• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.2
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• pp.1-13
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• 2008

Bus stops are places that a variety of things happen including waiting, meeting and taking breaks. Therefore, these locations need to be made to meet the usage demands. They should be landscaped to provide a comfortable and satisfying environment for users. At present, bus stops are made with artificial materials and are all unified in form without consideration to landscape, ecology and other characteristics. Users are exposed to the poor surroundings. However, if small urban parks, which are of vital importance as well as extremely convenient, were connected to bus stops, the results would be a quantitative increase in the leisure locations and a qualitative change in the form of improved landscape and various forms of parks. The research was carried out to determine the effects of connecting bus stops and small parks, and the results are as follows. First, small parks provide resting areas for waiting passengers and buses. Second, it provides presently insufficient rest areas with convenience facilities and makes it more comfortable to take buses and use the park. Third, it provides a multi-functional efficient use of space that functions as both bus stop and park. Fourth, it can be a symbolic landmark that can represent the color of the area by adding a cultural element. Fifth, the existing green space in the park can contribute to the natural environment of the city. This study deduced general problems surrounding bus stops on the basis of selected indices and researched bus stops which are being used as parks and the places which have the potential for such purposes, in order to suggest a plan of attack. In addition, this proposal can create a new form of space, the bus stop park, and try to create a module of cases to establish it.

• Journal of Distribution Science
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• v.10 no.2
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• pp.43-52
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• 2012

As experiential education services are growing, the need for proper management is increasing. Considering that adequate measures are an essential factor for achieving success in managing something, it is important for managers to use a proper system of metrics to measure the performance of experiential education services. However, in spite of this need, little research has been done to develop a valid and reliable set of metrics for assessing the quality of experiential education services. The current study aims to develop a multi-item instrument for assessing the service quality of experiential education. The specific procedure is as follows. First, we generated a pool of possible metrics based on diverse literature on service quality. We elicited possiblemetric items not only from general service quality metrics such as SERVQUAL and SERVPERF but also from educational service quality metrics such as HEdPERF and PESPERF. Second, specialist teachers in the experiential education area screened the initial metrics to boost face validity. Third, we proceeded with multiple rounds of empirical validation of those metrics. Based on this processes, we refined the metrics to determine the final metrics to be used. Fourth, we examined predictive validity by checking the well-established positive relationship between each dimension of metrics and customer satisfaction. In sum, starting with the initial pool of scale items elicited from the previous literature and purifying them empirically through the surveying method, we developed a four-dimensional systemized scale to measure the superiority of experiential education and named it "Experiential Education PERFormance" (EEPERF). Our findings indicate that students (consumers) perceive the superiority of the experiential education (EE) service in the following four dimensions: EE-empathy, EE-reliability, EE-outcome, and EE-landscape. EE-empathy is a judgment in response to the question, "How empathetically does the experiential educational service provider interact with me?" Principal measures are "How well does the service provider understand my needs?," and "How well does the service provider listen to my voice?" Next, EE-reliability is a judgment in response to the question, "How reliably does the experiential educational service provider interact with me?" Major measures are "How reliable is the schedule here?," and "How credible is the service provider?" EE-outcome is a judgmentin response to the question, "What results could I get from this experiential educational service encounter?" Representative measures are "How good is the information that I will acquire form this service encounter?," and "How useful is this service encounter in helping me develop creativity?" Finally, EE-landscape is a judgment about the physical environment. Essential measures are "How convenient is the access to the service encounter?,"and "How well managed are the facilities?" We showed the reliability and validity of the system of metrics. All four dimensions influence customer satisfaction significantly. Practitioners may use the results in planning experiential educational service programs and evaluating each service encounter. The current study isexpected to act as a stepping-stone for future scale improvement. In this case, researchers may use the experience quality paradigm that has recently arisen.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.80-88
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• 2020

With the issuance of one-week fine dust emergency reduction measures in March 2019, the public's anxiety about fine dust is increasingly growing. In order to assess the application of air purifying plant-based bio-filters to public facilities, this study presented a method for measuring pollutant reduction effects by creating an indoor environment for continuous discharge of particle pollutants and conducted basic studies to verify whether indoor air quality has improved through the system. In this study conducted in a lecture room in spring, the background concentration was created by using mosquito repellent incense as a pollutant one hour before monitoring. Then, according to the schedule, the fine dust reduction capacity was monitored by irrigating for two hours and venting air for one hour. PM₁₀, PM_2.5, and temperature & humidity sensors were installed two meters front of the bio-filters, and velocity probes were installed at the center of the three air vents to conduct time-series monitoring. The average face velocity of three air vents set up in the bio-filter was 0.38±0.16 m/s. Total air-conditioning air volume was calculated at 776.89±320.16㎥/h by applying an air vent area of 0.29m×0.65m after deducing damper area. With the system in operation, average temperature and average relative humidity were maintained at 21.5-22.3℃, and 63.79-73.6%, respectively, which indicates that it satisfies temperature and humidity range of various conditions of preceding studies. When the effects of raising relatively humidity rapidly by operating system's air-conditioning function are used efficiently, it would be possible to reduce indoor fine dust and maintain appropriate relative humidity seasonally. Concentration of fine dust increased the same in all cycles before operating the bio-filter system. After operating the system, in cycle 1 blast section (C-1, β=-3.83, β=-2.45), particulate matters (PM₁₀) were lowered by up to 28.8% or 560.3㎍/㎥ and fine particulate matters (PM_2.5) were reduced by up to 28.0% or 350.0㎍/㎥. Then, the concentration of find dust (PM₁₀, PM_2.5) was reduced by up to 32.6% or 647.0㎍/㎥ and 32.4% or 401.3㎍/㎥ respectively through reduction in cycle 2 blast section (C-2, β=-5.50, β=-3.30) and up to 30.8% or 732.7㎍/㎥ and 31.0% or 459.3㎍/㎥ respectively through reduction in cycle 3 blast section (C-3, β=5.48, β=-3.51). By referring to standards and regulations related to the installation of vegetation bio-filters in public facilities, this study provided plans on how to set up objective performance evaluation environment. By doing so, it was possible to create monitoring infrastructure more objective than a regular lecture room environment and secure relatively reliable data.

• 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.