소매업의 발달과정과 입지 변화에 관한 한.미 비교 연구 (A comparative study between Korea and the USA on the development process in retail trade & its changing locations)
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- 한국지역지리학회지
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- 제6권2호
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- pp.21-40
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- 2000
급변하는 소매업 환경 속에서 각 소매업체는 생존을 위한 다양한 전략을 구사하고 있다. 무한 경쟁 시대를 맞이하여, 유럽 및 미국의 대자본이 포화상태에 달한 자국 시장 대신 해외로 시장을 확대하고 있으므로, 한국의 소매업도 이들과의 경쟁은 불가피한 일이다. 이에 본 연구에서는 한국 및 미국 소매업의 발달과점과 입지 변화를 비교 분석하여, 한국 소매업의 바람직한 발전 방향 모색에 기여하고자 한다. 독창적인 마케팅 전략으로 새로운 업태의 세계적 선두 주자로서 계속 성장하고 있는 미국 소매업의 실체 분석은 한국 소매업의 바람직한 전략 구축의 기반이 된다. 최근, 미국의 소매업은 가격 지향의 할인점 시기를 지나서 고급화, 다양화, 대규모 테마파크화가 진행되고 있다. 이와 함께 입지 면에서는 새로운 교외지역이 성장하는 가운데, 기존 업체의 재정비 재개발로 도시 내부지역이 활성화된다. 그리고 입지가 문제시되지 않는 전자 소매업 TV 홈쇼핑도 성장하고 있다. 한편, 시장과 백화점 중심의 정적(靜的)인 구조를 지속해 오던 한국의 소매업은 1980년대 말 이후 다양한 업종이 소개되면서 급변하고 있다. 특히 1990년대 중반이후에는 대형 할인점이 급성장하고 유통구조의 체계화, 서비스 중심의 소매업으로 새로운 도약의 계기를 마련하고 있다. 한국 소매업의 발전을 위해서는 우선 정보통신 및 기술수준을 적극적으로 활용해야 한다. 나아가 전통과 현대/세계의 접목, 중소 업체의 협동화 같은 독창적인 시도, 그리고 유통업계 행정부 학계의 적극적인 지원이 요구된다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.
관상동맥우회술은 인공심폐기 및 심근보호의 안정성으로 인공심폐기 사용하에 시행하는 것이 보편화된 방법이다. 그러나 좌전하행지 및 우관상동맥에 병소가 위치하는 경우는 인공심폐기의 사용 없이 심장이 박동하는 상태에서 관상동맥우회술을 시행하는 방법도 일부에서는 시행되어 왔으며 최근에는 최소한의 절개하에서 관상동맥우회술을 시행하는 방법도 대두되었다. 본 병원에서는 1996년 3월부터 8월까지 시행한 35례의 관상동맥우회술중 6례에서 인공심폐기를 사용하지 않고 관상동맥우회술을 시행하였다. 환자는 남자 4례와 여자 2례였으며 나이는 55세에서 76세로 평균 64세였다. 수술전 관상동맥의 병소는 6례 모두 좌전하행지에 90%이상의 협착을 보였으며 1례는 대각지가 50%, 또 다른 1례에서는 좌주관상동맥이 50%정도의 협착을 동시에 보였다. 수술에 필요한 개흉방법은 통상적인 정중흉골절개가 1례, 부분흉골절개술을 시행한 경우가 2례, 좌측전흉부를 개흉한 경우가 3례였다. 관상동맥우회술은 5례에서 좌측내흉동맥을 좌전하행지에 문합하였으며, 1례에서는 좌측내흉동맥에 요골동맥편을 단단문합한후 좌전하행지에 문합하였다. 또한 1례는 복재정맥을 이용하여 대각지에도 동시에 우회술을 시행하였다. 수술후 기관발관시간은 4시간에서 14시간으로 평균 9시간이었다. 수술에 사용한 혈액의 양은 평균 800 ml였으며 2례에서는 전혀 혈액을 사용하지 않았다. 이들중 5례에서 수술후 추적관상동맥조영술을 술후 7일에서 10일사이에 시행하여 100%의 개통율을 보였다. 결론적으로 좌전하행지나 우관상동맥에 협착이 있는 경우나 상기병소에 경피적풍선확장술이 실패한 경우에 인공심폐기를 사용하지 않고 최소한의 개흉하에 관상동맥우회술을 시행하는 것도 하나의 방법이라 사료되며 앞으로 기술적 발전과 더불어 적응대상군이 증가할 것으로 판단된다.32, 88.67