• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.85-97
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• 2010

The present study aims at estimating the model of bus bunching duration along with its influential determinants in an attempt to understand the status of bus bunching being created by buses from various routes converging into one bus stop. To do so, the duration analysis, a well-known survival analysis, was adopted in order to capture the distribution of duration time as for 8 base lines, accordingly developing the model best fit for weibull distribution. Key attention to draw out the duration time model for bus bunching phenomena was laid, by analyzing 18 impact factors, on such criterion variables as number of berth, number of bus line in each berth and maximum capacity of on-and-off passengers in each line. Comparison in two typical types of bus lane was made between bus-only center lane(Dobong Mia-ro) and normal street-side lane(Tongil Euiju-ro). In this regard, the study, based on the model as above, suggested appropriate alternatives to improve the bus operation by effectively controlling bus bunching.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.1-11
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• 2008

As bus is realized by economical, environmental transportation that is available mass transport than car, various policy for improvement of services is achieved. As innovative public transportation systems like BMS (Bus Management System) have established, it is possible to manage bus service efficiently. However, the present bus service management system mainly focuses on enhancing service reliability represented by schedule adherence index. This study discusses the necessity of a special management for bus bunching phenomena at stops, and develops two kinds of bus bunching indices based on the Number of Berth and the Average Bus Arrival Rate. The bus bunching indices were measured by utilizing the bus operational information from BMS at the Seoul TOPIS(Transportation & Information Service). In order to evaluate the sensitivity of the Indices, the indices were applied to two different bus groups: buses on exclusive bus median lane, and regular (shared) lanes. As analysis result, is bunching as is near in downtown and is bunching to peak time morning than the afternoon. Compared with the schedule adherence index, the suggested indices were proved as an efficient complementary indices in the evaluation of the bus operational performance. The results of index comparison between exclusive bus median lane and shared lanes can promote the expansion of exclusive bus median lane. Moreover, it can also be used as a reference in deciding bus station scale including the Number of Berth and the route adjustment plan.

• 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.