• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.179-188
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• 2017

It is very challenging to analyze the traffic flow in the city because there are lots of traffic accidents, intersections, and pedestrians etc. Now, even in mid-size cities Bus Information Systems(BIS) have been deployed, which have offered the forecast of arriving times at the stations to passengers. BIS also provides more informations such as the current locations, departure-arrival times of buses. In this paper, we perform the time-series analysis of the traffic flow using the data of the average trvel time and the average speed between stations extracted from the BIS. In the mid size cities, the data from BIS will have a important role on prediction and analysis of the traffic flow. We used the Dynamic Linear Model(DLM) for how to make the time series forecasting model to analyze and predict the average speeds at the given locations, which seem to show the representative of traffics in the city. Especially, we analysis travel times for weekdays and weekends separately. We think this study can help forecast the traffic jams, congestion areas and more accurate arrival times of buses.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.2
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• pp.114-128
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• 2013

Rapid industrialization and economic growth have led to serious problems including reduced open space, environmental degradation, traffic congestion, and urban sprawl. These problems have been exacerbated by the absence of effective conservation and governance, and have resulted in various social conflicts. In response to these challenges, many scholar and government hope to achieve sustainable development through the establishment and management of environment-friendly planning. For this purpose, we would like to analyze functional change for ecosystem by future land-use/cover changes in South Korea. Toward this goal, we predicted land-use/cover changes from 2010 to 2060 using the future population of Statistics Korea and urban growth probability map created by logistic regression analysis and analyzed ecosystem service value using costanza's coefficient. In the case of scenario 1, ecosystem service value represented 6,783~7,092 million USD. In the case of scenario 2, ecosystem represented 6,775~7,089 million USD, 2.9~7.6 million USD decreased compared by scenario 1. This was the result of area reduction for farmland and wetland which have high environmental value relatively according to urban growth by development point of view. The results of this analysis indicate that environmentally sustainable systems and urban development must be applied to achieve sustainable development and environmental protection. Quantitative analysis of environmental values in accordance with environmental policy can help inform the decisions of policy makers and urban developers. Furthermore, forecasting urban growth based on future demand will provide more precise predictive analysis.

• 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.