• Journal of Korean Society of Transportation
• /
• v.28 no.1
• /
• pp.63-74
• /
• 2010

Recently, there has been increasing demand from academic society in Korea for the improvement of current traffic congestion cost estimation methods. The purpose of this study is to suggest a better way to estimate congestion cost followed by in-depth review regarding traffic congestion. The key improvements proposed in this study include: 1) adding social externality to congestion cost, 2) integrating the green house and environmental pollution impacts with congestion costs, 3) taking non-recurrent traffic congestion costs into account for the assessment, 4) revising the criteria to determining the level of traffic congestion speed, and 5) deciding how to limit congestion measurement period. It is found meaningful that the improvements, notwithstanding difficulties in their real case application, provide invaluable insights in our efforts to change the meaning of congestion cost in an era of sustainable growth.

• Asia pacific journal of information systems
• /
• v.26 no.3
• /
• pp.427-448
• /
• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• International Journal of Highway Engineering
• /
• v.18 no.1
• /
• pp.99-107
• /
• 2016

PURPOSES : This study was initiated to estimate expressway traffic congestion costs by using Vehicle Detection System (VDS) data. METHODS : The overall methodology for estimating expressway traffic congestion costs is based on the methodology used in a study conducted by a study team from the Korea Transport Institute (KOTI). However, this study uses VDS data, including conzone speeds and volumes, instead of the volume delay function for estimating travel times. RESULTS : The expressway traffic congestion costs estimated in this study are generally lower than those observed in KOTI's method. The expressway lines that ranked highest for traffic congestion costs are the Seoul Ring Expressway, Gyeongbu Expressway, and the Youngdong Expressway. Those lines account for 64.54% of the entire expressway traffic congestion costs. In addition, this study estimates the daily traffic congestion costs. The traffic congestion cost on Saturdays is the highest. CONCLUSIONS : This study can be thought of as a new trial to estimate expressway traffic congestion costs by using actual traffic data collected from an entire expressway system in order to overcome the limitations of associated studies. In the future, the methodology for estimating traffic congestion cost is expected to be improved by utilizing associated big-data gathered from other ITS facilities and car navigation systems.

• Journal of Korean Society of Transportation
• /
• v.23 no.7 s.85
• /
• pp.77-86
• /
• 2005

Level of service(LOS) is a quantify measure describing operational conditions within a traffic stream, generally, in terms of such service measures as speed, travel time, freedom to measures, traffic interruptions, comfort and convenience. The LOS is leveled by highway facilities according to measure of effectiveness(MOE) and then used to evaluate performance capacity. The current evaluation of a urban road is performed by only a aspect of traffic operation without any concepts of safety. Therefore, this paper presents a method for evaluation of risk order for urban road with new MOE, user cost analysis, considering both smooth traffic operation(congestion) and traffic safety(accident). The user coat is included traffic accident cast by traffic safety and traffic congestion cost by traffic operation. First of all, a number of traffic accident and accident rate by highway geometric is inferred from urban road traffic accident prediction model (Poul Greibe(2001)) Secondly, a user cost is inferred as traffic accident cast and traffic congestion cost is putting together. Thirdly, a method for evaluation of a urban road is inferred by user cost analysis. Fourthly a accident rate by segment predict with traffic accidents and data related to the accidents in $1996{\sim}1998$ on 11 urban road segments, Gwang-Ju, predicted accident rate. Traffic accident cost predict using predicted accident rate, and, traffic congestion cost predict using predicted average traffic speed(KHCM). Fifthly, a risk order are presented by predicted user cost at each segment in urban roads. Finally, it si compared and evaluated that LOS of 11 urban road segments, Gwang-Ju, by only a aspect of traffic operation without any concepts of safety and risk order by a method for evaluation of urban road in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2483-2503
• /
• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.6
• /
• pp.67-78
• /
• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Journal of Korean Society of Transportation
• /
• v.20 no.2
• /
• pp.27-38
• /
• 2002

This study estimates the social cost of the traffic congestion of the Express highway from Seoul to Pusan during the New Year holiday in 1999. Considering inelastic demand and the hyper congestion, we show the congestion cost can be estimated as externalities caused by traffics which exceed the road capacity. Due to the congestion, it is estimated that it took about 12.40 hours more from Seoul to Pusan. The congestion is also estimated to have caused fuel cost of 0.6 billion won. time cost of 43.6 billion won and environmental cost of 0.5 billion won. The total cost reached up to 44.8 billion won.

• International Journal of Highway Engineering
• /
• v.19 no.5
• /
• pp.153-162
• /
• 2017

PURPOSES : This study was conducted to analyze the effects arising from extending the duration of nighttime road construction on improving road quality and durability. METHODS : Most previous studies estimating the social cost of various construction conditions did not consider road pavement cooling time as a factor in improving road pavement quality. This study investigated the feasibility of achieving higher road quality and durability by extending the duration of nighttime road construction time extension. For this investigation, the effects of such an extension on traffic conditions were analyzed based on micro-simulation studies and scenario-based cost-benefit analyses, using factors including traffic volume, delay, construction cost, and road pavement cooling time. RESULTS : The results of the traffic simulation studies and cost-benefit analyses indicate that the current road construction method that emphasizes completing nighttime road construction by 6 a.m. reduces pavement life while causing relatively little traffic delay. If the night construction time is instead extended to 2 p.m., road pavement lifetime is increased, reducing road re-construction cost. These savings are greater than the cost of congestion arising from extending the duration of nighttime construction. CONCLUSIONS : The current nighttime construction durations need to be extended in order to efficiently manage roads and reduce road management costs.

• International Journal of Highway Engineering
• /
• v.13 no.3
• /
• pp.147-156
• /
• 2011

Since the urbanization process has been taking place, negative outcomes such as environmental pollution and traffic congestion have produced as well. Reflecting the phenomenon, our study assumed that physical structure of urban form were implicit in relation to both economic performance and cost. It can be interpreted that as the urban space has been growing bigger, economic performances such as regional product output, economy of scale and the effect of agglomeration economies are increased. On the contrary, the negative effects such as environmental pollution and traffic congestion were incurred as economic loss and expenses. It means that even though economic performance can help increase regional product output, we should consider the loss on economic expenses which are paid for social problems such as environmental pollution and traffic congestion, which are caused by urbanization. Therefore, this study aims to statistically validate the relationship between traffic congestion as the most representative economy costs and physical characteristics of urban in a large city such as Seoul and to suggest its implications. As a result of model development for empirical analysis, GRDP(0.604), the population(0.582), employment GINI coefficients(0.296), population GINI coefficients(0.254) in order led to congestion cost. We can come to the conclusion that in case of scale factor such as the population, if the population tends to concentrate, urban becomes more crowded and that if GINI coefficients (the population, employment) which are variable on inequality according to region have the disparity with surrounding areas, congestion cost is caused a lot on account of movement related with employment. In addition, this phenomenon was caused if both the population and employment were geographically biased on one side.

• IE interfaces
• /
• v.23 no.4
• /
• pp.349-356
• /
• 2010

The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.