment. We perform the case study to evaluate model performance applying hypothetical scenarios involved terror. Results show that the model successfully describe effective path for each vehicle under emergency situation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제17권4호
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• pp.54-62
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• 2018

As a part of realization of artificial intelligence signal(AI Signal), this study proposed an actuated signal algorithm based on vehicle queue length that estimates in real time by deep learning. In order to implement the algorithm, we built an API(COM Interface) to control the micro traffic simulator Vissim in the tensorflow that implements the deep learning model. In Vissim, when the link travel time and the traffic volume collected by signal cycle are transferred to the tensorflow, the vehicle queue length is estimated by the deep learning model. The signal time is calculated based on the vehicle queue length, and the simulation is performed by adjusting the signaling inside Vissim. The algorithm developed in this study is analyzed that the vehicle delay is reduced by about 5% compared to the current TOD mode. It is applied to only one intersection in the network and its effect is limited. Future study is proposed to expand the space such as corridor control or network control using this algorithm.

• Spatial Information Research
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• 제21권5호
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• pp.33-41
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• 2013

Recently, the number of disasters has been seriously increasing. The total damages by the natural or man-made disasters during the past years resulted in tremendous fatalities and recovery costs. It is necessary to have efficient emergency evacuation management which is concerned with identifying evacuation route, and the estimation of evacuation and clearance times. An emergency evacuation model is important in identifying critical locations, and developing various evacuation strategies. In that existing evacuation models have focused on route analysis for indoor evacuation, there are only a few models for areawide emergency evacuation analysis. Therefore, we developed a mesoscopic model by using Cube Avenue and performed evacuation simulation, targeting road network in City of Fargo, North Dakota. Consequently, a mesoscopic model developed in this study is used to carry out dynamic analysis using network and input variable of existing travel demand model. The results of this study show that the model is an appropriate tool for areawide emergency evacuation analysis to save time and cost. Henceforth, the results of this study can be applied to develop a disaster evacuation model which can be used for a variety of disaster simulation and evaluation based on scenarios in the local metropolitan area.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제20권1호
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• pp.86-99
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• 2021

One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제13권4호
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• pp.57-62
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• 2014

Recently, research activities to develop services for providing safety information to the drivers in fast moving vehicles based on various wireless network technologies such as DSRC (Dedicated Short Range Communication), IEEE 802.11p WAVE (Wireless Access for Vehicular Environment) are widely being carried out. This paper presents a proof-of-concept model based on a robot-car for Dilemma Zone Decision Assistant Service using the wireless LAN technology. The proposed model system consists of a robot-car based on an embedded Linux OS equipped with a WiFi interface and an on-board unit emulator, an Android-based remote controller to model a human driver interface, a laptop computer to run a model traffic signal controller and signal lights, and a WiFi access point to model a road-side unit.

• Journal of Korean Society of Transportation
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• 제17권1호
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• pp.173-185
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• 1999

The purpose of this study is to develope a forecasting model to implement short-term Congestion Management Program (CMP) based on TDM strategies in Seoul. The CMP is composed of three elements: 1) setting a goal of short-term traffic management. 2) developing a model to forecast the impacts of TDM alternatives, and 3) finding TDM measures to achieve the goal To Predict the impacts of TDM alternatives, a model called SECOMM (SEoul COngestion Management Model) is developed. The model assumes that trip generation and distribution are not changing in a short term, and that only mode split and traffic assignment are affected by TDM. The model includes the parameter values calibrated by a discrete mode choice model, and roadway and transit networks with 1,020 zones. As a TDM measure implement, it affects mode choice behavior first and then the speeds of roadway network. The chanced speed again affects the mode choice behavior and the roadway speeds. These steps continue until the network is equilibrated. The study recommends that CMP be introduced in Seoul, and that road way conditions be monitored regularly to secure the prediction accuracy of SECOMM. Also, TDM should be the major Policy tools in removing short-term congestion problems in a big city.