• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.711-718
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• 2013

Traffic pavement markings are one of primary transportation facilities that provide drivers with various road information directly. Thus, a clear understanding of traffic pavement markings is utmost important to improve traffic safety as well as to establish a proper traffic culture. However, no past studies examined drivers' understanding of traffic pavement markings in Korea. Hence, this study investigated drivers' understanding of traffic pavement markings through an elaborated administrated survey, and analyzed the relationship between various drivers' characteristics and understanding regarding pavement marking via cross-classification table and logistic model. The analysis results show that drivers have limited understanding regarding the purpose of the markings. Specifically, the average understanding of pavement markings is 57.41%: the lowest understanding is 25.88% for yield pavement marking, and the highest understanding is 91.18% for advanced pedestrian crosswalk pavement marking. This study also revealed that the understanding of some pavement markings are somewhat influenced by user group such as drivers with suspended or revoked driver licenses, but the overall understanding of pavement markings are not significantly affected by drivers' characteristics such as gender and driving experiences at ${\alpha}$=0.05. Thus, it might be desirable for policy makers to establish pavement marking-related policies for overall drivers rather than specific drivers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.1-19
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• 2023

Bikes have recently emerged as an alternative to carbon neutrality. To understand the demand for public bikes, we endeavored to estimate travel frequency of public bike by considering the intermediate stops. Using the GPS trajectory data of 'Ttareungyi', a public bike service in Seoul, we identified a stay point and estimated travel frequency reflecting population, land use, and physical characteristics. Application of map matching and a stay point detection algorithm revealed that stay point appeared in about 12.1% of the total trips. Compared to a trip without stay point, the trip with stay point has a longer average travel distance and travel time and a higher occurrence rate during off-peak hours. According to visualization analysis, the stay points are mainly found in parks, leisure facilities, and business facilities. To consider the stay point, the unit of analysis was set as a hexagonal grid rather than the existing rental station base. Travel frequency considering the stay point were analyzed using the Zero-Inflated Negative Binomial (ZINB) model. Results of our analysis revealed that the travel frequency were higher in bike infrastructure where the safety of bike users was secured, such as 'Bikepath' and 'Bike and pedestrian path'. Also, public bikes play a role as first & last mile means of access to public transportation. The measure of travel frequency was also observed to increase in life and employment centers. Considering the results of this analysis, securing safety facilities and space for users should be given priority when planning any additional expansion of bike infrastructure. Moreover, there is a necessity to establish a plan to supply bike infrastructure facilities linked to public transportation, especially the subway.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.375-382
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• 2020

As the 4th Industrial Revolution era in worldwide, interest in autonomous vehicles is increasing. but due to recent safety issues such as pedestrian accidents and car accidents, as a technical model for this, the demand for 3D HD maps (High Definition maps) is increasing in including lanes, road markings, road information, traffic lights and traffic signs etc. However, since some complementary points have been continuously raised according to demand, It is necessary to collect the opinions of institutions and companies utilizing HD maps and to improve HD maps. This study was conducted by utilizing the results of the contest for usability verification of HD Maps hosted by the National Geographic Information Institute and organized by the Spatial Information Industry Promotion Institute. For this study, we researched HD maps' layers and codes for HD maps object usability to improve HD maps, constructed HD maps object usability items accordingly, and contested usability verification of HD maps according to the items The contestants conducted verification and analyzed the results. As a result, the most frequently used code for each layer was the flat intersection, and the code showing the highest usage rate was a safety sign. In addition, the use rate of the sub-section and height obstacles was 16.67% and 8.88%, respectively, showing a low ratio. In order to utilize HD maps in the future, this study is expected to require research to continuously collect opinions from customers and improve data objects and data models that are actually needed by customers.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.