SANET-CC : 해상 네트워크를 위한 구역 IP 할당 프로토콜 (SANET-CC : Zone IP Allocation Protocol for Offshore Networks)
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- 지능정보연구
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- 제26권4호
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- pp.87-109
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- 2020
현재 육상에서는 유무선 통신의 발전으로 다양한 IT 서비스를 제공받고 있다. 이러한 변화는 육상을 넘어서서 해상에서 항해 중인 선박에서도 다양한 IT 서비스가 제공되어야 하며 육상에서 이용하는 것과 마찬가지로 양방향 디지털 데이터 전송, Web, App 등과 같은 다양한 IT 서비스들의 제공에 대한 요구가 증가될 것으로 예상하고 있다. 하지만 이러한 초고속 정보통신망은 AP(Access Point)와 기지국과 같은 고정된 기반 구조를 바탕으로 네트워크를 구성하는 지상에서는 쉽게 사용할 수 있는 반면 해상에서는 고정된 기반 구조를 이용하여 네트워크를 구성할 수 없다. 그래서 전송 거리가 긴 라디오 통신망 기반의 음성 위주의 통신 서비스를 사용하고 있다. 이러한 라디오 통신망은 낮은 전송 속도로 인해 매우 기본적인 정보만을 제공할 수 있었으며, 효율적인 서비스 제공에 어려움이 있다. 이를 해결하기 위해서 디지털 데이터 상호교환을 위한 추가적인 주파수가 할당되었으며 이 주파수를 사용하여 활용할 수 있는 선박 애드 혹 네트워크인 SANET(ship ad-hoc network)이 제안되었다. SANET은 높은 설치비용과 사용료의 위성 통신을 대신하여 해상에서 IP 기반으로 선박에 다양한 IT 서비스를 제공할 수 있도록 개발되었다. SANET에서는 육상 기지국과 선박의 연결성이 중요하다. 이러한 연결성을 갖기 위해서는 선박은 자신의 IP 주소를 할당 받아 네트워크의 구성원이 되어야 한다. 본 논문에서는 선박 스스로 자신의 IP 주소를 할당 받을 수 있는 SANET-CC(Ship Ad-hoc Network-Cell Connection) 프로토콜을 제안한다. SANET-CC는 중복되지 않는 다수의 IP 주소들을 육상기지국에서 선박들에 이어지는 트리 형태로 네트워크 전반에 전파한다. 선박은 IP 주소를 할당할 수 있는 육상 기지국 또는 나누어진 구역의 M-Ship(Mother Ship)들과 간단한 요청(Request) 및 응답(Response) 메시지 교환을 통해 자신의 IP 주소를 할당한다. 따라서 SANET-CC는 IP 충돌 방지(Duplicate Address Detection) 과정과 선박의 이동에 의해 발생하는 네트워크의 분리나 통합에 따른 처리 과정을 완전히 배제할 수 있다. 본 논문에서는 SANET-CC의 SANET 적용가능성을 검증하기 위해서 다양한 조건의 시뮬레이션을 수행하였으며 기존 연구와 비교 분석을 진행하였다.
HSDPA（高速下行分组接入）是在第三代的W－CDMA技术基础上的3.5代移动通信异步服务. 在韩国, 它主要是通过提供可视电话服务. 由于更强大和多元化的服务扩散, 随着移动通信技术迅速的进步, 消费者需要更多的选择. 然而, 由于各种技术, 不论消费者偏好往往会溢出市场, 消费者感到越来越迷惑. 因此, 我们不应该采取只注重发展假设是下一代新技术项目的战略相反, 我们应该了解消费者接受新的形式和技术的过程, 通过制定战略, 使开发人员能够理解并提供消费者真正想要的, 从而降低进入市场的障碍. 在技术接受模型（TAM）中, 感知到的有用性和使用的简单性被认为是影响人们接受新技术的态度的最重要因素(Davis, 1989; Taylor and Todd, 1995; Venkatesh, 2000; Lee et al., 2004). 感知到的有用性是一个人相信某种特定的技术能提高他或她工作绩效的程度. 感知易用性是主观认为使用某种特定技术不需要太多体力和精力的付出的程度(Davis, 1989; Morris and Dillon, 1997; Venkatesh, 2000). 感知的愉悦性和感知的有用性已经被清楚的证明对接受技术的态度有影响(Davis et al., 1992). 比如, 网上购物的愉悦性已经表现出对消费者对网上商家的态度有积极的影响(Eighmey and McCord, 1998; Mathwick, 2002; Jarvenpaa and Todd, 1997). 消费者的感知风险是一种主观风险. 这种风险和客观可能的风险是有显著区别的. 感知风险包括心理上的风险, 这是当消费者为某一特定物品而选择品牌, 商店和购买方式时所感知到的. 企业革新产品的能力取决于有效的获得有关新产品的知识(Bierly and Chakrabarti, 1996; Rothwell and Dodgson, 1991). 知识获取是公司感知外界新事物和技术的价值的能力(Cohen and Levinthal, 1990); 是公司评估外界最新的技术的能力(Arora and Gambaradella, 1994); 是公司正确预测这项科技对未来革新的能力(Cohen and Levinthal, 1990). 消费者创新是一种在社会体系中比其他人更早接受创新的程度(Lee, Ahn, and Ha, 2001; Gatignon and Robertson, 1985). 也就是说, 它显示了消费者如何快速、方便地接受新的思路. 创新被认为是重要的, 因为它对消费者是否接受新产品和他们多快接受新产品有显著的影响(Midgley and Dowling, 1978; Foxall, 1988; Hirschman, 1980). 我们用技术接受模型来进行跨国家的研究比较, 此模型实证验证了影响态度的因素-感知有用性, 易用性, 感知愉悦, 感知风险, 创新和感知的知识管理水平-和对ＨＳＤＰＡ服务的态度之间的关系. 我们为HSDPA服务提供商开发更有效的管理方法还验证了态度和使用意图之间的关系. 在本研究中, 我们在韩国350名学生中分发了346份问卷调查. 由于其中26份收回的问卷时不完整的或者有缺失数据, 所以在假设检验时320份问卷被使用. 在英国, 200份问卷收回了192份, 舍弃了两份不完整的之后, 总共有190份问卷用于统计分析中. 整体模型的分析结果如下: 韩国, x2=333.27(p=0.0), NFI=0.88, NNFI=0.88, CFI=0.91, IFI=0.91, RMR=0.054, GFI=0.90, AGFI=0.84; 英国, x2=176.57(p=0.0), NFI=0.88, NNFI=0.90, CFI=0.93, IFI=0.93, RMR=0.062, GFI=0.90, AGFI=0.84. 在韩国消费者中, 从有关影响HSDPA服务的使用意图和态度之间的关系的假设检验的结果中, 感知的有用性, 易用性, 乐趣, 知识管理的高水平和促进创新对HSDPA移动手机的态度有积极的影响. 然后, 易用性和感知的乐趣对HSDPA服务的使用意图没有直接的影响. 这可能是因为在日常生活中使用视频电话还不是必需的这一现实. 而且消费者对ＨＳＤＰＡ视频电话的态度和使用意图有直接的关系, 这些态度包括感知的有用性, 易用性, 乐趣, 知识管理的高水平和创新. 这些关系构成了购买意图的基础, 并造成消费者决定谨慎购买的情况. 对欧洲消费者的假设检验结果揭示了感知的有用性, 乐趣, 风险和知识管理水平是影响态度形成的因素, 而易用性和创新则对态度没有影响. 特别是效果价值和感知有用性, 在快乐和知识管理之后对态度有最大的影响. 相反, 认为感知风险对态度影响较小. 在亚洲模型中易用性和感知的乐趣没有发现对使用意图有直接影响. 然而, 因为态度广泛的影响使用意图, 感知有用性, 乐趣, 风险和知识管理可被视为从使用意图中的态度发展的关键因素. 总之, 感知的有用性, 愉悦和知识管理水平在亚洲和欧洲消费者中对态度形成都有影响, 这些梯度形成了消费者的使用意图. 而且, 易用性和感知的乐趣对使用意图的假设被拒绝. 然而, 易用性, 感知风险和创新有不同的结果. 在亚洲消费者中, 感知风险对态度形成没有影响, 而在欧洲消费者中, 易用性和创新对态度都没有影响.
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.