• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.83-90
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• 2015

Almost of current Automatic Incident Detection(AID) algorithms involve the vulnerability that detects the traffic accident in open road or in tunnel as the traffic jam not as the traffic accident. This paper proposes the improved accident detection algorithm to enhance the detection probability based on accident detection algorithms applied in open roads. The improved accident detection algorithm provides the preliminary judgment of potential accident by detecting the stopped object by Gaussian Mixture Model. Afterwards, it measures the detection area is divided into blocks so that the occupancy rate can be determined for each block. All experimental results of applying the new algorithm on a real incident was detected image without error.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.10-18
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• 2012

This paper develops a decentralized advanced traveler information system (ATIS) under the connected vehicle environment, recently regarded as one of most promising tools in Intelligent Transportation Systems (ITS). The performance of the proposed ATIS is reinforced by introducing autonomous automatic incident detection (AAID) function. The proposed ATIS is implemented and tested using an off-the-shelf microscopic simulation model (VISSIM) on a simple traffic network under idealized communication conditions. A key attribute of this experiment is the inclusion of a non-recurrent traffic state (i.e., traffic incident). Simulation results indicate that the ATIS using V2V communication is efficient in saving drivers' travel time and AAID plays an important role in improving the effectiveness of the system.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.121-138
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• 2011

More affordable and available cutting-edge technologies (e.g., wireless vehicle communication) are regarded as a possible alternative to the fixed infrastructure-based traffic information system requiring the expensive infrastructure investments and mostly implemented in the uninterrupted freeway network with limited spatial system expansion. This paper develops an advanced decentralized traveler information System (ATIS) using vehicle-to-vehicle (V2V) communication system whose performance (drivers' travel time savings) are enhanced by three complementary functions (autonomous automatic incident detection algorithm, reliable sample size function, and driver behavior model) and evaluates it in the typical $6{\times}6$ urban grid network with non-recurrent traffic state (traffic incident) with the varying key parameters (traffic flow, communication radio range, and penetration ratio), employing the off-the-shelf microscopic simulation model (VISSIM) under the ideal vehicle communication environment. Simulation outputs indicate that as the three key parameters are increased more participating vehicles are involved for traffic data propagation in the less communication groups at the faster data dissemination speed. Also, participating vehicles saved their travel time by dynamically updating the up-to-date traffic states and searching for the new route. Focusing on the travel time difference of (instant) re-routing vehicles, lower traffic flow cases saved more time than higher traffic flow ones. This is because a relatively small number of vehicles in 300vph case re-route during the most system-efficient time period (the early time of the traffic incident) but more vehicles in 514vph case re-route during less system-efficient time period, even after the incident is resolved. Also, normally re-routings on the network-entering links saved more travel time than any other places inside the network except the case where the direct effect of traffic incident triggers vehicle re-routings during the effective incident time period and the location and direction of the incident link determines the spatial distribution of re-routing vehicles.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.61-66
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• 2013

The central core of the Traffic Management System is an Incident Management System. Whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first action taken during the development process was the selection of the required data for each components within the existing infrastructure of Algeria freeway system. After review and analysis of existing incident detection methodologies, Paramics was utilized to test the performance of APID, DELOS algorithms. The existing system of Algeria freeway was tested in a different configuration at different sections of freeway, thereby increasing the validity and scope of the overall findings. The incident detection module has been performed according to predefined system validation specifications. The Paramics simulation was done with the use of synchronous analysis, thereby providing a means for testing the incident detection module.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.175-196
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• 2004

This study focuses on improving the performance of freeway incident detection by introducing some new measures to reduce false alarms in developing a new incident detection model. The model consists of the 5 major components through which a series of decision makings in determining the given traffic flow condition are made. The decision making process was designed such that the causes of traffic congestions can be accurately classified into several types including incidents and bottlenecks according to their unique characteristics. The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of the detection rate and detection time. It should noted that the model produced much less false alarms than most of the existing models. The study results prove that the initial objective of the study was satisfied as it was an experimental trial to improve the false alarm rate for the incident detection model to be more pactically usable for traffic management purposes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.927-933
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• 2006

The Variable Message Signs (VMS) are useful way to reduce the socio-economic costs due to the traffic congestions and delays by providing the information on traffic condition to drivers. This study provided a methodology to determine the locations of VMS's in terms of the minimization of the delay by applying the genetic algorithm. The optimal number of VMS's was also derived by the economic analysis based on the cost and the benefit. The simulation considered the variation of traffic volume, the frequency and duration of the incident, and the traffic conversion in order to reflect the real situation. I've made a scenario to consider traffic volume and incident, and it can undergo through changing different traffic volume and incident in time and days and seasons. And I've comprised two kinds of result, one is based on empirical studies, the other is based on Genetic Algorithm about optimal allocation VMS. This result of using optimal location VMS, reduce total travel time rather than preceding study based on normal location VMS and we can estimate optimal location VMS each one.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.17-30
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• 2002

Incident duration prediction is one of the most important steps of the overall incident management process. An accurate and reliable estimate of the incident duration can be the main difference between an effective incident management operation and an unacceptable one since, without the knowledge of such time durations, traffic impact can not be estimated or calculated. This research presents several multiple linear regression models for incident duration prediction using data consisting of 384 incident cases. The main source of various incident cases was the Traffic Incident Reports filled out by the Motorist Assistant Units of the Korea Highway Corporation. The models were proposed separately according to the time of day(daytime vs. nighttime) and the fatality/injury incurred (fatality/injury vs. property damage only). Two models using an integrated dataset, one with an intercept and the other without it, were also calibrated and proposed for the generality of model application. Some findings are as follows ; ?Variables such as vehicle turnover, load spills, the number of heavy vehicles involved and the number of blocked lanes were found to significantly affect incident duration times. ?Models, however, tend to overestimate the duration times when a dummy variable, load spill, is used. It was simply because several of load spill incidents had excessively long clearance times. The precision was improved when load spills were further categorized into "small spills" and "large spills" based on the size of vehicles involved. ?Variables such as the number of vehicles involved and the number of blocked lanes found not significant when a regression model was calibrated with an intercept. whereas excluding the intercept from the model structure signifies those variables in a statistical sense.

• Journal of the Korea Computer Industry Society
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• v.5 no.3
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• pp.337-344
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• 2004

ITS(Intelligent transportation system) provides users with realtime traffic information based on technologies such as advanced information ＆ telecommunication, electronic control and transportation engineering. To operate efficient ITS, it is necessary to quickly identify and take actions for incidents(accidents, broken vehicles, public functions, traffic control, etc.). However, there have been few reliable incident detection algorithms developed so far. The algorithm presented in this study greatly resolved the problems in the existing incident detection algorithms, which determine incidents according to the input of constant values, by defining ranges based on the concept of pseudo level of service. With this improvement, operators can determine the incident detection parameters more accurately.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.119-126
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• 2018

This study intended to exploratively testify human error causing factors for railway traffic controller, using AMOS structural equation model. Through literature survey, fatigue and stress as exogenous variable, errors in information process such as cognitive, memory, storage, and execution error as endogenous variable, and accident and incident(near-miss) as dependent variable were set up. Results based on AMOS using 201 railway traffic controllers' questionnaire showed that a clear causality loop like as 'stress ${\rightarrow}$ memory error ${\rightarrow}$ storage error ${\rightarrow}$ incident(near-miss) ${\rightarrow}$ accident' is formed. This result suggests that for the purpose of mitigation of traffic controller's accident, it is so necessary to reduce memory and execution error in the information processing process based on the effective management of stress, as the precedent of them.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.101-110
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• 2010

In this study, a prediction model for incident reaction time was developed so that we can cope with the increasing demand for information related to the accident reaction time. For this, the time for dealing with accidents and dependent variables were classified into incident grade, A, B, and C. Then, fifteen independent variables including traffic volume, number of accident-related vehicles and the accidents time zone were utilized. As a result, traffic volume, possibility of including heavy vehicles, and an accident time zone were found as important variables. The results showed that the model has some degree of explanatory power. In addition, when the CHAID Technique was applied, the Answer Tree was constructed based on the variables included in the prediction model for incident reaction time. Using the developed Answer Tree model, accidents firstly were classified into grades A, B, and C. In the secondary classification, they were grouped according to the traffic volume. This study is expected to make a contribution to provide expressway users with quicker and more effective traffic information through the prediction model for incident reaction time and the Answer Tree, when incidents happen on expressway