• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.141-156
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• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.130-145
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• 2021

As part of the Smart Seoul policy, the importance of using big urban data is being highlighted. Furthermore interest in the impact of transportation-related urban environmental factors such as PM10 and noise on citizen's quality of life is steadily increasing. This study established the integrated DB by matching IoT big data with transportation data, including traffic volume and speed in the microscopic Spatio-temporal scope. This data analyzed the impact of a spatial unit in the road-effect zone on environmental risk level. In addition, spatial units with similar characteristics of road traffic and environmental factors were clustered. The results of this study can provide the basis for systematically establishing environmental risk management of urban spatial units such as PM10 or PM2.5 hot-spot and noise hot-spot.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.705-713
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• 2024

Purpose: Currently, the only routes that enter Yeongjong Island are Yeongjong Bridge and Incheon Bridge, which are private roads. The purpose of this study is to predict and study changes in transportation demand for new routes and two existing routes according to the plan to open the 3rd Bridge, a new route, in December 2025. Method: The basic data for traffic demand forecast were O/D and NETWORK data from 2021.08, KOTI. In order to examine the reliable impact of Yeongjong Bridge and Incheon Bridge on the opening of the 3rd Bridge, it is necessary to correct the traffic distribution of Yeongjong Island and Incheon International Airport to suit reality, and in this study, the trip distribution by region was corrected and applied using Mobile Big Data. Result: As of 2026, the scheduled year of the opening of the 3rd Bridge, two alternatives, Alternative 1 (2,000 won) and Alternative 2 (4,000 won), were established and future transportation demand analysis was conducted, In the case of Alternative 1, which is similar to the existing private road toll restructuring, the traffic volume of the 3rd Bridge was predicted to be 42,836 out of 199,101 veh/day in the Yeongjong area in 2026, and the traffic volume reduction rate of the existing road was analyzed as 21.5%. Conlclusion: As a result of the review (based on Alternative 1), the proportion of convertted traffic on the 3rd Yanji Bridge was estimated to be 70% of Yeongjong Bridge and 30% of Incheon Bridge, and 21.5% of the predicted traffic reduction on the existing road when the 3rd Yanji Bridge was opened is considered appropriate considering the results of the case review and changes in conditions. It is judged that it is a way to secure the reliability of the prediction of traffic demand because communication big data is used to reflect more realistic traffic distribution when predicting future traffic demand.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.229-233
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• 2013

The technologies related with information communication regions are progressing continuously. These technologies in today are converged with different industries in rapidly. Because of these properties, A number of data are made in our life. Through many devices such as smart phone, camera, game machine, tablet pc, various data types are produced and the traffic is increased. We called it Big Data. There are many efforts to create new worth creation through Big Data utilization. Therefore, we described current trends and future of Big Data in this paper.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.1-12
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• 2017

Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.269-278
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• 2000

Today the pedestrian-related problems a key subject requiring the attention of the traffic engineers for improving the transportation system. Particularly in urban and CBD locations, the pedestrian presents an element of sharp conflict with vehicular traffic. Therefore pedestrian movements must be studied for the purpose of providing guideline for the design and operation of walking transportation systems. This paper is to address the characteristics of walking transportation in a big city. Especially the focuses are emphasized on the ratio occupied by pedestrian traffic among the whole unlinked trips in a city and walking time. The data for analysis are collected in Seoul metropolitan city through sampling 1,006 citizens. Compared with other similar research works this paper utilized diversified tools to acquire more useful results. Finally, policy directions for pedestrian environment improvement were suggested.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.173-179
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• 2010

Currently, the communication systems are progressing two ways as the wireless and multimedia and these need big transmission capacity then before. In these circumstance, communication services existed as two different service forms which have different rates and characteristics. For example Voice/Video Services accept some errors but transmit on realtime, but Date Services don't need to transmit on realtime but have to retransmit if these have only one bit error. In Voice/Date Integrated traffics, it has big throughput that realtime voice/video data which could have some errors if integrated traffic is increased rapidly have transmission priority, then Data traffics which delay is accepted is sent after that. In this paper, I introduce the calculation method for various traffic when voice/data mixed traffics is transmitted to asynchronous unslotted ALOHA CDMA system proposed and the result is presented. And We can easily theoretical analysis for the system traffic and changing traffic using proposed solution in this paper.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.