도시고속도로의 유입연결로 접속영향권내 밀도예측모형 구축에 관한연구-부산광역시 번영로를 대상으로

Abstract

Urban freeway is the transportation facility which has the function to swiftly handle a large-scale travel demand and takes charge of a key role in the urban transportation system in the city. Urban freeway is also composed of the basic segment, the weaving segment, the ramp roadway and the ramp junction. More traffic congestion and accidents caused by the vehicles to enter or exit through the entrance and exit ramps occur within the ramp junctions than those caused by the lane change or deceleration maneuver of the vehicles on the mainline segment of the urban freeway.

Additionally, density is used as the measure of effectiveness (MOE) within the ramp junction influence area suggested in the Korean highway capacity manual(KHCM) in the LOS analysis of the ramp junction, and also density predictive models suggested in the KHCM is constructed based on the expressway with the speed limit of 100km/h or 110km/h in Korea. So, the density predictive models suggested in the KHCM are needed to verify if the models could be applied to the urban freeway with the speed limit of 80km/h or less, because the speed limits on most of the urban freeways in Korea are 80km/h or less.

The purpose in this study is to collect and investigate the real-time traffic characteristics within the on-ramp junction influence areas of the urban freeway, compare and analyze the traffic characteristic relationship within the ramp junction influence areas of the urban freeway, and finally construct and verify the appropriate density predictive model within the on-ramp junction influence area of the urban freeway by comparing with the USHCM and KHCM models.

By analyzing the real-time traffic characteristics, and constructing and verifying the density predictive model within the on-ramp junction influence area of the urban freeway, the following conclusions were drawn:

ⅰ) Traffic characteristic analyses were shown to have a distinct difference in the traffic characteristic distributions within the on-ramp junction influence areas. The capacities within the on-ramp junction influence areas were also found to decrease by about 30% when compared with those on the mainline segments of the urban freeway.

ⅱ) Density predictive model was especially shown to have a high explanatory power with the coefficient of determination() of 0.986, and also to be very valid with a high correlation coefficients() of about 0.994 within the on-ramp junction influence areas of the urban freeway.

ⅲ) Density predictive model in this study was shown to have a higher significance by showing no difference in the predicted and observed densities with the significant probability of 0.453 than the significant probability of 0.000 in the KHCM and USHCM models at the 95% level of significance as a result of the t-test.