1,720,987 research outputs found
Travel time reliability
No full textTravel time and travel time reliability are important performance measures for assessing traffic condition and extent of congestion on a roadway. Most commonly used methods to obtain travel time data either produce only estimates of travel times or too few travel time data points for meaningful analysis. This study focuses on using a new probe vehicle technique, the Bluetooth technology, to collect two weeks of travel time data on Interstate-69 in Indianapolis. These data are then used to estimate econometric models, which can be used to predict freeway segment travel times. First, an autoregressive model is estimated based on the collected data. Individual vehicle travel times on a freeway segment are expressed as a function of speed, volume, time of day indicators, and previous vehicle travel times. In addition to the autoregressive formulation, a duration model is estimated based on the same travel time data. The duration model enables calculation of the probability of the vehicle exiting the segment of the road at any point in time. The estimated models indicate that the rate of vehicles exiting the segment as a function of their travel time rises initially until the inflection point and then decreases. It is hypothesized that the inflection point occurs at the onset of congestion, when longer travel time may not result in a higher probability of exiting the freeway segment. Lastly, a seemingly unrelated regression equation model to predict travel time and intervehicle variability is proposed. This model predicts 15-minute interval travel time and the standard deviation of travel time based on speed, volume and time of day indicators. The estimated model shows a good fit with the data. Furthermore, the results indicate that it is superior to the model based on point-speed estimates, which is commonly used in practice. Thus, the SURE model can be used to improve real-time travel time prediction
An exploratory analysis of temperature distributions in PCC pavements: A simultaneous equations approach
No full textThe accurate prediction of temperature profile distributions within concrete pavements is not yet reasonably within the grasp of concrete pavement engineers. This problem is of significant interest and has defied a complete and usable solution to date. The work herein explores the possibility of applying a simultaneous equations approach coupled with concepts from true environmental energy physics applications to produce a viable concrete temperature profile prediction methodology. Reasoning and functions behind the application choices of the 3SLS simultaneous equations methodology and the environmental energy balance concepts were discussed. Field data collection supporting model development was conducted and subsequently analyzed. Results revealed that the shape of the most prevalent PCC pavement profiles contained one or more changes in curvature throughout the height of the profile, negating previously held presumptions of simple linear or second degree curvatures. Profile shapes and related behaviors were explained in terms of commonly applied concepts found in the biophysics literature. Model variables were found by investigating a series of four different methodologies until the optimum combination of variables could be employed. Variables were evaluated through statistical measures and engineering judgment. Modeling results indicated that the proposed methodology produced temperature predictions throughout the profile height that were on average 97 percent correct. The exploratory analyses appear to have identified a method which is simple to employ, does not require expensive or detailed input and produces highly accurate results in a very timely manner
Infrastructure asset management: A case study on pavement rehabilitation
No full textTransportation agencies spend billions of dollars annually on managing a wide range of assets to meet public, legislative, and agency expectations. These assets vary from the physical transportation infrastructure, to equipment, material stocks, data and information, and human resources. The effectiveness of asset treatments in terms of their effect on the asset life is not well understood. This is further complicated by the effect that physical deterioration, load volumes, weather, geology, and other factors may have on the effectiveness of the treatment. Given the role that the treatments of physical transportation infrastructure play in infrastructure asset management, understanding the survivability of these treatments has the potential to provide improved resource allocation and more effective use of State funds. The present research extends the traditional infrastructure management framework by formulating methodologies that enable transportation agencies to evaluate the effectiveness of their assets\u27 treatments with respect to each treatment service life. The analysis goes beyond standard performance modeling, and demonstrates a comprehensive framework to evaluate a set of asset treatments. The end product of this research is a quantitative tool that can be used at the project development phase to estimate the effects of different types of asset treatments. To that perspective, a case study is presented, where common pavement rehabilitation treatments are evaluated for their effectiveness on pavement life for various road functional classes. The models developed in this study are calibrated using data from the Indiana Department of Transportation. First, the asset\u27s performance (the pavement performance) is forecasted and influential factors that affect performance deterioration are identified. A system of equations approach is introduced, to explicitly account for simultaneous relationships that potentially exist among performance indicators. Next, safety-based thresholds of the performance condition indicators, that initiate the asset (pavement) treatment, are estimated. A mathematical programming approach is counterpoised to a simple alternative, and both define equally effective thresholds. Finally, the previous two steps are used to approximate the service life of the asset (pavement) treatments, and by conducting random parameters hazard-based duration analysis, survival curves for each treatment are estimated. A major contribution of this work is the demonstration of a general approach that can be applied for comprehensive analysis of the effects of asset treatments, while taking into account specific characteristics of the infrastructure system. The case study results set forth herein provide a better understanding of the interrelationships among pavement rehabilitation treatment, pavement condition, road functional class, safety, pavement service life, traffic loads and trucks, weather and soil condition, and rehabilitation expenditure. Moreover, this study illustrates the steps necessary to evaluate the asset treatments effectiveness and demonstrates how analysis can be carried out and ultimately improved. Given the complexity of the problem and the limitations of available data, this study should be viewed as an incremental step toward enabling transportation agencies to make better decisions regarding a number of treatments, allowing the selection of asset treatment options that will last the longest
The effects of weather on male and female driver injury severities: A mixed logit analysis
No full textThe dependence on motor vehicle travel in the United States has led to an increase in the number of vehicle miles traveled each year and, therefore, an increase in the exposure to the risks associated with such travel. In order to significantly reduce the number of annual injuries and fatalities associated with motor vehicle crashes, there is a need for a more comprehensive understanding of the factors that are involved in these crashes. One approach to expanding the knowledge of crash safety is to focus on the role of weather in automobile crashes. Existing transportation safety research that has considered the effects of weather has been limited in scope: considering only one or two weather conditions or analyzing the effects on a macroscopic level. This research provides an interpretation of the factors that affect injury severity based upon the analysis of crashes that occurred on multiple weather-related roadway surface conditions. The effects of weather on the injury severity outcomes of drivers of passenger vehicles who were involved in single-vehicle crashes on dry, wet, and snow- or ice-covered roadway surfaces are identified in this study. The probability of injury severity was estimated using data collected from police crash reports on the state-maintained roads throughout Indiana during 2007 and 2008. Likelihood ratio tests determined that parameters were estimated differently by roadway surface condition, driver gender, and driver age. As a result, 12 separate severity models were estimated using the mixed logit model with random parameters. This research provides a unique understanding of the factors that affect driver injury severities on ideal and adverse weather-related roadway surface conditions that will be beneficial to future transportation safety programs and designs. The average predicted probabilities for each injury severity outcome were determined for each driver group and weather-related roadway surface condition. Overall, adverse weather conditions increased the severe and no injury/PDO outcome probabilities, compared to dry surface conditions. The severe injury probabilities more than doubled in adverse conditions for female drivers and male drivers 45 years old and older. This study also identifies the increased risk of severe injury that male drivers under 45 years old experienced on average in dry surface crashes, compared to adverse surface crashes. Furthermore, several findings of this study support safety awareness and enforcement efforts already in effect (such as the importance of seatbelt use regardless of the weather-related roadway surface condition), while others have indicated additional areas of safety concern for specific driver groups
Modeling unobserved heterogeneity in motor vehicle crash injury severity data
No full textThe American Association of State Highway Transportation Officials (AASHTO) has established a goal to halve the national number of highway fatalities by 2027. In order to fulfill the states\u27 portion of the goal, efforts are needed on building sophisticated crash injury data analysis methodologies for reliable safety hazards identification in development of state and local safety programs. On account of the considerable amount of unobserved and omitted on-the-spot information in crash datasets used by agencies, the issue of unobserved heterogeneity in crash data modeling has been identified and has attracted growing attention in recent years. Prior studies on relationships between highway safety elements and crash injury severity outcomes have suggested that effects of contributing factors in different situations may be non-homogenous. However, little is understood about the dynamics. This dissertation aims to contribute to the literature by (a) investigating how effects of hazardous factors vary across road segments and over time periods and (b) how they would interact with the effects of other factors on crash injury severity outcomes, with accommodation of unobserved heterogeneity in different levels and without prespecified assumptions on probability distributions. The analysis went beyond the heterogeneous effects formulation and included the model estimation details based on Bayesian inference. This dissertation consists two studies: (1) A particular case of cross-sectional unobserved heterogeneity modeling for a safety intervention program was studied by using Indiana adolescent crash data. A Markov Chain Monte Carlo (MCMC) algorithm was developed for estimation and a permutation sampler was extended for model identification. (2) A general case of time-varying unobserved heterogeneity modeling was carried out based on Indiana rural interstate crash data. Reparameterization and partially marginalized conditional samplers techniques were designed to reduce autocorrelation between consecutive draws and to improve the convergence efficiency of chains in estimation simulation. The implications for implementation of regulation enforcement and highway infrastructure upgrade and maintenance were discussed. The empirical results can provide substantial insights to government agencies that are concerned about strategic programming of safety countermeasures to leverage safety intervention resources. The methodologies set forth herein should be of interest to individuals who are developing analysis tools for crash cause diagnosis in state and local transportation safety programs, and have the potential for valuable new insights into a wide variety of questions in discrete data modeling
Economic development effects of highway investment
No full textThe evaluation of transportation projects has traditionally been carried out in the context of economic efficiency in terms of savings in travel time, vehicle operating cost, and safety. The broader long-term effects on economic development (i.e., job, income and business growth) are a concern of transportation planners and decision-makers but have been often overlooked due to the lack of a reliable impact estimation methodology and/or data. Information on economic development effects of proposed highway investments is valuable for understanding the total impact of project proposals and ensuring an efficient allocation of resources. The present research extends the traditional transportation impact framework by examining how different types of highway improvements can affect a state\u27s economy, and how project- and location-specific factors interact to stimulate economic development. The analysis went beyond accounting for user benefits and travel efficiency improvements and included additional economic development benefits in terms of business cost savings and productivity benefits. The end product of this research is a quantitative tool that can be used at the project development phase to estimate the economic development effects of different types of highway investments. The models developed in this study are calibrated using data from the Indiana Department of Transportation. A major contribution of this work is the demonstration of a general approach that can be applied for broad analysis of a highway project\u27s economic development effects at the state-level, while taking into account the intensity of highway system use. The results set forth herein provide a better understanding of the interrelationships among economic development, type of highway improvement and geographical location, and how investments in highway infrastructure can be ranked and prioritized based on sound economic development criteria. Moreover, this study illustrates the types of data necessary to document these effects and demonstrates how analysis can be carried out and ultimately improved. Given the complexity of the problem and the limitations of available data, this study should be viewed as an incremental step toward a broad analysis of the economic impacts of highway projects
Markov switching models: An application to roadway safety
No full textIn this research, two-state Markov switching models are proposed to study accident frequencies and severities. These models assume that there are two unobserved states of roadway safety, and that roadway entities (e.g., roadway segments) can switch between these states over time. The states are distinct, in the sense that in the different states accident frequencies or severities are generated by separate processes (e.g., Poisson, negative binomial, multinomial logit). Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for estimation of Markov switching models. To demonstrate the applicability of the approach, we conduct the following three studies. In the first study, two-state Markov switching count data models are considered as an alternative to zero-inflated models, in order to account for preponderance of zeros typically observed in accident frequency data. In this study, one of the states of roadway safety is a zero-accident state, which is perfectly safe. The other state is an unsafe state, in which accident frequencies can be positive and are generated by a given counting process – a Poisson or a negative binomial. Two-state Markov switching Poisson model, two-state Markov switching negative binomial model, and standard zero-inflated models are estimated for annual accident frequencies on selected Indiana interstate highway segments over a five-year time period. An important advantage of Markov switching models over zero-inflated models is that the former allow a direct statistical estimation of what states specific roadway segments are in, while the later do not. In the second study, two-state Markov switching Poisson model and two-state Markov switching negative binomial model are estimated using weekly accident frequencies on selected Indiana interstate highway segments over a five-year time period. In this study, both states of roadway safety are unsafe. Thus, accident frequencies can be positive and are generated by either Poisson or negative binomial processes in both states. It is found that the more frequent state is safer and it is correlated with better weather conditions. The less frequent state is found to be less safe and to be correlated with adverse weather conditions. In the third study, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. It is again found that the more frequent state of roadway safety is correlated with better weather conditions. The less frequent state is found to be correlated with adverse weather conditions. One of the most important results found in each of the three studies, is that in each case the estimated Markov switching models are strongly favored by accident frequency and severity data and result in a superior statistical fit, as compared to the corresponding standard (single-state) models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Improving future construction project quality through analysis of completed contract documentation.
Full text linkIn my past experience as a construction contract administrator for the Navy, I administered many types and sizes of projects and noticed that a few projects resulted in either very satisfied or very dissatisfied customers, while most received no comment. Now, with quality issues in the forefront of society, I wish to answer the following question: Can future facilities be improved by studying the contract documentation of completed projects that were considered to be high or low quality? Of course the definition of quality encompasses different things to different people, for this research project, quality was judged by a committee of nine facility engineers, the people responsible for planning and upkeep of the completed construction projects, based on how well the facility meets the required function and its durability or maintainability The contract documentation will vary somewhat between organizations and will inevitably be more extensive for public projects. For most organizations, contract documentation follows a standard formal progressing from pie-award through close-out files and includes such things as constructibility reviews, bid results, correspondence, changes, daily inspection reports, submittals, A/C visits and disputes. The objective of this research was to develop and test a method that could aid in troubleshooting an organization's construction administration process to uncover recurring problems that should be eliminated; or solutions that should be institutionalized to improve the quality of future projects The parameters used to select the contracts to be analyzed can be customized to meet the particular circumstances of the organization under study. Tins paper will discuss the methodology and results of such a case study for the Naval Air Station, Whidbey Island located near Oak Harbor, Washington.http://archive.org/details/improvingfuturec109452392
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