【机械类毕业论文中英文对照文献翻译】累积降雪与降雪
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机械类毕业论文中英文对照文献翻译
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【机械类毕业论文中英文对照文献翻译】累积降雪与降雪,机械类毕业论文中英文对照文献翻译,机械类,毕业论文,中英文,对照,文献,翻译,累积,降雪
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附录A摘要降雪支出的减少是日本政府迫切需要解决的问题。降低国家级降雪支出需要：（1）成本结构分析，（2）区域间成本比较方法的制定，（3）降低成本标准的制定。我们研究了客观地确定降雪成本效益的方法，对于区域间的比较已经被认为是困难的。为此，我们开发了一个模型，使用“降雪单位成本”（UCSR）的线性回归线，这是基于降雪成本和累积降雪之间的关系。本研究介绍了如何开发该模型，并讨论了其适用性，通过使用实际降雪记录。我们还研究了模型的适用性，当模型导出的数据包括极端降雪年和异常少雪年的数据。关键词：降雪、降耗、预算、UCSR、降低成本标准1.介绍日本的许多大城市都在下雪地区，除雪起着重要的作用。确保冬季道路畅通（图1）。研究已经解决了道路除雪的各个方面。作为第一个研究在日本的经济利益的道路除雪，IGARASHI等。衡量利益。他们还提出了一种部署除雪机械的方法，以使“总降雪成本最小化”。萨凯等。计算雪去除的好处，除掉雪去除所造成的经济损失的降雪成本。图1-1日本及海外主要城市降雪量Fig.1-1 Snowfall of major cities in Japan and overseas最近的研究集中于受益者对降雪服务水平的认识，以找到降低成本的解决方案。从这一角度出发，讨论了降雪需求、居民支付意愿、居民降雪满意度等问题。大多数关于降雪的研究涉及经济效益，或使用问卷调查来解决公众参与或顾客满意度。很少有深入的研究已经解决了成本结构的降雪日本政府。关于降低基础设施管理成本的努力，包括：美国和其他国家，巴罗加，E.V.建议测量性能为基础的服务水平，使用它的预算和资源分配；和Lindsey，R.K.et al。集中的工作条件，包括地理和气候不同的工作场所，以实现有效的资源分配降雪。除了除雪之外，还包括桥梁管理系统，以优化基础设施管理的预算分配。然而，对于基于成本结构分析的雪去除预算和资源分配以及除雪成本的时间序列数据的区域间比较，似乎很少有研究。为了降低国家层面的除雪成本，必须分析除雪成本的结构，开发一种区域成本比较方法，制定降低成本的标准。降雪成本的区域间比较被认为是非常复杂的，因为成本随着降雪量、气温的变化而变化，雪纹理和其他自然现象；随着发展程度（例如城市化对农村）；以及对雪控制的需求，包括道路使用者习惯于降雪。本文介绍了一种客观评价方法，通过澄清积雪成本和积雪的关系，来评估积雪成本，这两种方法相对容易获得，以及如何在区域间比较除雪成本是可能的。利用所开发的估计方法。通过对模型估算的除雪费用与实际除雪费用的比较，验证了模型的适用性。此外，该模型的适用性当除雪数据从该模型导出包括那些对于一个非常下雪的一年和一个不寻常的少雪年。2005-2006年冬季是2005年日本发生特大降雪的第一次，日本气象厅将其命名为“2006年大雪”（FY2005）。地州市和市政府的除雪预算已经枯竭，土地、基础设施和交通部（MLIT）通过增加降雪补贴来响应地方政府的要求。在那个时候，MLIT被要求客观地估计雪去除成本通过使用数据，如降雪和指定的道路长度除雪。然而，没有建立这种估计的方法。然后，MLIT采用了本研究中引入的单位降雪（UCSR）线路模型（以下简称UCSR线路模型）的方法，用于自2005年FEI分配公共降雪预算。2.日本除雪预算制度日本的总道路长度为1.2万英里，市政道路约占84%。国民政府直接控制的国道长度仅占国民经济总量的1.9%.寒冷、降雪地区的道路长度约占日本陆地面积的60%，约为41.6公里（25.79）。迈尔斯）国家政府对高速公路、高速公路等国家的高速公路进行了全面的降雪处理，并通过公路防冻特别措施法规定，在被指定为“天寒地冻”的地州公路控制下，对部分公路进行了除雪。在雪域和寒冷地区（SCAA法）的图2。国民政府覆盖国家管理的高速公路的除雪费用。国家政府通过SCA法，对被指定为“雪”和“冷”地区的国家公路和地道的除雪费用进行补贴。该法规定，国家政府应向州政府提供三分之二的降雪费用，其余第三个州补贴。目前，国家政府对被指定为“雪”和“冷”的地区的道路补贴约为700亿日元（5亿8300万美元（1美元/日元120，FY 2007）。国家补贴的收入包括国家天然气税、汽车吨位税和汽车购置税。这些收入的支出仅限于公路相关项目，约为5兆6000亿日元（470亿美元，2007财年）。各州的除雪费都是由地级总户头和地府专户资助的。地税专户的收入来源包括轻油批发税，其支出仅限于公路相关项目。图2-1日本公路及其预算Fig.2-1 Roads in Japan and their budgets3.累积降雪与降雪成本关系的UCSR线模型本文介绍了UCSR线路模型的发展过程。一般来说，道路除雪费用包括的费用：道路除雪，2）人行道除雪，防冻剂应用和雪牵引和杂项。对于这些成本，可以考虑各种解释变量。变量包括道路长度、道路宽度、降雪量、路面状况、降雪频率和拖曳雪量。然而，获取所有这些数据是相当困难的，因此，澄清所有变量之间的关系和除雪成本是不切实际的。因此，我们试图通过使用诸如积雪和指定用于降雪的道路长度的数据来计算除雪成本，这是相对容易获得的。两种数据代表降雪量：降雪量、降雪量和年降雪量。降雪部署是为了应对每天的降雪而展开的。当我们检查降雪费用时，使用累积降雪是合适的。如上所述，除雪成本的结构是复杂的。成本随降雪量而变化，如下述公式所示。p= f（x） （3-1）其中，P是降雪成本，X是积雪。 该函数预计绘制为向上倾斜的线，因为积雪成本随着累积降雪的增加而增加。然而，总降雪成本包括与降雪量无关的成本，包括防冻剂应用的成本和固定的运营成本。函数具有变化的术语和不随累积降雪的变化而变化的术语（常数项）。如果我们假设线性回归模型能够描述除雪成本与积雪的关系，那么雪去除成本可以用公式2来表示。p= f（x）AX+B （3-2）其中P是除雪成本，X是累积降雪，A是表示每厘米累积降雪的雪去除成本的系数，B是不取决于进行除雪区域的累积降雪的降雪成本系数。在上述方程中表示的除雪费用包括拖运雪的成本，随着积雪量的增加，积雪逐渐增加。除雪成本包括逐步递增的成本，并认为表示雪去除成本和累积降雪之间关系的线不是一条简单的线。然而，当运输雪的成本不占总降雪成本时，可以认为线性近似可以描述除雪成本与累积降雪之间的关系。图3-1除雪和雪牵引费用与累积降雪的成本Fig.3-1 Costs for snow removal and snow hauling vs. cumulative snowfall接下来，我们研究了UCSR与累积降雪的关系。UCSR是通过将每年的除雪成本除以雪去除和累积降雪的道路长度来获得的，表示如下。y= p/L/x （3-3）除雪道的长度是指国家和地方政府根据SCA法规定的除雪费用的路线长度。道路长度可以被视为一个常数，因为它不随累积降雪（X）的变化而变化。去除5厘米降雪所需的工作时间和费用与去除6厘米降雪所需的时间相差不大。去除10厘米降雪所需的工作时间要比去除5厘米降雪所需的时间长，但不能长两倍。从这个例子中，可以认为UCSR随着累积降雪的增加而减少。换言之，除雪成本包括不依赖于累积降雪的成本，这意味着UCSR不能低于某个值，而USSR与积雪呈负相关。据此，UCSR曲线呈向下倾斜的回归线，代表变量之间的负相关，与X轴上的积雪和UCSR在Y轴上的累积降雪有关。为了近似UCSR，使用累积降雪的幂函数是合适的。幂函数采用方程（3-4）的形式。4.UCSR与积雪的关系USCR和累积降雪之间的关系，在这里被检查的地区管理的国家公路和地道的地区在SCA法的范围内。利用USSR线模型对各县的前期降雪成本和累计降雪量进行了近似分析，通过回归分析确定相关系数。已收集的数据的细节和地区使用的近似如下：1）每年累计降雪十年，从1995年到2004, 2年年的降雪费从1995年到2004和3年的道路长度与降雪。2）和3）的数据为国家管理的国家公路和县公路，其雪耗费用在国家政府和州之间共享，如SCA法规定。每年的累计降雪量是一个地区每天测量的降雪量的平均值。在制定年度预算要求时，制定法律规定的除雪道路长度。4.1县间决定系数的比较在这里，我们介绍了如何进行区域间的降雪成本效益比较，以福州地区和东北地区的县为例。在图4-1中，UCSR与新潟、富山和Ishikawa prefectures的累积降雪极为密切，它们的道路位于日本土家族区域发展局、土地、基础设施和交通部（MLIT）管辖之下。测定系数为0.88（图4-1）。东北地区六个县（青森县除外）的五个县的关系近似线，这五个县的判定系数较低，0.096（图4-2）。我们认为，UCSR根据气候和其他区域特征而不同，我们在东北地区的日本海一侧得到了一个线性近似，另一个近似于该地区的太平洋边。在这种划分下，相关系数较高，日海侧群的确定系数为0.39，太平洋侧群的系数为0.57。这一检验证实了类似的自然和当地条件的地区在UCSR和累积降雪的关系上有相似的趋势，并且有可能将两个以上的县分为这个检查（4-2）。 图4-1乌鲁木库地区UCSR线（新潟，富山，石川）Fig.4-1 UCSR line for the Hokuriku Region (Niigata, Toyama, Ishikawa)图4-2 ucsr线的东北地区 Fig.4-2 UCSR line for the Tohoku Region4.2区域确定的个系数图4-3所列的七个区域开发局管理的国家管理的国道的UCSR与累积降雪的散点图揭示了三个组。第一条是由关东区域开发局（A组）管理的道路，其中数据点围绕图中的较高区域聚集，而不是其他组的数据点。第二条是由楚国区域开发局管理的道路，数据点集中在较低的区域（B组）。第三条是由北海道区域开发局和其他区域局所管理的道路，除关东和诸谷区域开发局外，数据点沿A区和B区的区域（C组）分布在A和B组之间（图4-3）。图4-4（1）和（2）显示了UCSR与全国政府管理的所有国家公路的累积降雪的线，以及C组的线。前者的测定系数为0.25，后者为0.77。鉴于此，我们假设有三个UCSR线。C组可以用一条UCSR线来解释。对于A组，其线是最右上的线，UCSR是高的。C组除雪成本低。图4-3各地区UCSR线路Fig.4-3UCSR line for each region图4-4（1）UCSR线所有政府管理的国家公路Fig.4-4(1)UCSR line for all government-managed national highways图4-4（2）C组UCSR线路Fig.4-4(2) UCSR line for Group C5.使用UCSR线路的成本比较5.1各州除雪的成本效益USSR线是从除雪记录中获得的经验公式。通过使用这条线，可以估计对应于特定累积降雪的UCSR。通过比较估算的除雪成本和实际消耗的费用，一个州有可能使用UCSR线来评估任何一年的降雪成本效率。5.2从降雪数据中获得的USSR线的适用性包括多年积雪经过2005-2006年冬季（FY2005），日本各地都出现了特大暴雨，包括日本北部地区。气象局在43年内首次将“2006大雪”命名为“大雪”。同时，2006-2007年冬季（KY2006）冬岛地区的降雪量相当轻。我们将采取HukuriKu地区，以验证UCSR线模型的适用性，检查是否使用多年降雪数据绘制的UCSR线与冬季不寻常的天气将是有用的。图5-1显示了两个HUSCRUKU地区的UCSR线：一条线绘制通过使用FY 1995至2004的除雪数据和另一条线绘制使用FY 1994至2007的数据，包括一个非常下雪的一年（FY 2005）和一个异常少雪的一年（FY 2006）。图5-1显示了这两条线的确定系数。图5-1Fig.5-1如图5-1所示，从多年降雪数据（FY 19942007年）得到的USR线的确定系数（R2），包括一个非常下雪的年和一个下雪的年份，比根据多年的数据绘制的另一条线高出0.78。19952004年，不包括极端天气的人冬天。图5-2中的散点图表明，在2005财年，所有地区的除雪费用都很高。FY 2005除雪成本高的可能原因是，由于较低的积雪融化而造成的雪地运输成本比其他年份的低温时间长。其次，通过对UCSR线路除雪费用与实际费用的比较，考察了新潟地区不同年份降雪费用的成本效益。2005财年的UCSR为2327日元，与FY 2007的2390日圆几乎相同。然而，将实际UCSR与由图9中的UCSR线估算的UCSR相比，2007的UCSR可以比FY 2005更具成本效益，因为实际的UCSR和由UCSR线估计的差距（图5-2）对于FY 2007来说较小，因为更少的累积量。全年降雪量比2005年度降雪量大。FYS 2006, 2760日元的实际UCSR高于FY 2005和2007，但低于UCSR线估计的UCSR（图5-2），这意味着FY 2006的降雪工作被认为是以成本效益最高的方式进行的。三年。图5-2还显示，富山州的除雪费用是每年最低的，与其他州的费用相比。如上所述，UCSR线提供了评估不同地区除雪成本的效率的标准。该标准有助于评估除雪预算的适宜性，以及确定成本效益的除雪工作，从而最终实现除雪成本的降低。在新潟州，应研究FY 2006的除雪工作状况，以寻求最具成本效益的除雪，因为尽管三财年，该州实际的UCSR为2006年度的最高UCSR，但低于UCSR林区估计的UCSR。E和UCSR估计的差距是三年来最小的（图5-2）。图5-2乌鲁木齐地区（UFSR 20052007）UCSR线路模型实际UCSR和UCSR估计Fig.5-2Comparison of the actual UCSR and estimated UCSR by the UCSR-line model for prefectures in the Hokuriku Region (FY 20052007)6.总结本研究的8.1个要点a.提出了利用UCSR线分析降雪成本与累积降雪量之间的关系的模型。b.提出了利用UCSR线路进行除雪成本的比较。c.通过比较UCSR线路模型估算的除雪费用与实际降雪费用的消耗，验证了模型的适用性。d.利用实际资料，对冬季降雪资料中包括冬季异常天气的USSR线的适用性进行了检验。成功地解决了使用UCSR线路的预算分配问题，这使得该方法被应用于国民政府的预算编制。6.1有待改进项下列清单显示了应检查的项目，以提高除雪成本估算的准确性，并进一步降低除雪成本。a.县和国家政府数据的比较。b.分析了UCSR与部分地州的积雪相关性低的原因，这将更好地与附近的州进行比较。c.在降雪小于降雪部署标准（5厘米（0.4英寸）/天）的积雪和UCSR之间的相关性的检验。d.检验霜冻天数与防冻剂用量之间的相关性。e.推导改进的除雪预算分配的修正项，该修正项考虑了近似公式和每个数据集的分散度。f.单位劳动成本对USSR除雪的影响的检验。g.使用考虑数据分散的“近似区”进行检查。附录BAbstractThe reduction of snow removal expenditures is a pressing need for governments in Japan. Reducing snow removal expenditures at the national government level requires the following: 1) analysis of the cost structure, 2) development of a method for comparing costs among regions, and 3) development of cost reduction standards.We studied methods for objectively determining the cost effectiveness of snow removal, toward inter-regional comparison that has been regarded as difficult. To this end, we developed a model uses a linear regression line of unit cost of snow removal (UCSR) that is based on the relationship between snow removal costs and cumulative snowfall. This study presents how the model has been developed and discusses its applicability by using actual snow removal records. We also examine applicability of the model when the data from which the model is derived include those for an extremely snowy year and an unusually less snowy year.Key Words: snow removal, reduction of expenditures, budg1.INTRODUCTIONMany of Japans large cities are in snowy regions, and snow removal plays an important role in securing smooth road traffic in winter (FIGURE 1). Studies have addressed various aspects of road snow removal. As the first study in Japan on the economic benefit of road snow removal, Igarashi et al. measured that benefit. They further suggested a method for deploying snow removal machinery such as to minimize the “total snow removal cost.” Sakai et al. calculated the benefit of snow removal by dividing the mitigation of economic loss achieved by snow removal by the snow removal cost.Fig.1-1 Snowfall of major cities in Japan and overseasThere are recent studies that focus on beneficiaries awareness for the service level of snow removal to find a solution for cost reduction. From this viewpoint, issues including the demand for snow removal, residents willingness to pay and residents satisfaction levels for snow removal have been discussed.Most of these studies on snow removal addressed economic benefits, or used questionnaire surveys to address public involvement or customer satisfaction. Very few in-depth studies have addressed the cost structure of snow removal by the Japanese government.As for the cost reduction efforts for infrastructure management including snow removal in the U.S.A. and other countries, Baroga, E.V. proposed to measure performance-based service levels to use it for budget and resource allocations; and Lindsey, R.K.et al. focused work conditions including geography and climate that are different by work site to realize efficient resource allocation for snow removal. Other than those for snow removal, efforts including the bridge management system have been made to optimize budget allocation for infrastructure management. However, there seem to be few studies on a model that enables efficient snow removal budget and resource allocations on the basis of cost structure analyses as well as of inter-regional comparisons of the time-series data of snow removal costs.To reduce snow removal cost at the national level, it is necessary to 1) analyze the structure of snow removal cost, 2) develop a method for inter-regional comparison of such cost, and 3) set standards for cost reduction. Inter-regional comparison of snow removal cost has been regarded as very complicated, because the cost varies with snowfall amount, air temperature, snow texture and other natural phenomena; with the degree of development (e.g., urbanized vs. rural); and with the demand for snow control, including how accustomed the road users are to snowfall. This paper reports on the development of an objective evaluation method to estimate snow removal cost by clarifying the relationship between snow removal cost and cumulative snowfall, both of which are relatively easy to obtain, and on how inter-regional comparison of snow removal costs is possible by using the developed estimation method. The applicability of the model is also verified by comparing the snow removal costs estimated by the model with the actual snow removal costs expended. Further, the applicability of the model when the snow removal data from which the model is derived include those for an extremely snowy year and an unusually less snowy year is examined.The winter of 2005-2006 (FY 2005) was the first in 43 years with extremely heavy snowfall in Japan, and the Japan Meteorological Agency designated it “the Heavy Snowfall of 2006” (FY2005). Prefectural and municipal snow removal budgets were exhausted, and the Ministry of Land, Infrastructure and Transport (MLIT) responded to local governments requests by increasing snow removal subsidies. On that occasion, MLIT was asked to objectively estimate snow removal costs by using data such as snowfall and length of road designated for snow removal. However, no method for such estimation had been established.Then, MLIT has adopted the method, a unit cost of snow removal (UCSR) line model (hereinafter: UCSR-line model) introduced in this study for allocating public snow removal budget since FY 2005.2.BUDGETARY SYSTEM FOR SNOW REMOVAL IN JAPANOut of Japans total road length of 1.2 mil. km (744 thou. miles), municipal roads account for about 84%. The length of national roads under the direct control of the national government accounts for a mere 1.9% of the total. The length of roads in the cold, snowy areas that account for about 60% of Japans land area is about 41.6 thou. km (25.79 thou. miles). The national government fully finances snow removal for national highways, including expressways, and partially subsidizes snow removal for national highways under the control of prefectures and prefectural roads in areas designated as snowy and cold by The Special Measures Law for Ensuring Road Traffic in Snowy and Cold Areas (The SCA Law) (Figure 2).The national government covers snow removal costs for nationally managed national highways. The national government subsidizes snow removal costs for prefecturally managed national highways and prefectural roads in areas designated as snowy and cold by The SCA Law. The law states that the national government shall subsidize prefectures for two thirds of the cost of snow removal, with the prefecture covering the remaining third. At present, the national governments subsidy for roads in areas that are designated as snowy and cold by the law amounts to about 70 billion yen ($583 million ($1/ 120), FY 2007). National revenues for this subsidy include those from the national gas tax, the automobile tonnage tax and the automobile acquisition tax. Those revenues, whose disbursement is limited to road-related projects, amount to about 5.6 trillion yen ($47 billion, FY 2007).Each prefectures snow removal costs are financed from the prefectural general account and the prefectural special account. The revenue sources for the prefectural special account includes the light oil wholesale tax, whose disbursement is limited to road-related projects.Fig.2-1 Roads in Japan and their budgets3.UCSR-LINE MODEL DERIVED BY THE RELATIONSHIP BETWEEN CUMULATIVE SNOWFALL AND SNOW REMOVAL COSTHow the UCSR-line model has been developed is introduced here.Generally, the snow removal cost for roads (hereinafter: snow removal cost) consists of the costs for 1) roadway snow removal, 2) sidewalk snow removal, 3) anti-freezing agent application and 4) snow hauling and miscellaneous.Various explanatory variables can be considered for these costs. The variables include road length, road width, snowfall amount, road surface condition, snow removal frequency and amount of hauled snow.However, obtaining all these data is quite difficult; consequently, clarifying the relationship between all the variables and the snow removal costs is impractical.Thus, we tried to calculate snow removal cost by using data such as cumulative snowfall and length of road designated for snow removal, which are relatively easy to obtain.Two kinds of data represent the amount of snowfall: snowfall per snowfall event, and annual snowfall. Snow removal deployment is launched in response to each days snowfall. When we examine the cost for snow removal, it is appropriate to use cumulative snowfall.The structure of snow removal cost is complicated, as shown above. The cost varies depending on the snowfall, as expressed in the following equation.p=f(x)(1)Where, p is snow removal cost and x is cumulative snowfall.This function is expected to plot as an upward-sloping line, because snow removal cost increases with increases in cumulative snowfall. The total snow removal cost, however, includes costs that are independent of snowfall amount, including the cost for anti-freezing agent application and fixed operating costs. The function has terms that change and terms that do not change (constant terms) with changes in cumulative snowfall.If we assume that a linear regression model can describe the relationship between snow removal cost and cumulative snowfall, then the snow removal cost can be expressed by Equation 2.p=f(x)=ax+b(2)Where p is snow removal cost, x is cumulative snowfall, a is a coefficient expressing snow removal cost per centimeter of cumulative snowfall and b is a coefficient of snow removal cost that does not depend on the cumulative snowfall of the area where the snow removal is conducted. The snow removal cost expressed in the above equation includes the cost for hauling snow, which increases in stepwise increments with increases in cumulative snowfall (FIGURE 3(2).The snow removal cost includes a cost that changes in stepwise increments, and it is thought that the line that expresses the relationship between snow removal cost and cumulative snowfall is not a simple line. However, when the cost for hauling snow does not dominate the total snow removal cost, it is considered that a linear approximation can describe the relationship between snow removal cost and cumulative snowfall (FIGURE 3(3).Fig.3-1 Costs for snow removal and snow hauling vs. cumulative snowfallNext, we examine the relationship between the UCSR and cumulative snowfall.The UCSR, which is obtained by dividing the annual snow removal cost (p, (million yen) by the length of road with snow removal (L (km) and cumulative snowfall (x (cm), is expressed as follows.The work time and cost required to remove 5 cm of snowfall do not differ much from those required to remove 6 cm of snowfall. The work time required to remove 10 cm of snowfall is longer than that to remove 5 cm of snowfall, but not twice as long. From this example, it can be thought that the UCSR decreases with increases in cumulative snowfall.In other words, the snow removal cost includes costs that are not dependent on the cumulative snowfall, which means that the UCSR cannot fall below a certain value, and the UCSR has a negative correlation with the cumulative snowfall.In light of this, UCSR plots as a downward-sloping regression line that represents the negative correlation between the variables, with cumulative snowfall on the x axis and UCSR on the y axis. To approximate the UCSR, it is appropriate to use the power function of cumulative snowfall. The power function takes the form of Equation (5).4.RELATIONSHIP BETWEEN UCSR AND CUMULATIVE SNOWFALL DETERMINED BY USING PREVIOUS SNOW REMOVAL DATAThe relationship between UCSR and cumulative snowfall is examined here for prefecturally managed national highways and prefectural roads that are in the areas subject to The SCA Law.Approximation of that relationship on a prefecture-by-prefecture basis is done by using a UCSR-line model for each prefectures previous snow removal cost and cumulative snowfall, so as to determine a correlation coefficient by regression analysis.The details of the data that have been collected by prefecture and are used in the approximation follow: 1) annual cumulative snowfall for ten FY years from FY1995 to FY 2004, 2) annual snow removal costs from FY1995 to FY 2004 and 3) the length of the road with snow removal. The data for 2) and 3) are for those prefecturally managed national highways and prefectural roads whose snow removal costs are shared between the national government and the prefecture as prescribed by The SCA Law. The annual cumulative snowfall is the average of cumulative snowfall measured once everyday at several measurement sites within a prefecture. The length of road designated for snow removal by the law is set when the annual budgetary request is made.4.1Comparison of the Coefficient of Determination Among the PrefecturesHere we introduce how we can make inter-regional comparison of snow removal cost effectiveness by taking prefectures in the Hokuriku Region and Tohoku Region as examples. In Figure 4, UCSR correlates extremely closely with cumulative snowfall for Niigata, Toyama, and Ishikawa prefectures, whose roads are under the jurisdiction of the Hokuriku Regional Development Bureau, Ministry of Land, Infrastructure, and Transport (MLIT). The coefficient of determination is 0.88 (FIGURE 4).An approximation line of the relationship was obtained for five of the six prefectures in the Tohoku Region, excluding Aomori Prefecture; the coefficient of determination for those five prefectures was low, 0.096 (FIGURE 5(1). We assume that the UCSR differs depending on the climate and other regional characteristics, and we obtained one liner approximation for the prefectures on the Japan Sea side of the Tohoku Region and another for those on the Pacific side of that region. Under such a division, the correlation is higher, with a coefficient of determination of 0.39 for the Japan Sea side group and a coefficient of determination of 0.57 for the Pacific side group. This examination confirmed that areas with similar natural and local conditions have similar trends in the relationship between the UCSR and cumulative snowfall, and it is possible to group more than two prefectures for this examination (FIGURE 5(2).Fig.4-1 UCSR line for the Hokuriku Region (Niigata, Toyama, Ishikawa) Fig.4-2 UCSR line for the Tohoku Region4.2Coefficients of Determination for RegionsA scatter plot of UCSR vs. cumulative snowfall for nationally managed national highways administered by the seven Regional Development Bureaus listed in FIGURE 6 reveals three groups. The first is roads administered by the Kanto Regional Development Bureau (Group A), for which the data points cluster around a higher region in the graph than the data points for other groups. The second is roads administered by the Chugoku Regional Development Bureau, for which the data points cluster in the lower region (Group B). The third is roads administered by the Hokkaido Regional Development Bureau and other regional bureaus excluding the Kanto and Chugoku Regional Development Bureaus, for which the data points distribute along a lined region (Group C) between the regions for Groups A and B (Figure 6).FIGURE 7 (1) and (2) show a line correlating the UCSR and the cumulative snowfall for all the national highways administered by the national government, and that line for Group C, respectively. The coefficient of determination for the former is 0.25, and that for the latter is high: 0.77. In light of this, we assume that there are three UCSR lines. Group C can be explained by using one UCSR line. For Group A, whose line is the upper-rightmost line, the UCSR is high. For Group C the snow removal cost is low.Fig.4-3UCSR line for each regionFig.4-4(1)UCSR line for all government-managed national highwaysFig.4-4(2) UCSR line for Group C5.COST COMPARISON USING THE UCSR LINE5.1Cost Efficiency of Snow Removal for Each PrefectureThe UCSR line is an empirical formula obtained from snow removal records. By using this line, it is possible to estimate the UCSR that corresponds to a particular cumulative snowfall.It is possible for a prefecture to use the UCSR line to evaluate the cost efficiency of its snow removal for any given year, by comparing the estimated snow removal cost and the actual cost expended.5.2Applicability of the UCSR Line Obtained From the Snow Removal Data for Years Including an Extremely Snowy Year and a Less Snowy YearThrough 2005-2006 winter (FY2005), extremely heavy snowfall was seen all over Japan including the Hokuriku Region. The Meteorological Agency designated it “the Heavy Snowfall of 2006” for the first time in 43 years. Meanwhile, the annual snowfall in the Hokuriku Region for 2006-2007 winter (FY2006) was quite light. We will take up the Hokuriku Region to verify the applicability of the UCSR-line model, examining whether a UCSR line plotted by using the snow removal data for years with unusual weather in winter would be useful. Figure 8 shows two UCSR lines for the Hokuriku Region: one line plotted by using snow removal data from FY 1995 to 2004 and another line plotted by using the data from FY 1994 to 2007 including an extremely snowy year (FY 2005) and a unusually less snowy year (FY 2006). Figure 8 shows the coefficients of determination for the two lines.Fig.5-1As shown in Figure 8, the coefficient of determination (R2) of the UCSR line obtained from the snow removal data for the years (FY 1994-2007) including an extremely snowy year and a less snowy year is relatively high 0.78, compared with another line plotted on the basis of the data of the years (FY 1995-2004) that do not include those with such extreme weather in winter. The scatter plots in Figure 9 identify that snow removal costs for all the prefectures are high in FY 2005. The probable cause for the high snow removal costs in FY 2005 is that higher costs of snow hauling due to low snow thaw resulting from the longer period of time with low temperatures than other years.Next, we examine the cost efficiency of snow removal costs for different years for Niigata Prefecture by comparing the snow removal cost estimated by the UCSR line and the actual cost expended. The UCSR for FY 2005 is 2,327 yen (Table 2), which is almost the same as that 2,390 yen for FY 2007. However, comparing the actual UCSR with that estimated by the UCSR line in Figure 9, the UCSR for 2007 can be evaluated more cost efficient than that for FY 2005 because the disparity between the actual UCSR and that estimated by the UCSR line (Figure 9) is smaller for FY 2007 because of less cumulative snowfall of the year than that in FY 2005. The actual UCSR for FY 2006, 2,760 yen (Table 2) is higher than that for FY 2005 and 2007, however it is lower than the UCSR estimated by the UCSR line (Figure 9), which means the snow removal work in FY 2006 was supposed to be conducted in the most cost efficient manner for the prefecture in t
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