• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.58-71
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• 2021

The number of elderly people worldwide is rapidly increasing and the mobility handicapped suffering from inconvenient public transportation service is also increasing. In Korea and abroad, various policies are being implemented to provide high-quality transportation services for the mobility handicapped, and budget support and investment related to mobility facilities are being expanded. The mobility handicapped spends more time for transit transfer than normal users and their satisfaction with transit service is also lower. There exist transfer inconvenience points of the mobility handicapped due to various factors such as long transfer distances, absence of transportation facilities like elevators, escalators, etc. The purpose of this study is to find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. This study process traffic card transaction data and construct transfer travel data by user groups using smart card big data and analysis of the transfer characteristics for each user group ; normal, children, elderly, etc. Finally, find transfer inconveniences points by comparing transfer patterns between normal users and the mobility handicapped. This study is significant in that it can find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. In addition, it can be applicated of Smart card Big data for developing public transportation polices in the future. It is expected that the result of this study be used to improve the accessibility of transit transportation for mobility handicapped.

• Journal of Urban Science
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• v.11 no.2
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• pp.9-17
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• 2022

The purpose of this study is to develop an index for evaluating the function of a new motorway using the travel distance frequency distribution (TLFD) calculated using the vehicle travel route big data, and to overcome the limitations of the evaluation through the existing traffic volume. The mobility evaluation index of motorways was developed by applying it to the TLFD data table in 2019. The smaller the value of the mobility evaluation index of the link is calculated, the more it is a link with mainly short-distance travel, and the higher the value of the mobility evaluation index, the more it means a link with mainly long-distance travel. The accessibility evaluation index was calculated through the result of the mobility evaluation index of all motorways developed, and all motorways were grouped into three groups using K-means clustering. Group A was found to exist inside a large city and consisted of motorways with many short-distance traffic, Group B was investigated as acting as an arterial between groups, and Group C was classified as a motorway consisting mainly of long-distance traffic connecting large cities and large cities. This study is significant in developing a new motorway function evaluation index that can overcome the limitations of motorway function evaluation through the existing traffic volume. It is expected that this study can be a reasonable comprehensive indicator in the operation and planning process of motorways.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• Construction Engineering and Management
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• v.19 no.2
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• pp.63-68
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• 2018

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1061-1069
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• 2015

Recently, transportation big data generated in the transportation sector has been widely used in the transportation policies making and efficient system management. Bus passengers' mobility patterns are useful insight for transportation policy maker to optimize bus lines and time intervals in a city. We propose a new methodology to discover mobility patterns by using transportation card data. We first estimate the bus stations where the passengers get-off because the transportation card data don't have the get-off information in most cities. We then applies LDA (Latent Dirichlet Allocation), the most representative topic modeling technique, to discover mobility patterns of bus passengers in Cheong-Ju city. To understand discovered patterns, we construct a data warehouse and perform multi-dimensional analysis by bus-route, region, time-period, and the mobility patterns (get-on/get-off station). In the case of Cheong Ju, we discovered mobility pattern 1 from suburban area to Cheong-Ju terminal, mobility pattern 2 from residential area to commercial area, mobility pattern 3 from school areas to commercial area.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.655-666
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• 2021

Purpose: This study is to develop a method for measuring dynamic speeding risks using vehicle trajectory big data and to demonstrate the feasibility of the devised speeding index. Method: The speed behaviors of vehicles were analysed in microscopic space and time using individual vehicle trajectories, and then the boundary condition of speeding (i.e., boundary speed) was determined from the standpoint of crash risk. A novel index for measuring the risk exposure of speeding was developed in microscopic space and time with the boundary speed. Result: A validation study was conducted with vehicle-GPS trajectory big data and ground-truth vehicle crash data. As a result of the analysis, it turned out that the index of speeding-risk exposure has a strong explanatory power (R²=0.7) for motorway traffic accidents. This directly indicates that speeding behaviors should be analysed at a microscopic spatiotemporal dimension. Conclusion: The spatial and temporal evolution of vehicle velocity is very variable. It is, hence, expected that the method presented in this study could be efficaciously employed to analyse the causal factors of traffic accidents and the crash risk exposure in microscopic space using mobility trajectory data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.8
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• pp.544-554
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• 2014

In this paper, we introduce a cyber-physical intersection systems (CPIS) which intimately interconnects roadside units (RSU) located at intersection, on-board units (OBU) of moving vehicles, and smartphone apps. Based on the architecture of CPIS, we also present a green light optimal speed advisory based on the reference mobility models (GLOSA-RMM) to reduce intersection stopping time (IST) and fuel consumption. Based on several reference mobility models, the GLOSA-RMM determines the appropriate speed advisory by taking into account the current mobility and the intersection traffic light status, and then provides screen/voice GLOSA instructions to minimize the driver's distraction. We show that the GLOSA-RMM can reduce both the IST and the fuel consumption through the numerical results obtained from the prototype of the CPIS consisting of the OBU, the RSU and the smartphone app.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.151-159
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• 2017

This study deals with characteristics and risk of a PM based on user survey result, road driving test and data analysis of PM accident. Text mining method is applied to extract PM accident data from Big Data, which are claim data of private insurance company. Road driving test and survey on safety, convenience, noise, overtake ability, steering ability, and climbing ability of PM are performed to evaluate user's safety and convenience considering domestic road condition. As the result of claim data analysis, annual average increase rate of PM accident is 47.4% and average compensation of personal mobility is higher than that of bicycle by maximum 1.5 times. 79.8% of PM accident is self-caused accident due to unskilled driving and age-specific diagnosis rate of driver over 60 is higher than that of under 60. Diagnosis rate of over 60 at lower limb, foot, rib and spine is especially higher than that of under 60. As the result of road driving test and user survey, satisfaction level on safety and convenience of PM is evaluated as close to that of bicycle and satisfaction level of PM is increased after boarding. Overtake ability, steering ability, and climbing ability of PM are evaluated as same or better than that of bicycle but warning equipment to pedestrian or bike such as horn is required because noise level of PM during driving is too low. Finally, user survey result shows that bicycle road is suitable for PM and safety standard, advance-education and insurance are required for PM. It is suggested that drivers' license for PM can be replaced by advance-education. Results of this study can be used to prepare safety measures and legal basis for PM operation.

• Journal of the Korean Regional Science Association
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• v.33 no.3
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• pp.101-113
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• 2017

This study examines the mobility of the disadvantaged population groups in terms of public transportation using the 2014 smart card data in Seoul, Korea. Particularly, we focus on the disadvantaged population such as senior group, junior group, and low-income population group. Based on the spatial distributions of public transportation mobility levels and the disadvantaged population groups, we identify specific areas where public transportation service should be improved for the disadvantaged population. As a result, we identify 15 administrative-dongs where the ratio of the disadvantaged population is high while the mobility index of public transit is low. The main contributions of this study are as follows. First, we use the smart card data which contains the information of actual trip made by individuals and develop the evaluation process of urban mobility for the disadvantaged population groups. Second, we identify the specific areas where public transportation service should be improved for the different group of the disadvantaged population. Lastly, we discuss policy implications to improve the urban mobility of the disadvantaged population.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.661-664
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• 2017

Recently, during disasters occurrence, dealing with emergencies has been handled well by the early transmission of disaster relating notifications on social media networks (e.g., Twitter or Facebook). Intuitively, with their characteristics (e.g., real-time, mobility) and big communities whose users could be regarded as volunteers, social networks are proved to be a crucial role for disasters response. However, the amount of data transmitted during disasters is an obstacle for filtering informative messages; because the messages are diversity, large and very noise. This large volume of data could be seen as Social Big Data (SBD). In this paper, we proposed a big data platform for collecting and analyzing disasters' data from SBD. Firstly, we designed a collecting module; which could rapidly extract disasters' information from the Twitter; by big data frameworks supporting streaming data on distributed system; such as Kafka and Spark. Secondly, we developed an analyzing module which learned from SBD to distinguish the useful information from the irrelevant one. Finally, we also designed a real-time visualization on the web interface for displaying the results of analysis phase. To show the viability of our platform, we conducted experiments of the collecting and analyzing phases in 10 days for both real-time and historical tweets, which were about disasters happened in South Korea. The results prove that our big data platform could be applied to disaster information based systems, by providing a huge relevant data; which can be used for inferring affected regions and victims in disaster situations, from 21.000 collected tweets.