Earticle

국내 교통은 날로 증가하는 차량으로 인해 도로의 상습정체, 대기오염 발생 등의 다양한 교통문제가 발생되고 있다. 이러한 문제의 해결을 위해 지자체는 지능형교통체계(ITS : Intelligent Transport System), 첨단교통관리시스템(ATMS : Advanced Traffic Management Systems) 등의 시스템 구축을 통해 교통관리를 시행하고자 했으나 인프라 중심의 교통시스템 구축만으로 는 만성적인 교통문제 해결에 효과가 미비하여 기존 시설물에 운영관리 기능을 강화한 시스템 고도화가 필요한 시점이다. 도 시부 내 교통류는 임의의 시간대별로 특성 차량군이 형성되어 다양한 교통패턴이 존재하며, 이러한 상황별 교통패턴을 처리할 수 있는 지자체 네트워크 차원의 상황별 신호운영 설계가 필요하다고 판단된다. 따라서, 본 연구에서는 기존의 획일적인 신호 운영의 문제점을 개선하기 위해 단기적 교통상황 예측 데이터의 교통패턴을 기반으로 Frame Signal을 설정한 뒤 네트워크 차 원의 신호최적화를 통한 상황별 신호제어 운영방안을 목적으로 연구를 진행하고자 한다.

An increasing number of vehicles is causing various traffic problems such as chronic congestion of highways and air pollution. Local governments have been managing traffic by constructing systems such as Intelligent Transport Systems (ITS) and Advanced Traffic Management Systems (ATMS) to relieve such problems, but construction of an infrastructure-based traffic system is insufficient in resolving chronic traffic problems. A more sophisticated system with enhanced operational management capabilities added to the existing facilities is necessary at this point. As traffic patterns of the urban traffic flow is time-specific due to the different vehicle populations throughout the time of the day, a local network-wide signal operation plan that can manage such situation-specific traffic patterns is deemed to be necessary. Therefore, this study is conducted for the purpose of establishment of a plan for contextual signal control management through signal optimization at the network level after setting the Frame Signal in accordance to the traffic patterns gathered from the short-term traffic forecast data as a means to mitigate the problems with existing standardized signal operations.

The research on Urban traffic simulation is more widely, But there are some problems, For example, the traffic system in the spatial scale is from microscopic to macroscopic highly integrated. In the time scale is from the second grade to the height of continuous integration. Simulation and objective traffic system complexity determines that any single scale are difficult to be better traffic phenomena. On this basis, the simulation system was proposed based on multi-scale fusion, from the macro, meso, micro in three dimensions using the corresponding algorithm, and carries on the design to the system. Finally, Based on multi-scale fusion of urban traffic network optimization simulation system has carried on the experiment. Through the example of Nanjing Fujian road and Traffic Signal Priority of the three arches to analyze network simulation experiment. The experiment proves that the system is practical and reliable.

Network traffic prediction is one of the significant issues. The model for network traffic prediction should meet the following requirements. First, the model should be taken into consideration the characteristics of the network flow such as burstiness, long-range dependence, periodicity and self-similarity. To achieve this, we decompose the original flow in a multi-scale manner into a set of linear and stable representations, and introduce chaos theory to improve the diversity and search coverage. Second, the model should be efficient and accurate. To this end, we propose a prediction model based on SVR, and utilize Ant Colony Optimization (ACO) algorithm for parameter selection of SVR. Besides, we conduct experiments to evaluate the proposed model.

Transforming the convolution to the (+,-) in legend domain is proposed in this paper. Based on the transformation, the Legend Transformation of service curve and arrive curve is given in the case of independent cross traffic, and the closed expression of stochastic delay and stochastic backlog is obtained. In the paper, we analyze influence of the dynamic of traffic and network to QoS parameter. Based on the analysis result, the QoS analysis in Legend domain is given, and the upper bound of delay and backlog in Legend domain. For the case of non-independent cross traffic, the relationship expression of different traffic is got. And simulation proves that the route optimization algorithm is correct.

Now-a-days the transportation network traffic plays a vital role in the society. People are focusing on arriving at our destination as quickly as possible. With this lifestyle, people are not always aware of all the traffic conditions that are experienced while operating an automobile. In the existing system, reduction of green house gas emissions from transportation network based on road network graphs where all edges are annotated with accurate weight that capture environmental cost, fuel usage are needed for eco-routing. However, such weights are not readily available on the road network. So, randomly assign the weights for each road segments and it typically lack the speed pattern of the road network. This paper address these limitation, proposes two stage routing algorithm and weight propagation model to predict the cost of an arbitrary path on road network and accurately detects the traffic environment and also provides the optimal alternate route for destination.

최근 네트워크에서 안정된 전송을 위해 광대역의 대역폭과 중복 링크를 사용함으로써 과도하게 에너지를 소비하고 전송효율을 떨어뜨리는 결과를 낳고 있다. 본 논문에서는 트래픽 중복이 허용되는 네트워크에서 중복 트래픽을 제거함으로써 전송 링크의 수를 줄이고 전송에너지를 최소화하는 최적화 알고리즘을 제안한다. 본 논문에서 제안하는 최적화 알고리즘은 타부서치 알고리즘을 이용한 메타휴리스틱방식을 사용한다. 제안된 최적화 알고리즘은 중복되는 트래픽을 효율적으로 경로 설정할 수 있도록 이웃해 생성방식을 설계하여 전송에너지를 최소화한다. 제안된 최적화 알고리즘의 성능평가는 네트워크에서 발생하는 모든 트래픽을 전송하기 위해 사용되는 링크의 수와 소모되는 전송에너지 관점에서 수행되었다. 성능평가 결과에서 제안된 알고리즘이 이전에 제안된 다른 알고리즘에 비해 더 우수한 성능 결과가 나타남을 확인할 수 있었다.

In recent years, the use of broadband bandwidth and redundant links for stable transmission in networks has resulted in excessive energy consumption and reduced transmission efficiency. In this paper, we propose an optimization algorithm that reduces the number of transmission links and minimizes transmission energy by removing redundant traffic in networks where traffic redundancy is allowed. The optimization algorithm proposed in this paper uses the meta-heuristic method using Tabu search algorithm. The proposed optimization algorithm minimizes transmission energy by designing a neighborhood generation method that efficiently routes overlapping traffic. The performance evaluation of the proposed optimization algorithm was performed in terms of the number of links used to transmit all traffic generated in the network and the transmission energy consumed. From the performance evaluation results, it was confirmed that the proposed algorithm is superior to other algorithms previously proposed.