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  • 1.
    Svenson, Kristin
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A Microdata Analysis Approach to Transport Infrastructure Maintenance2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy.

    This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance.

    Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies.

    In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.

  • 2.
    Svenson, Kristin
    Dalarna University, School of Technology and Business Studies, Statistics.
    Estimated lifetimes of road pavements in Sweden using time-to-event analysis2014In: Journal of transportation engineering, ISSN 0733-947X, E-ISSN 1943-5436, Vol. 140, no 11, 04014056Article in journal (Refereed)
    Abstract [en]

    Maintenance planning of road pavement requires reliable estimates of roads' lifetimes. In determining the lifetime of a road, this study combines maintenance activities and road condition measurements. The scope of the paper is to estimate lifetimes of road pavements in Sweden with time-to-event analysis. The model is stratified according to traffic load and includes effects of pavement type, road type, bearing capacity, road width, speed limit, stone size, and climate zone. Among the nine analyzed pavement types, stone mastic had the longest expected lifetime with a hazard ratio (risk of needing maintenance) estimated to be 36% lower than asphalt concrete. Among road types, 2+1 roads had 22% higher hazard ratio than ordinary roads indicating significantly lower lifetimes. Increased speed lowered the lifetime, while increased stone size (up to 20 mm) and increased road width lengthened the lifetime. The results are of importance for life-cycle cost analysis and road management. (C) 2014 American Society of Civil Engineers.

  • 3.
    Svenson, Kristin
    Dalarna University, School of Technology and Business Studies, Statistics.
    Estimated lifetimes of road pavements in Sweden using time-to-event analysis2013Report (Other academic)
    Abstract [en]

    Maintenance planning of road pavement requires reliable estimates of roads’ lifetimes. In determining the lifetime of a road, this study combines maintenance activities and road condition measurements. The scope of the paper is to estimate lifetimes of road pavements in Sweden with time to event analysis. The model used includes effects of pavement type, road type, bearing capacity, road width, speed limit, stone size and climate zone, where the model is stratified according to traffic load. Among the nine analyzed pavement types, stone mastic had the longest expected lifetime, 32 percent longer than asphalt concrete. Among road types, ordinary roads with cable barriers had 30 percent shorter lifetime than ordinary roads. Increased speed lowered the lifetime, while increased stone size (up to 20 mm) and increased road width lengthened the lifetime. The results are of importance for life cycle cost analysis and road management.

  • 4.
    Svenson, Kristin
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Statistics.
    Macuchova, Zuzana
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Evaluating needs of road maintenance in Sweden with the mixed proportional hazards model2016In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, no 2589, 51-58 p.Article in journal (Refereed)
    Abstract [en]

    National road databases often lack important information for long-term maintenance planning of paved roads. In the Swedish case, latent variables of which there are no recordings in the pavement management systems database are, for example, underlying road construction, subsoil conditions, and amount of heavy traffic measured by the equivalent single-axle load. The mixed proportional hazards model with random effects was used to capture the effect of these latent variables on a road's risk of needing maintenance. Estimation of random effects makes it possible to identify sections that have shorter or longer lifetimes than could be expected from the observed explanatory variables (traffic load, pavement type, road type, climate zone, road width, speed limit, and bearing capacity restrictions). The results indicate that the mixed proportional hazards model is useful for maintenance planning because the weakest and strongest sections in a road network can be identified. The effect of the latent variables was visualized by,plotting the random effect of each section in a map of the road network. In addition, the spatial correlation between road sections was evaluated by fitting the random effects in an intrinsic conditional autoregressive model. The spatial correlation was estimated to explain 17% of the variation in lifetimes of roads that occur because of the latent variables. The Swedish example shows that the mixed proportional hazards and intrinsic conditional autoregressive models are suitable for analyzing the effect of latent variables in national road databases.

  • 5.
    Svenson, Kristin
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    McRobbie, S.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Detecting road pavement deterioration with finite mixture models2017In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, 1-8 p.Article in journal (Refereed)
    Abstract [en]

    Budget restrictions often limit the number of possible maintenance activities in a road network each year. To effectively allocate resources, the rate of road pavement deterioration is of great importance. If two maintenance candidates have an equivalent condition, it is reasonable to maintain the segment with the highest deterioration rate first. To identify such segments, finite mixture models were applied to road condition data from a part of the M4 highway in England. Assuming that data originates from two different normal distributions – defined as a ‘change’ distribution and an ‘unchanged’ distribution – all road segments were classified into one of the groups. Comparisons with known measurement errors and maintenance records showed that segments in the unchanged group had a stationary road condition. Segments classified into the change group showed either a rapid deterioration, improvement in condition because of previous maintenance or unusual measurement errors. Together with additional information from maintenance records, finite mixture models can identify segments with the most rapid deterioration rate, and contribute to more efficient maintenance decisions.

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