Improving Seasonal Factor Estimates for Adjustment of Annual Average Daily Traffic

Traffic volume data are input to many transportation analyses including planning, roadway design, pavement design, air quality, roadway maintenance, funding allocation, etc. Annual Average Daily Traffic (AADT) is one of the most often used measures of traffic volume. Acquiring the actual AADT data requires the collection of traffic counts continuously throughout a year, which is expensive, thus, can only be conducted at a very limited number of locations. Typically, AADTs are estimated by applying seasonal factors (SFs) to short-term counts collected at portable traffic monitoring sites (PTMSs). Statewide in Florida, the Florida Department of Transportation (FDOT) operates about 300 permanent traffic monitoring sites (TTMSs) to collect traffic counts at these sites continuously. TTMSs are first manually classified into different groups (known as seasonal factor categories) based on both engineering judgment and similarities in the traffic and roadway characteristics. A seasonal factor category is then assigned to each PTMS according to the site’s functional classification and geographical location. The SFs of the assigned category are then used to adjust traffic counts collected at PTMSs to estimate the final AADTs. This dissertation research aims to develop a more objective and data-driven method to improve the accuracy of SFs for adjusting PTMSs. A statewide investigation was first conducted to identify potential influential factors that contribute to seasonal fluctuations in traffic volumes in both urban and rural areas in Florida. The influential factors considered include roadway functional classification, demographic, socioeconomic, land use, etc. Based on these factors, a methodology was developed for assigning seasonal factors from one or more TTMSs to each PTMS. The assigned seasonal factors were validated with data from existing TTMSs. The results show that the average errors of the estimated seasonal factors are, on average, about 4 percent. Nearly 95 percent of the estimated monthly SFs contain errors of no more than 10 percent. It was concluded that the method could be applied to improve the accuracy in AADT estimation for both urban and rural areas in Florida

都市の持続可能性に向けた旅行行動と知的移動データ統合に関する包括的研究

過去数十年にわたり世界中で都市の持続可能性がトレンドとなり研究対象となっている．人々は，非効率な天然資源の消費や社会経済活動による環境破壊など，地球環境に有害な活動を行い，これには都市計画や交通計画を始め，多くの分野が密接に関係している．現在では，これらを解決する新技術の開発や応用が広範囲な研究分野で日々取り組まれている．本研究では観光に関する問題を，交通と都市の研究の観点からさまざまなビックデータを使用し，持続可能な都市開発を目標とした具体的な解決策を示した．本研究では都市や地域の持続可能性に資するデータの活用方法として，Wi-Fiパケットセンサーを使用した旅行者にとって魅力的な観光目的地マネジメントに関する研究，およびETCプローブデータを使用した旅行時間の信頼性の観測における天候の影響に関する分析を組み合わせて示した．本論文では，都市の移動性の認知に対して以下に示す3つの研究から，特徴的な結果と有効な分析手法を確立した．1）Wi-Fiパッケージセンシング調査を使用した，広域観光エリアでの周遊パターンのマイニングベースの関連法則の調査，2）Wi-Fi追跡データでの大規模な観光地の持続可能な開発に向けた魅力的な目的地の抽出，3）ETC2.0プローブデータを使用して，様々な道路タイプを考慮した旅行の信頼性に対する降雪の影響の評価．以上の研究から，複数視点の考察を積み重ね，包括的な評価と提案を行い，いくつかの重要な結果が得られた．この論文の貢献は，より良い社会への問題解決への糸口となり，今後の政策立案者にとって有意義な内容となるだろう．According to sustainability, the trend is spreading out around the world for past decades. There are many area subjects involved, such as city planning, transportation planning, and so on, because people realized human activities harmful to the environment by consuming natural resources with less efficiency process or damage environment by social and economic movements. Currently, emerging technologies considered for the proactive procedure in extensive study areas regarding new technology application and knowledge based. In term of transport and urban study, including tourism concerns, we used intelligent data from deferent sources to be demonstrating the possible solutions which involve sustainable urban development concept. In this study, as a method of utilizing data that contributes to the sustainability of cities and regions, consideration of attractive destination management for tourists by using wireless probe data, and the weather impact on travel time reliability observation by using electronic toll collection probe data, it represented as combination experiments throughout comprehensive study. This dissertation addressed three contribution studies to the composed acknowledgment of urban mobility, and it obtained the intelligent data and specific method of research-based. It consists of; 1) an association rule mining-based exploration of travel patterns in wide tourism areas using a Wi-Fi package sensing survey, 2) Attractive destinations mining towards massive tourism area sustainable development on Wi-Fi tracking data, and 3) Assessment of the impact of snowfall on travel reliability considering different road types using ETC2.0 probe data. Hence, a stack of varying viewpoints researches provided a comprehensive review and suggestion throughout significant results. The contribution of this dissertation could be an advantage substance for strategy and policies planner to recognize alternative solutions leading to a better society.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Wind Farm Management Decision Support Systems For Short Term Horizon

Wind energy is one of the fastest growing energy sources and its technology maturity level is already higher than the majority of other renewables. Therefore, many countries started to change their financial support policies in an unfavourable way for the wind energy. This unsubsidised new era forces the wind industry to re-visit its expenditure components and to make improvements in operating strategies in order to minimise operational and maintenance (O&M) costs. The classical maintenance strategies focus on a year advanced programming of calendar based maintenance visits and corrective interventions. In this classical approach the maintenance programming flexibility is quite limited, since this kind of programming ignores dynamic environment of the wind farm and real time data-driven indicators. Then, downtimes, and corresponding revenue losses, due to wind turbine inaccessibility occur because wind turbines are exposed to challenging dynamic environmental conditions and located in remote areas. Low accessibility is one of the predominant problems, and remote control not always solves the problems. The cost optimal O&M strategies for the wind energy must consider condition based maintenance and a timely programming of wind turbine visit.Thus, an elaborate and flexible approach, which is capable of considering condition and accessibility of wind turbines using meteorological measurements and operational records is highly needed for the wind farm O&M management. The core objective of this thesis is the investigation of decision-making processes in wind farm management, and the generation of Decision Support Systems (DSSs) for O&M of wind farms. In order to develop practical and feasible DSSs, the research is conducted prioritising data-driven approaches. There still exist various inefficiently used data sources in an operational wind farm, therefore there is a room for an improvement to use efficiently available data. Generally, in a wind farm, two types of condition monitoring data can be collected as online inspection and offline inspection data. Online inspection data can be obtained from both condition monitoring system (CMS) and Supervisory Control and Data Acquisition (SCADA). CMS data require an additional investment in the turbines while, on the contrary, SCADA data are already available in the turbines. As a third source, offline inspection data consist of the records of all O&M visits to the wind farm, which are available but poorly recorded. In this study, the answer for the question of how to change a classical O&M strategy to an enhanced one using only the existing data sources without the need for an additional investment is searched.Firstly, analysis of key factors influencing in wind farm maintenance decisions is performed. In this regard, exploratory data analysis was considered to understand the monthly seasonality and the dependencies of day ahead hourly electricity market price, which is one of the decisive parameters for the wind farm revenue. Then, the connection between wind turbine failures, atmospheric variables and downtime is studied in order to provide additional information to a maintenance team and a maintenance planner for the intervention day. For the first part, well-structured and analysed electricity market price, electricity generation and demand data are needed. Therefore, the existing databases are reviewed for the case countries and a relevant analysis period is chosen. The electricity market data can be easily interpreted as time series data. To exhibit the characteristics of different electricity markets, various time series comparison tools are combined as an analysis guideline. By using this guideline, the drivers of the electricity market price are summarised for each case country. For the second part, available atmospheric and failure data for the relevant wind turbine components are gathered and combined. Then, convenient approaches among unsupervised learning models are selected. By combining the available tools and considering the needed information level for different purposes, the failure rules of prior to failure occurrence per month, in hours and in ten minutes increments are mined.Then, what-if analysis for revenue tracking of maintenance decisions is performed in order to generate a DSS for the evaluation of the major maintenance decisions taken in wind farms. To this purpose, the impact of country dynamics and subsidy frameworks considering the electricity market conditions are modelled. The impact of the intervention timing is analysed and the sensitivity of financial losses to environmental causes of underperformance are estimated.Finally, generation of decision support tool for planning of a maintenance day is studied to provide a useful maintenance DSS for in situ applications. The safe working rules considering the wind speed constraints for the accessibility to the wind turbine are reviewed taking into account the turbine manufacturer's O&M guidelines. The characteristics of the maintenance visits are summarised. Wind turbine accessibility trials using numerical weather prediction forecasting techniques for wind speed variable and synthetic forecasts for wind speed and wind gust variables are presented. An intervention decision pool considering safe working rules is generated, containing a list of plans capable of providing the optimal sequence of various tasks and ranked for revenue prioritised timing.This work has been part of the “Advanced Wind Energy Systems Operation and Maintenance Expertise" project, a European consortium with companies, universities and research centres from the wind energy sector. Parts of this work were developed in collaboration with other fellows in the project.<br /

Space-time exposure modelling of troposheric O3 in Europe

Exposure models need to be developed which can be applied at the continental scale, while still reflecting local variations in exposure conditions. Land use regression (LUR) has been widely adopted to describe the spatial variations in air pollutants over the longer term but not for short-term time-variable exposures. This study, therefore, aimed to develop and validate a space-time O3 model applicable to epidemiological studies investigating the health effects of short-term (e.g. daily) O3 exposures at the small-area scale. A geographical information system (GIS) was developed, incorporating data from 1211 O3 monitoring sites across Western Europe and a range of predictors, stored as 100m grids, including land cover, roads, topography and meteorology. The spatial model consisted of a LUR model representing the long-term average for years 2001-2007. The monitoring sites were classified, using multivariate statistical techniques, into 13 site types based on a set of descriptive indicators, then 13 temporal models represented by time functions were produced – one for each site type. These were linked to the spatial model using probability of group membership as a weighting factor. Finally, local meteorological data were incorporated to produce the full space-time model to predict daily concentrations for point locations. The spatial and temporal models were individually evaluated based on agreement with measurement data from a reserved subset of 20% of the monitoring sites. The performance of the spatial model was similar to other continental LUR models (R2=0.67; RMSE=7.64 μg/m3), while performance of the temporal models ranged from 0.3 to 0.5 (R2). Including local meteorological data into the full spatial-temporal model improved correlation with the concentrations measured at 30 monitoring sites in the Netherlands (R2= 0.42 without; R2=0.53 with meteorology). Modelling daily O3 over large areas at a fine spatial scale is possible using this approach. Overall model performance was further improved as the temporal period was aggregated to weekly or monthly. The model was applied to mothers in two birth cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE) to provide daily O3 exposure estimates, which can be aggregated as needed to provide individualised exposures based on date of birth

Footfall and the territorialisation of urban places measured through the rhythms of social activity

The UK high street is constantly changing and evolving in response to, for example, online sales, out-of-town developments, and economic crises. With over 10 years of hourly footfall counts from sensors across the UK, this study was an opportunity to perform a longitudinal and quantitative investigation to diagnose how these changes are reflected in the changing patterns of pedestrian activity. Footfall provides a recognised performance measure of place vitality. However, through a lack of data availability due to historic manual counting methods, few opportunities to contextualise the temporal patterns longitudinally have existed. This study therefore investigates daily, weekly, and annual footfall patterns, to diagnose the similarities and differences between places as social activity patterns from UK high streets evolve over time. Theoretically, footfall is conceptualised within the framework of Territorology and Assemblage Theory, conceptually underpinning a quantitative approach to represent the collective meso-level (street and town-centre) patterns of footfall (social) activity. To explore the data, the periodic signatures of daily, weekly, and annual footfall are extracted using STL (seasonal trend decomposition using Loess) algorithms and the outputs are then analysed using fuzzy clustering techniques. The analyses successfully identify daily, weekly, and annual periodic patterns and diagnose the varying social activity patterns for different urban place types and how places, both individually and collectively are changing. Footfall is demonstrated to be a performance measure of meso-scale changes in collective social activity. For place management, the fuzzy analysis provides an analytical tool to monitor the annual, weekly, and daily footfall signatures providing an evidence-based diagnostic of how places are changing over time. The place manager is therefore better able to identify place specific interventions that correspond to the usage patterns of visitors and adapt these interventions as behaviours change

Methods for analysing emerging data sources to understand variability in traveller behaviour on the road network

This thesis argues that while simplifications are a necessary part of the modelling process, there is a lack of empirical research to identify which types of variability should be included in our models, and how they should be represented. This research aims to develop methodologies to undertake empirical analyses of variability on the road network, focusing specifically on traveller behaviour. This is particularly timely given the emergence of rich new data sources. Firstly, a method is proposed for examining predictable differences in daily link flow profiles by considering both magnitude and timing. Unlike previous methods, this approach can test for statistically significant differences whilst also comparing the shapes of the profiles, by applying Functional Linear Models to transportation data for the first time. Secondly, a flexible, data-driven method is proposed for classifying road users based on their trip frequency and spatial and temporal intrapersonal variability. Previous research has proposed methodologies for identifying public transport user classes based on repeated trip behaviour, but equivalent methods for data from the road network did not exist. As there was not an established data source to use, this research examines the feasibility of using Bluetooth data. Spatial variability is examined using Sequence Alignment which has not previously been applied to Bluetooth data from road networks, nor for spatial intrapersonal variability. The time of day variability analysis adapts a technique from smart card research so that all observations are classified into travel patterns and, therefore, systematic and random variability can be measured. These network- and traveller-focused analyses are then brought together using a framework which uses them concurrently and interactively to gain additional insights into traveller behaviour. For each of the methods proposed, an application to at least one year of real world data is presented

Intraurban Spatiotemporal Variability of Ambient Air Pollutants across Metropolitan St. Louis

Ambient air monitoring networks have been established in the United States since the 1970s to comply with the Clean Air Act. The monitoring networks are primarily used to determine compliance but also provide substantive support to air quality management and air quality research including studies on health effects of air pollutants. The Roxana Air Quality Study (RAQS) was conducted at the fenceline of a petroleum refinery in Roxana, Illinois. In addition to providing insights into air pollutant impacts from the refinery, these measurements increased the St. Louis area monitoring network density for gaseous air toxics and fine particulate matter (PM2.5) speciation and thus provided an opportunity to examine intraurban spatiotemporal variability for these air quality parameters. This dissertation focused on exploring and assessing aspects of ambient air pollutant spatiotemporal variability in the St. Louis area from three progressively expanded spatial scales using a suite of methods and metrics. RAQS data were used to characterize air quality conditions in the immediate vicinity of the petroleum refinery. For example, PM2.5 lanthanoids were used to track impacts from refinery fluidized bed catalytic cracker emissions. RAQS air toxics data were interpreted by comparing to network data from the Blair Street station in the City of St. Louis which is a National Air Toxics Trends Station. Species were classified as being spatially homogeneous (similar between sites) or heterogeneous (different between sites) and in the latter case these differences were interpreted using surface winds data. For PM2.5 species, there were five concurrently operating sites in the St. Louis area - including the site in Roxana - which are either formally part of the national Chemical Speciation Network (CSN) or rigorously follow the CSN sampling and analytical protocols. This unusually large number of speciation sites for a region the size of St. Louis motivated a detailed examination of these data. Intraurban spatiotemporal variability for certain species was evaluated in the context of measurement error. For example, for species otherwise considered homogeneous, differential impacts from local point sources at different locations could be identified after comparing the observed day-to-day variations to those contributed by measurement error. In addition, source apportionment modeling was conducted using single- and multi-site datasets to assign measured PM2.5 mass to emission source categories. A suite of approaches were used to aid in the selection of an appropriate number of factors including metrics recently added to the US EPA Positive Matrix Factorization (EPA PMF) modeling software and the sensitivity of modeling results to perturbations on the measurement uncertainties