• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.643-656
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• 2019

The development of hot social networking services, including the Internet, has transformed rental car services into IT-based car-sharing services. The car-sharing service is a service that allows people to rent cars online without having to face-to-face. It has become the preffered service among young people who are accustomed to drinking and eating alone. Users can use their smartphones to book their types of cars and hours of rent, and then go to a nearby designated parking lot to use their reserved cars. You can open a designated car door with a smart phone and drive. It is a very convenient service to pay for the distance you drive. However, the car-sharing service already in use in business has the following problems: underage who do not have a driver's license may drive a car borrowed by an acquaintance, the status of a license registered at the time of join membership with a car-sharing company may change to a suspension or cancellation of a license while renting and driving, or even a drunk person may rent a car and drive. In this paper, the method to solve these problems has been studied and proposed. The proposed method is to reduce the cost of investment by a car-sharing service provider and to minimize user inconvenience. And, it was compared and analyzed with the existing method. For the method to be used efficiently, the active operation of the car-sharing company and the government's policies will have to be supported.

• Information Systems Review
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• v.18 no.4
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• pp.141-154
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• 2016

Mobile application is growing explosively with the advent of a new technology: smartphones. Mobile application is a new marketing channel and performs as a start-up platform. This study examines the effect of mobile application on the off-line market. Despite the continuous declining demand for taxi service, paradoxically, the supply of taxi service has increased. The taxi industry can be categorized into general taxi and call taxi. General taxi is accidental and inefficient because it has to search for its own passenger. As call taxi takes the request of a passenger, it is more efficient than general taxi. However, the current defective passenger-taxi driver matching system and insufficient taxi driver management hinder the development of the call taxi market. Differences in differences (DID) is an econometrical methodology that examines whether or not an event has meaningful influence. This research uses DID to investigate the effect of the Kakao taxi application on the call taxi industry. Furthermore, it examines the effect of major companies' reckless diversification, which is considered unethical behavior. The passengers of call taxi data from August 2014 to July 2015 and those of designated driving service data of the same period were collected as the control group.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.320-323
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• 2014

Today is going with the advancement of intelligent transportation systems and traffic environment and helping to provide safe and convenient service through a mobile device work with the popularization of the vehicle black box. The traffic flow by a variety of causes is constantly changing, it is often unable to prepare the driver, depending on external factors can not be controlled by the power of the public, leading to a major accident. The system needs to pass the real-time weather data in the inter-operator to prevent this. The proposed detection algorithm weather information delivered real-time weather information for this paper. The weather condition is detected by using the contrast between the histogram of the motion of the wiper and the clear day algorithm. In general, the wiper is worked in extreme weather conditions that will have a value different contrast due to rain or snow. Situation was considered clear, snowy conditions, such as using it on a rainy situation. First, designated as ROI (Region Of Interest) of the minimum area that can be detected in order to reduce the amount of calculation for the wiper, the wiper, which was detected through the operation of the threshold Thresholding the brightness of the vehicle wiper. In addition, we distinguish the value of each meteorological situation by using contrast. Results was obtained to 80% for the snow conditions, a rainy situation.