실시간의 BIS자료를 이용한 간선도로의 버스도착시간 예측모형구축에 관한 연구

Abstract

Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. So, bus information system is in haste introduced into their bus transportation systems in the cities, and also its extension taken into consideration in some of the cities which already imported the bus information system. However, bus information data such as the present bus location, the user waiting time, the bus arrival time, and so on are not correctly provided in most of the cities putting the bus information system into operation because the proper models for predicting the bus arrival time are not suggested yet.

The purpose in this study is to investigate the real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze the real-time bus traffic characteristic data such as bus travel speed, inter-arrival time, the number of vehicles, etc. in the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments such as the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for the real-time bus arrival time prediction on the bus stops of the arterial under the study.

From the bus roadway and traffic characteristic analyses on the each unit segment, and the integrated model construction and verification for predicting the real-time bus arrival time on the bus stops of the urban arterial under the study, the following conclusions were drawn:

ⅰ) Roadway characteristics were found to show a little difference in the width and length of the roadway, the number of the unit segments, and the figure of intersection on the arterial under the study, but traffic characteristics were found not to show a distinct difference in the number of vehicles, the travel speeds, and the inter-arrival times on the arterial under the study.

ⅱ) Signal operation characteristics were found to show a considerable difference in the Green time ratios depending on the signalized intersections and time periods within the study segments, but all the arterial under the study segments were found to be put in operation by the real-time signal progressive operation system with the bus information system, except the Samsanro.

ⅲ) Bus traffic characteristics were found not to show a distinct difference in the number of buses and routes passed on the arterial, but they were found to be a distinct difference in the time intervals, the travel speeds and the travel times depending on the unit segment under the study. Especially, the travel times at the node were found to show a distinct difference in Green and Red signals.

ⅳ) Unit segment models were needed to be differently constructed based on the unit segments and time periods. Particularly, the WSM1 was shown to be correlated with the bus traffic characteristics during the morning and 1-day periods, the WSM2 correlated during the noon period, and the ESM2 correlated during afternoon period at the bus stop, respectively.

ⅴ) ESM1 and ESM2 were shown to be correlated with the bus traffic characteristics during the 1-day period, and the WSM1 correlated during the morning period at the intersection. Also, the ESM1 and ESM2 were shown to be correlated with the bus traffic characteristics during the noon period and afternoon period at the node, respectively.

ⅵ) ESM1 and ESM2 were shown to be correlated with the bus traffic characteristics during the 1-day and morning periods, respectively at the travel section. And the WSM1 was shown to be correlated with the bus traffic characteristics during the noon period, and the ESM1 and ESM2 correlated during the afternoon period at the travel section.

ⅶ）Integrated predictive model was shown to have a high explanatory power in the coefficient of determination () of 0.945 or more, and a high significance at the F-significance level of 0.000 and the t-significance level of 0.000. Also, integrated predictive model was found to be very valid in testing between the observed and expected travel times at the 95 % level of confidence.

Thus, it was concluded that the integrated predictive model would be very valid in predicting the real-time bus arrival time in the cities putting the bus information system(BIS) in operation.