Industrial research laboratories of the United States, including consulting research laboratories.

Other slight variations in title.

Iowa Highway Research Report No. 2, 1950-1951

During the fiscal year, July 1, 1950 to June 30, 1951, the Iowa State Highway Commission expanded its research program by adding to the work under way during the previous fiscal year, fifteen substantial projects recommended to the Commission by the Iowa Highway Research Board. The major portion of the projects under way in the previous fiscal year have been active in this fiscal year. All of the additional projects have gotten under way during this fiscal year. This year, as in the past, the work has been performed partly by Commission forces and partly by outside agencies such as the Institute of Hydraulic Research at Iowa City, the State University of Iowa, Iowa State College, the Iowa Geological Survey and the United States Geological Survey. The major portion of the fifteen new projects has been assigned to these outside agencies.

Research training and national innovation systems in Australia, Finland and the United States : a policy and systems study supported by 30 case studies of research students in the fields of geospatial science, wireless communication, biosciences, and materials science and engineering

Reforms to the national research and research training system by the Commonwealth Government of Australia sought to effectively connect research conducted in universities to Australia's national innovation system. Research training has a key role in ensuring an adequate supply of highly skilled people for the national innovation system. During their studies, research students produce and disseminate a massive amount of new knowledge. Prior to this study, there was no research that examined the contribution of research training to Australia's national innovation system despite the existence of policy initiatives aiming to enhance this contribution. Given Australia's below average (but improving) innovation performance compared to other OECD countries, the inclusion of Finland and the United States provided further insights into the key research question. This study examined three obvious ways that research training contributes to the national innovation systems in the three countries: the international mobility and migration of research students and graduates, knowledge production and distribution by research students, and the impact of research training as advanced human capital formation on economic growth. Findings have informed the concept of a research training culture of innovation that aims to enhance the contribution of research training to Australia's national innovation system. Key features include internationally competitive research and research training environments; research training programs that equip students with economically-relevant knowledge and the capabilities required by employers operating in knowledge-based economies; attractive research careers in different sectors; a national commitment to R&D as indicated by high levels of gross and business R&D expenditure; high private and social rates of return from research training; and the horizontal coordination of key organisations that create policy for, and/or invest in research training.

Estimation of travel time on urban networks with midlink sources and sinks

This paper presents a methodology for estimation of average travel time on signalized urban networks by integrating cumulative plots and probe data. This integration aims to reduce the relative deviations in the cumulative plots due to midlink sources and sinks. During undersaturated traffic conditions, the concept of a virtual probe is introduced, and therefore, accurate travel time can be obtained when a real probe is unavailable. For oversaturated traffic conditions, only one probe per travel time estimation interval—360 s or 3% of vehicles traversing the link as a probe—has the potential to provide accurate travel time.

Empirical investigation of interactive highway safety design model accident prediction algorithm : Rural intersections

One major gap in transportation system safety management is the ability to assess the safety ramifications of design changes for both new road projects and modifications to existing roads. To fulfill this need, FHWA and its many partners are developing a safety forecasting tool, the Interactive Highway Safety Design Model (IHSDM). The tool will be used by roadway design engineers, safety analysts, and planners throughout the United States. As such, the statistical models embedded in IHSDM will need to be able to forecast safety impacts under a wide range of roadway configurations and environmental conditions for a wide range of driver populations and will need to be able to capture elements of driving risk across states. One of the IHSDM algorithms developed by FHWA and its contractors is for forecasting accidents on rural road segments and rural intersections. The methodological approach is to use predictive models for specific base conditions, with traffic volume information as the sole explanatory variable for crashes, and then to apply regional or state calibration factors and accident modification factors (AMFs) to estimate the impact on accidents of geometric characteristics that differ from the base model conditions. In the majority of past approaches, AMFs are derived from parameter estimates associated with the explanatory variables. A recent study for FHWA used a multistate database to examine in detail the use of the algorithm with the base model-AMF approach and explored alternative base model forms as well as the use of full models that included nontraffic-related variables and other approaches to estimate AMFs. That research effort is reported. The results support the IHSDM methodology.

Conducting statistical tests of hypotheses : five common misconceptions found in transportation research

Most statistical methods use hypothesis testing. Analysis of variance, regression, discrete choice models, contingency tables, and other analysis methods commonly used in transportation research share hypothesis testing as the means of making inferences about the population of interest. Despite the fact that hypothesis testing has been a cornerstone of empirical research for many years, various aspects of hypothesis tests commonly are incorrectly applied, misinterpreted, and ignored—by novices and expert researchers alike. On initial glance, hypothesis testing appears straightforward: develop the null and alternative hypotheses, compute the test statistic to compare to a standard distribution, estimate the probability of rejecting the null hypothesis, and then make claims about the importance of the finding. This is an oversimplification of the process of hypothesis testing. Hypothesis testing as applied in empirical research is examined here. The reader is assumed to have a basic knowledge of the role of hypothesis testing in various statistical methods. Through the use of an example, the mechanics of hypothesis testing is first reviewed. Then, five precautions surrounding the use and interpretation of hypothesis tests are developed; examples of each are provided to demonstrate how errors are made, and solutions are identified so similar errors can be avoided. Remedies are provided for common errors, and conclusions are drawn on how to use the results of this paper to improve the conduct of empirical research in transportation.

Property damage crash equivalency factors to solve crash frequency-severity dilemma: Case study on South Korean rural roads

Safety interventions (e.g., median barriers, photo enforcement) and road features (e.g., median type and width) can influence crash severity, crash frequency, or both. Both dimensions—crash frequency and crash severity—are needed to obtain a full accounting of road safety. Extensive literature and common sense both dictate that crashes are not created equal, with fatalities costing society more than 1,000 times the cost of property damage crashes on average. Despite this glaring disparity, the profession has not unanimously embraced or successfully defended a nonarbitrary severity weighting approach for analyzing safety data and conducting safety analyses. It is argued here that the two dimensions (frequency and severity) are made available by intelligently and reliably weighting crash frequencies and converting all crashes to property-damage-only crash equivalents (PDOEs) by using comprehensive societal unit crash costs. This approach is analogous to calculating axle load equivalents in the prediction of pavement damage: for instance, a 40,000-lb truck causes 4,025 times more stress than does a 4,000-lb car and so simply counting axles is not sufficient. Calculating PDOEs using unit crash costs is the most defensible and nonarbitrary weighting scheme, allows for the simple incorporation of severity and frequency, and leads to crash models that are sensitive to factors that affect crash severity. Moreover, using PDOEs diminishes the errors introduced by underreporting of less severe crashes—an added benefit of the PDOE analysis approach. The method is illustrated with rural road segment data from South Korea (which in practice would develop PDOEs with Korean crash cost data).

New criteria for evaluating methods of identifying hot spots

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria—namely false positives and false negatives—are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

Forecasting crashes at the planning level: Simultaneous negative binomial crash model applied in Tucson, Arizona

At least two important transportation planning activities rely on planning-level crash prediction models. One is motivated by the Transportation Equity Act for the 21st Century, which requires departments of transportation and metropolitan planning organizations to consider safety explicitly in the transportation planning process. The second could arise from a need for state agencies to establish incentive programs to reduce injuries and save lives. Both applications require a forecast of safety for a future period. Planning-level crash prediction models for the Tucson, Arizona, metropolitan region are presented to demonstrate the feasibility of such models. Data were separated into fatal, injury, and property-damage crashes. To accommodate overdispersion in the data, negative binomial regression models were applied. To accommodate the simultaneity of fatality and injury crash outcomes, simultaneous estimation of the models was conducted. All models produce crash forecasts at the traffic analysis zone level. Statistically significant (p-values < 0.05) and theoretically meaningful variables for the fatal crash model included population density, persons 17 years old or younger as a percentage of the total population, and intersection density. Significant variables for the injury and property-damage crash models were population density, number of employees, intersections density, percentage of miles of principal arterial, percentage of miles of minor arterials, and percentage of miles of urban collectors. Among several conclusions it is suggested that planning-level safety models are feasible and may play a role in future planning activities. However, caution must be exercised with such models.

Effects of transportation accessibility on residential property values: Application of spatial hedonic price model in Seoul, South Korea, metropolitan area

A number of studies have focused on estimating the effects of accessibility on housing values by using the hedonic price model. In the majority of studies, estimation results have revealed that housing values increase as accessibility improves, although the magnitude of estimates has varied across studies. Adequately estimating the relationship between transportation accessibility and housing values is challenging for at least two reasons. First, the monocentric city assumption applied in location theory is no longer valid for many large or growing cities. Second, rather than being randomly distributed in space, housing values are clustered in space—often exhibiting spatial dependence. Recognizing these challenges, a study was undertaken to develop a spatial lag hedonic price model in the Seoul, South Korea, metropolitan region, which includes a measure of local accessibility as well as systemwide accessibility, in addition to other model covariates. Although the accessibility measures can be improved, the modeling results suggest that the spatial interactions of apartment sales prices occur across and within traffic analysis zones, and the sales prices for apartment communities are devalued as accessibility deteriorates. Consistent with findings in other cities, this study revealed that the distance to the central business district is still a significant determinant of sales price.

Expected safety performance of rural signalized intersections in South Korea

Understanding the expected safety performance of rural signalized intersections is critical for (a) identifying high-risk sites where the observed safety performance is substantially worse than the expected safety performance, (b) understanding influential factors associated with crashes, and (c) predicting the future performance of sites and helping plan safety-enhancing activities. These three critical activities are routinely conducted for safety management and planning purposes in jurisdictions throughout the United States and around the world. This paper aims to develop baseline expected safety performance functions of rural signalized intersections in South Korea, which to date have not yet been established or reported in the literature. Data are examined from numerous locations within South Korea for both three-legged and four-legged configurations. The safety effects of a host of operational and geometric variables on the safety performance of these sites are also examined. In addition, supplementary tables and graphs are developed for comparing the baseline safety performance of sites with various geometric and operational features. These graphs identify how various factors are associated with safety. The expected safety prediction tables offer advantages over regression prediction equations by allowing the safety manager to isolate specific features of the intersections and examine their impact on expected safety. The examination of the expected safety performance tables through illustrated examples highlights the need to correct for regression-to-the-mean effects, emphasizes the negative impacts of multicollinearity, shows why multivariate models do not translate well to accident modification factors, and illuminates the need to examine road safety carefully and methodically. Caveats are provided on the use of the safety performance prediction graphs developed in this paper.

Bayesian multinomial logit: Theory and route choice example

Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL) model. MNL models are frequently applied in transportation research to model choice outcomes such as mode, destination, or route choices or to model categorical outcomes such as crash outcomes. Recent developments allow for the modification of the potentially limiting assumptions of MNL such as the independence from irrelevant alternatives (IIA) property. However, relatively little transportation-related research has focused on Bayesian MNL models, the tractability of which is of great value to researchers and practitioners alike. This paper addresses MNL model specification issues in the Bayesian framework, such as the value of including prior information on parameters, allowing for nonlinear covariate effects, and extensions to random parameter models, so changing the usual limiting IIA assumption. This paper also provides an example that demonstrates, using route-choice data, the considerable potential of the Bayesian MNL approach with many transportation applications. This paper then concludes with a discussion of the pros and cons of this Bayesian approach and identifies when its application is worthwhile

How do level of experience, purpose for riding, and preference for facilities affect location of riding?

Characteristics of the road infrastructure affect both the popularity of bicycling and its safety, but comparisons of the safety performance of infrastructure may be confounded by differences in the profiles of cyclists who use them. Data from a survey of 2,532 adult bicycle riders in Queensland, Australia, demonstrated that many riders rode reluctantly in particular locations and that preference for riding location was influenced by degree of experience and riding purpose. Most riders rode most often and furthest per week on urban roads, but approximately one-third of all riders (and more new riders) rode there reluctantly. Almost two-thirds of riders rode on bicycle paths, most by choice, not reluctantly. New riders rode proportionally more on bicycle paths, but continuing riders rode further in absolute terms. Utilitarian riders were more likely to ride on bicycle paths than social and fitness riders and almost all of this riding was by choice. Fitness riders were more reluctant in their use of bicycle paths, but still most of their use was by choice. One-third of the respondents reported riding on the sidewalk (legal in Queensland), with approximately two-thirds doing so reluctantly. The frequency and distance ridden on the sidewalk was less than for urban roads and bicycle paths. Sidewalks and bicycle paths were important facilities for both inexperienced and experienced riders and for utilitarian riding, especially when urban roads were considered a poor choice for cycling.

Sustainable urban transport : smart technology initiatives in Singapore

Achieving sustainability is one of the major goals of many urban transportation systems. Over the years, many innovative policies have been attempted to achieve an efficient, safe, and sustainable transport system. Those policies often require smart technologies to assist implementation process and enhance effectiveness. This paper discusses how sustainability can be promoted by embedding smart technologies in a modern transportation system. In particular, this paper studies the transport system of Singapore to address how this system is addressing sustainability through the use of smart technologies. Various technological initiatives in managing traffic flow, monitoring and enforcement, sharing real-time information, and managing revenues are discussed in light of their potentiality in addressing sustainability issues. The Singapore experience provides a useful reference for the cities intending to develop and promote a sustainable transport system.