• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.69-82
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• 2002

The objective of this study is to evaluate the performance of macroscopic traffic flow models with the analytical and field data. Five candidate models were selected as follows ; Lax Method Model, Upwind Scheme Model, Hilliges'Model, Papageorgiou's Model, and Cell-Transmission Model. In the analytical test scenario, the traffic condition was assumed that could cause the building and dissipation of queue, and each model was compared with analytical solutions and the numerical results. An analytical test indicated that both simple continuum and high order continuum models are able to reproduce queue building and dissipating behavior in a reasonable way A field test has shown that Upwind and Papageorgiou's model show similar performances. Considering the simplicity in model formulation and numerical computation, we firstly recommend Upwind scheme model , and secondly Papageorgiou's model that performed will to represent traffic flow in tests as candidate models for further development of simulation model for Naebu expressway in Seoul.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.5
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• pp.125-135
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• 2004

The validity of simulation has been well-established for decades in areas such as computer and communication system. Recently, the technique has become entrenched in specific areas such as transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and their driver's characteristics, even though it is known that they are important factors for any traffic flow analysis, have never been considered sufficiently. In this paper, the traffic simulation using a multi-agent approach with considering vehicle dynamics is proposed. The multi-agent system is constructed with the traffic environment and the agents of vehicle and driver. The traffic environment consists of multi-lane roads, nodes, virtual lanes, and signals. To ensure the fast calculation, the agents are performed on the based of the rules to regulate their behaviors. The communication frameworks are proposed for the agents to share the information of vehicles' velocity and position. The model of a driver agent which controls a vehicle agent is described in the companion paper. The vehicle model contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation has proceeded for an interrupted and uninterrupted flow model. The result has shown that the driver agent performs human-like behavior ranging from slow and careful to fast and aggressive driving behavior, and that the change of the traffic state is closely related with the distance and the signal delay between intersections. The system developed shows the effectiveness and the practical usefulness of the traffic simulation.

• Journal of Korean Society of Transportation
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• v.19 no.3
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• pp.115-131
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• 2001

The Purpose of this study is to develop a simulation model for large-scale network with interrupted flow as well as uninterrupted flow. The Cell Transmission(CT) theory is used to simulate traffic flow. Flow transition rules have been newly developed to simulate traffic flows at merging and diverging sections, and signalized intersections. In the model, it is assumed that dynamic OD table is exogenously given. Simulation results for toy network shows that the model can explain queue dynamics not only in signalized intersections of urban arterials, but also in merging and diverging sections of freeway. In case study, the model successfully simulated traffic flows of 145,000 vehicles on CBD network of city of Seoul with 74 traffic zones, 133 signalized intersections among 395 nodes and 1110 links.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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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 Highway Engineering
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• v.15 no.4
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• pp.155-165
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• 2013

PURPOSES : Benefits for improvement of travel time reliability obtained from construction of new highways should be considered as a major factor in the feasibility study for highway constructions. The purpose of this study is to develop a method of estimation for the value of travel time reliability. METHODS : Highway type (urban/rural highway) and traffic flow type(interrupted/uninterrupted) was considered to estimate he value of travel time reliability. And Double-bounded Dichotomous Choice among Contingent Valuation Method(CVM) was applied to survey the willingness-to-pay of drivers when travel time reliability is improved. Finally the value of travel time reliability was estimated using the results of survey and logit model. The value of travel time reliability was estimated considering travel objectives, time constraint travel and non-time constraint travel. RESULTS: The value of travel time reliability of business trip is higher than that of non-business trip. The value of travel time reliability of time constraint travel is higher than that of non-time constraint travel. The value of travel time reliability in urban area is higher than that in rural area. CONCLUSIONS: It was concluded that the proposed method in this study is more realistic and proper to estimate the value of travel time reliability because it reflects the situations of time constraint travel and non-time constraint travel.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.107-116
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• 2005

The information on travel time in providing the information of traffic to drivers is one of the most important data to control a traffic congestion efficiently. Especially, this information is the major element of route choice of drivers, and based on the premise that it has the high degree of confidence in real situation. This study developed a vehicle arrival time prediction algorithm called as "VAT-DV" for 6 corridors in total 6.1Km of "Nam-san area trffic information system" in order to give an information of congestion to drivers using VMS, ARS, and WEB. The spatial scope of this study is 2.5km~3km sections of each corridor, but there are various situations of traffic flow in a short period because they have signalized intersections in a departure point and an arrival point of each corridor, so they have almost characteristics of interrupted and uninterrupted traffic flow. The algorithm uses the information on a demand volume and a queue length. The demand volume is estimated from density of each points based on the Greenburg model, and the queue length is from the density and speed of each point. In order to settle the variation of the unit time, the result of this algorithm is strategically regulated by importing the AVI(Automatic Vehicle Identification), one of the number plate matching methods. In this study, the AVI travel time information is composed by Hybrid Model in order to use it as the basic parameter to make one travel time in a day using ILD to classify the characteristics of the traffic flow along the queue length. According to the result of this study, in congestion situation, this algorithm has about more than 84% degree of accuracy. Specially, the result of providing the information of "Nam-san area traffic information system" shows that 72.6% of drivers are available.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.147-158
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• 2004

A class of SWP(Stochastic Wane Propagation) models microscopically mimics individual vehicles' stochastic behavior and traffic jam propagation with simplified car-following models based on CA(Cellular Automata) theory and macroscopically captures dynamic traffic flow relationships based on statistical physics. SWP model, a program-oriented model using both discrete time-space and integer data structure, can simulate a huge road network with high-speed computing time. However, the model has shortcomings to both the capturing of low speed within a jam microscopically and that of the density and back propagation speed of traffic congestion macroscopically because of the generation of spontaneous jam through unrealistic collision avoidance. In this paper, two additional rules are integrated into the NaSch model. The one is SMR(Stopping Maneuver Rule) to mimic vehicles' stopping process more realistically in the tail of traffic jams. the other is LAR(Low Acceleration Rule) for the explanation of low speed characteristics within traffic jams. Therefore, the CA car-following model with the two rules prevents the lockup condition within a heavily traffic density capturing both the stopping maneuver behavior in the tail of traffic jam and the low acceleration behavior within jam microscopically, and generates more various macroscopic traffic flow mechanism than NaSch model's with the explanation of propagation speed and density of traffic jam.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.199-206
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• 2009

In relation with economical efficiency analysis on investment evaluation of transportation system, among vehicle operating cost saving benefit that is applied to general preliminary assessment guidelines and investment evaluation guidelines, oil expense calculated data which concentrated and analyze on the relationship between oil consumption amount on running state and running speed. For uninterrupted flow which does not have stopped delay due to traffic signal, consideration for reduction benefit is possible due to the changes of running speed and travel time however, for interrupted flow which the stopping occurs due to signal control on actual signal intersection has no consideration for stopping delay time reduction and stopping rate improvement thus reflection of reality on improved effect analysis is difficult. Therefore, this research makes a framework to analyze benefits that reflects the features of signalized intersections by benefits associated with decrease of stopping delay time with existing research and developing vehicle operating cost calculation model formula. Vehicle operating cost has been redefined considering the stopping delay time by applying the oil consumption amount at idling and the economical benefit between conventional model and newly developed model when applied for the optimization of traffic signal system on the two roads in Seosan city has been analyzed comparative. While the importance of traffic system maintenance is being emphasized due to the increase of congested areas on roads, it is expected to assist in more realistic economical analysis which reflect the delay improvement through the presentation of an economic analysis model that considers the features of signalized intersections in signal optimization system improvements and effect analysis of congestion improvement projects`.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.1-11
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• 2014

Traffic congestion cost is more likely to occur in the inner city than interregional road, and it accounts for about 63.39% of the whole. Therefore, it is important to mitigate traffic congestion of the inner city. Traffic congestion in the urban could be divided into Recurrent congestion and Non-recurrent congestion. Quick and accurate detection of Non-recurrent congestion is also important in order to relieve traffic congestion. The existing studies about incident detection have been variously conducted, however it was limited to Uninterrupted Traffic Flow Facilities such as freeway. Moreover study of incident detection on the interrupted Traffic Flow Facilities is still inadequate due to complex geometric structure such as traffic signals and intersections. Therefore, in this study, incident detection model was constructed using by Artificial Neural Network to aim at urban arterial road that is interrupted traffic flow facility. In the result of the reliability assessment, the detection rate were 46.15% and false alarm rate were 25.00%. These results have a meaning as a result of the initial study aimed at interrupted traffic flow. Furthermore, it demonstrates the possibility that Non-recurrent congestion can be detected by using car navigation data such as car navigator system device.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.145-157
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• 2004

Microscopic freeway simulation models project the uncertain values of performance measures for subject traffic conditions by explaining drivers' driving behavior with lane changing and car-following models. However, the existing lane changing models are limited to gap acceptance oriented passive behavior of drivers and not able to capture more-or-less aggressive driving behavior(e.g. cut-in lane changing) ordinarily obseved in field. This paper suggests the definition of cut-inlane changing and presents its characteristics based on the findings from two different freeway on- and off-ramp sections. In addition, this paper proposes a new lane changing model capable of handling both passive and active drivers' driving behavior for better performance of simulations. The proposed lane changing model was tested with Hanyang Simulatin (HYTSIM), a microscopic freeway simulation program developed for this study. The HYTSIM simulation results reflecting the performance of the proposed lane changing model were compared against the field data. The test results showed that the distribution of gaps collected when vehicles change lanes were statistically identical to the field data at 95% confidence level.