• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.388-390
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• 2021

This paper introduces a system that can help visually impaired people to board a bus using an embedded board with keypad, dot matrix, lidar sensor, NFC reader, a public data portal Open API system, and deep learning algorithm (YOLOv5). The user inputs the desired bus number through the NFC reader and keypad, and then obtains the location and expected arrival time information of the bus through the Open API real-time data through the voice output entered into the system. In addition, by displaying the bus number as the dot matrix, it can help the bus driver to wait for the visually impaired, and at the same time, a deep learning algorithm (YOLOv5) recognizes the bus number that stops in real time and detects the distance to the bus with a distance detection sensor such as lidar sensor.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.175-177
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• 2022

This paper introduces a system that assists the blind to move to their destination, and compares the performance of 3-types of deep learning network (DNN) used in the system. The system is made up with a smartphone application that finds route from current location to destination using GPS and navigation API and a bus station installation module that recognizes and informs the bus (type and number) being about the board at bus stop using 3-types of DNN and bus information API. To make the module recognize bus number to get on, We adopted faster-RCNN, YOLOv4, YOLOv5s and YOLOv5s showed best performance in accuracy and speed.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.292-295
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• 2020

In the modern society, people can easily reach destination by using the rapidly developed public transportation services. Recently, location information is provided through the network, but this is useless to the weak people on transportation like the handicapped. This paper proposes a bus alert terminal system equipped with the arrival information of public buses based on location information and distance measurement sensor. By using this system, we look forward to providing more convenient and accessible services for the weak people on transportation.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.261-268
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• 2016

One of the most comfortable transportation in our day to day life is the bus, which provides real time information. In order to obtain reliable information on the arrival time of this information, BIS (Bus Information System) needs to analyse the main factor for the traffic environment. To manage the system, regional information analysis by local municipalities should be prioritized. In this paper, we analyse the features that are expected to affect traffic environment by commuting the travel to school, market, tourism and other influences around Suncheon-si, which has the facilities for education, tourism and urban locality. Data cleaning is performed on the DB information that is being collected from characterization BIS, which is organized by day of the week, day and month, to analyse the key factors of the traffic flow. If this is utilized by applying a key factor to the real time information, it is expected to provide more reliable and accurate information.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.592-594
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• 2019

본 연구는 서울숲 일대의 인구유동을 스마트폰으로 바로 확인할 수 있는 유비쿼터스 지도에 구성원리에 대해 다루었다. 버스를 보지 않아도 정류장에서 도착예정시관과 내부혼잡도를 파악할 수 있는 것처럼, 서울숲 인근에 방문한 개개인의 스마트폰에서 전송된 GSP정보, 인근 지역의 가게 WIFI, 서울시 공용 WIFI를 수신하여 인구분포현황을 표시, 쾌적한 환경에서 서울숲 일대를 돌아볼 수 있는 환경을 조성하고자 한다.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.33-41
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• 2012

BIS is an arrival guidance system which is able to supply passengers with bus service condition via Kiosks at a bus stop, internet and mobile service through pinpointing bus location in real time. It is very significant to improve the quality of traffic information by quality control of GIS-based bus network so as to maintain navigational information and to implement reliable BIS. Therefore this study aims to build criteria to quantitatively evaluate data quality of the product in accordance with the process in bus network data and to suggest guideline of quality control. To achieve this, we have categorized geometric and logical errors occurring during constructing bus network database by giving a specific case study on TAGO and set up sectional guideline and procedures to examine database for systematic and coherent quality control management. Proceeding from what has been said above, the outcome of our research leads to quality guarantee for objective and reliable bus network database and is fully expected to bring benefit of providing a more accurate public transportation information and improving reliability of BIS through preventing a variety of errors in system operation in advance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.42-52
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• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.3 s.11
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• pp.1-12
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• 2006

Nowdays ITS is being installed in each local autonomous entity, and BIS installation and operation is prior to other ITS sub systems for the public service. The methods of positioning md wireless communication in BIS are DSRC+DSRC, GPS+wireless communication, Beacon+Beacon, which are chosen and operated as the local features. Before this study, the before and after survey of BIS' quality have only been done without performance evaluation of BIS. So the method of BIS' evaluation have been established including performance test in this paper. And the evaluation of some BIS' reliability and efficiency have been done using the reliability of arriving data and the wireless communication response rates after choosing typical BIS' sub system.