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.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Classification with a reject option using a hinge loss

We consider the problem of binary classification where the classifier can, for a particular cost, choose not to classify an observation. Just as in the conventional classification problem, minimization of the sample average of the cost is a difficult optimization problem. As an alternative, we propose the optimization of a certain convex loss function φ, analogous to the hinge loss used in support vector machines (SVMs). Its convexity ensures that the sample average of this surrogate loss can be efficiently minimized. We study its statistical properties. We show that minimizing the expected surrogate loss—the φ-risk—also minimizes the risk. We also study the rate at which the φ-risk approaches its minimum value. We show that fast rates are possible when the conditional probability P(Y=1|X) is unlikely to be close to certain critical values.

Utilisation of solar energy for building operation

In face of the increasing concern on global warming and climate change, the interests in the utilization of solar energy for building operation are also rapidly growing. In this paper, the importance of using renewable energy in building operations is first discussed. The potential use of solar energy is then reviewed. Possible applications of solar energy in building operation are also discussed, including the use of solar energy in the forms of daylighting, hot water heating, space heating and cooling and building-integrated photovoltaics. Finally, the research activities in the utilization of solar energy for space cooling at QUT are highlighted.

Community response to disasters in Indonesia : Gotong Royong; a double edged-sword

Indonesia is a country spread across wide-ranging archipelago, located in South East Asia between two oceans, the Indian and the Pacific. Indonesia is well known as an active tectonic region because it lies on top of three major active tectonic plates: the Eurasian in the North, the Indian Ocean-Australian in the South, and the Pacific plate in the East. The southern and eastern part of the country features a range of volcanic arcs, volcanic mountains, and lowlands with 500 young volcanoes, of which 128 are active and thus representing 15% of the world’s active volcanoes. In the period 2002-2007, approximately 1782 disasters occurred, with hundreds of thousands of lives lost and billions of rupiah in losses incurred: (Floods - 1183 instances, cyclones - 272 instances, and landslides - 252 instances). Of these, the 2004 Aceh tsunami and the 2006 central Java earthquake (impacting predominantly city and suburbs of Yogyakarta) were the most significant. Even so, disaster management experts believe lessons learnt from the two major natural disasters needs to be formalised into laws and institutions before another disaster occurs, regardless of the type of natural disaster – i.e. Volcano eruption or landslide; as opposed to tsunami or earthquake. Following in the wake of disasters occurring in Yogyakarta, many of its community members responded by banding together as one, with the determination of rebuilding its villages and cities through the spirit of ‘gotong royong’. The idea of social interaction; in particular as a collective, consensual, and cooperative nation; has predominantly formed the ideological basis of Indonesia’s societal nature. Many Indonesian terms cohere to this ideology, such as: ‘koperasi” (cooperatives as the basis of economic interactions), ‘musyawarah’ (consensual nature in decision making), and ‘gotong royong’ (mutual assistance). ‘Gotong royong’ has become a key cultural operator in Indonesia, in particular In Jogjakarta. Appropriately so as ‘gotong royong’ is depicted from the traditional Javanese village, where labour is accomplished through reciprocal exchange and the villagers are motivated by a general ethos of selfishness and concern for the common good. The culture of ‘gotong royong’ promotes positive values such as social harmony and mutual reciprocation in disaster-affected areas provides the necessary spirit needed to endure the hardships and for all involved. While gotong royong emphasises the positive notions of mutual family support and deep community level activity there is a potential for contrast against government lead disaster response and recovery management activities especially in settings where sporadic governance mechanisms exist and transparency and accountability in the recovery process of public infrastructure assets have been questioned. This paper thus questions whether Gotong Royong is a double-edged sword, and explores the potential marriage of community values and governance mechanisms for future disaster management planning and practice.