Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to “stated preference” methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain ‘best’ estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.

Prediction model of the buildup of volatile organic compounds on urban roads

A model to predict the buildup of mainly traffic-generated volatile organic compounds or VOCs (toluene, ethylbenzene, ortho-xylene, meta-xylene, and para-xylene) on urban road surfaces is presented. The model required three traffic parameters, namely average daily traffic (ADT), volume to capacity ratio (V/C), and surface texture depth (STD), and two chemical parameters, namely total suspended solid (TSS) and total organic carbon (TOC), as predictor variables. Principal component analysis and two phase factor analysis were performed to characterize the model calibration parameters. Traffic congestion was found to be the underlying cause of traffic-related VOC buildup on urban roads. The model calibration was optimized using orthogonal experimental design. Partial least squares regression was used for model prediction. It was found that a better optimized orthogonal design could be achieved by including the latent factors of the data matrix into the design. The model performed fairly accurately for three different land uses as well as five different particle size fractions. The relative prediction errors were 10–40% for the different size fractions and 28–40% for the different land uses while the coefficients of variation of the predicted intersite VOC concentrations were in the range of 25–45% for the different size fractions. Considering the sizes of the data matrices, these coefficients of variation were within the acceptable interlaboratory range for analytes at ppb concentration levels.

Data modelling with first-grade students

This paper argues for a renewed focus on statistical reasoning in the beginning school years, with opportunities for children to engage in data modelling. Results are reported from the first year of a 3-year longitudinal study in which three classes of first-grade children (6-year-olds) and their teachers engaged in data modelling activities. The theme of Looking after our Environment, part of the children’s science curriculum, provided the task context. The goals for the two activities addressed here included engaging children in core components of data modelling, namely, selecting attributes, structuring and representing data, identifying variation in data, and making predictions from given data. Results include the various ways in which children represented and re represented collected data, including attribute selection, and the metarepresentational competence they displayed in doing so. The “data lenses” through which the children dealt with informal inference (variation and prediction) are also reported.

Associations between young children's emotion attributions and prediction of outcome in differing social situations

Associations between young children's attributions of emotion at different points in a story, and with regard to their own prediction about the story's outcome, were investigated using two hypothetical scenarios of social and emotional challenge (social entry and negative event). First grade children (N = 250) showed an understanding that emotions are tied to situational cues by varying the emotions they attributed both between and within scenarios. Furthermore, emotions attributed to the main protagonist at the beginning of the scenarios were differentially associated with children's prediction of a positive or negative outcome and with the valence of the emotion attributed at the end of the scenario. Gender differences in responses to some items were also found. © 2010 The British Psychological Society.

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

Effects of lime content and amelioration period in double lime application on the strength of lime treated expansive sub-grade soils

The addition of lime into soils has been widely used to stabilize the expansive sub-grade soils when the road pavements are constructed on them. It is common practice to apply a half of the required lime amount and allow a certain time period for lime to react with soils (Amelioration period) before applying the rest of lime and compacting the sub-grade. The optimum amelioration period is essential to minimize the construction delay and to gain the higher strength. In this study, two different expansive soils procured from two different locations in the state of Queensland in Australia were first mixed with different lime contents. A soil mixed with a particular lime content was compacted at different amelioration periods (e.g.: 0, 6, 12, 18, 24 hrs) to obtain soil samples to measure the Unconfined Compressive Strength (UCS). The results suggested that for a given amelioration period, UCS increased with the increase in lime content. The optimum amelioration period could be within 14~17 hours for most of the lime contents in tested soils. This could suggest that the current 24-48 hour amelioration period specified by the Queensland Department of Transport and Main roads could be reduced.

Evaluation of screening tests for predicting older driver performance and safety assessed by an on-road test

A number of tests and test batteries are available for the prediction of older driver safety, but many of these have not been validated against standardized driving outcome measures. The aim of this study was to evaluate a series of previously described screening tests in terms of their ability to predict the potential for safe and unsafe driving. Participants included 79 community-dwelling older drivers (M=72.16 years, SD=5.46; range 65-88 years; 57 males and 22 females) who completed a previously validated multi-disciplinary driving assessment, a hazard perception test, a hazard change detection test and a battery of vision and cognitive tests. Participants also completed a standardized on-road driving assessment. The multi-disciplinary test battery had the highest predictive ability with a sensitivity of 80% and a specificity of 73%, followed by the hazard perception test which demonstrated a sensitivity of 75% and a specificity of 61%. These findings suggest that a relatively simple and practical battery of tests from a range of domains has the capacity to predict safe and unsafe driving in older adults.

Characterising metal build-up on urban road surfaces

Reliable approaches for predicting pollutant build-up are essential for accurate urban stormwater quality modelling. Based on the in-depth investigation of metal build-up on residential road surfaces, this paper presents empirical models for predicting metal loads on these surfaces. The study investigated metals commonly present in the urban environment. Analysis undertaken found that the build-up process for metals primarily originating from anthropogenic (copper and zinc) and geogenic (aluminium, calcium, iron and manganese) sources were different. Chromium and nickel were below detection limits. Lead was primarily associated with geogenic sources, but also exhibited a significant relationship with anthropogenic sources. The empirical prediction models developed were validated using an independent data set and found to have relative prediction errors of 12-50%, which is generally acceptable for complex systems such as urban road surfaces. Also, the predicted values were very close to the observed values and well within 95% prediction interval.

Reply to comment on “Characterising metal build-up on urban road surfaces” by Egodawatta et al. (2013)

In the recent manuscript published by Egodawatta et al. (2013), the authors investigated the build-up process of heavy metals (HMs) associated with road-deposited sediment (RDS) on residential road surfaces, and presented empirical models for the prediction of both the surface loads and build-up rates of HMs on these surfaces...

Assessment of traffic noise level before and after freeway widening using traffic microsimulation and a refined classic noise prediction method

In this paper, a refined classic noise prediction method based on the VISSIM and FHWA noise prediction model is formulated to analyze the sound level contributed by traffic on the Nanjing Lukou airport connecting freeway before and after widening. The aim of this research is to (i) assess the traffic noise impact on the Nanjing University of Aeronautics and Astronautics (NUAA) campus before and after freeway widening, (ii) compare the prediction results with field data to test the accuracy of this method, (iii) analyze the relationship between traffic characteristics and sound level. The results indicate that the mean difference between model predictions and field measurements is acceptable. The traffic composition impact study indicates that buses (including mid-sizedtrucks) and heavy goods vehicles contribute a significant proportion of total noise power despite their low traffic volume. In addition, speed analysis offers an explanation for the minor differences in noise level across time periods. Future work will aim at reducing model error, by focusing on noise barrier analysis using the FEM/BEM method and modifying the vehicle noise emission equation by conducting field experimentation.

Prediction of driving safety in individuals with homonymous hemianopia and quadrantanopia from clinical neuro-imaging

BACKGROUND: The objective of this study was to determine whether it is possible to predict driving safety in individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuro-images that are routinely available in clinical practice. METHODS: Two experienced neuro-ophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which provided information regarding the site and extent of the lesion and made predictions regarding whether they would be safe/unsafe to drive. Driving safety was defined using two independent measures: (1) The potential for safe driving was defined based upon whether the participant was rated as having the potential for safe driving, determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist conducted just prior and (2) state recorded motor vehicle crashes (all crashes and at-fault). Driving safety was independently defined at the time of the study by state recorded motor vehicle crashes (all crashes and at-fault) recorded over the previous 5 years, as well as whether the participant was rated as having the potential for safe driving, determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. RESULTS: The ability to predict driving safety was highly variable regardless of the driving outcome measure, ranging from 31% to 63% (kappa levels ranged from -0.29 to 0.04). The level of agreement between the neuro-ophthalmologists was also only fair (kappa =0.28). CONCLUSIONS: The findings suggest that clinical evaluation of summary reports currently available neuro-images by neuro-ophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

Mathematical relationships for metal build-up on urban road surfaces based on traffic and land use characteristics

The study investigated the influence of traffic and land use parameters on metal build-up on urban road surfaces. Mathematical relationships were developed to predict metals originating from fuel combustion and vehicle wear. The analysis undertaken found that nickel and chromium originate from exhaust emissions, lead, copper and zinc from vehicle wear, cadmium from both exhaust and wear and manganese from geogenic sources. Land use does not demonstrate a clear pattern in relation to the metal build-up process, though its inherent characteristics such as traffic activities exert influence. The equation derived for fuel related metal load has high cross-validated coefficient of determination (Q2) and low Standard Error of Cross-Validation (SECV) values indicates that the model is reliable, while the equation derived for wear-related metal load has low Q2 and high SECV values suggesting its use only in preliminary investigations. Relative Prediction Error values for both equations are considered to be well within the error limits for a complex system such as an urban road surface. These equations will be beneficial for developing reliable stormwater treatment strategies in urban areas which specifically focus on mitigation of metal pollution.

Fire performance of cold-formed steel wall panels and prediction of their fire resistance rating

Recent research at the Queensland University of Technology has investigated the structural and thermal behaviour of load bearing Light gauge Steel Frame (LSF) wall systems made of 1.15 mm G500 steel studs and varying plasterboard and insulation configurations (cavity and external insulation) using full scale fire tests. Suitable finite element models of LSF walls were then developed and validated by comparing with test results. In this study, the validated finite element models of LSF wall panels subject to standard fire conditions were used in a detailed parametric study to investigate the effects of important parameters such as steel grade and thickness, plasterboard screw spacing, plasterboard lateral restraint, insulation materials and load ratio on their performance under standard fire conditions. Suitable equations were proposed to predict the time–temperature profiles of LSF wall studs with eight different plasterboard-insulation configurations, and used in the finite element analyses. Finite element parametric studies produced extensive fire performance data for the LSF wall panels in the form of load ratio versus time and critical hot flange (failure) temperature curves for eight wall configurations. This data demonstrated the superior fire performance of externally insulated LSF wall panels made of different steel grades and thicknesses. It also led to the development of a set of equations to predict the important relationship between the load ratio and the critical hot flange temperature of LSF wall studs. Finally this paper proposes a simplified method to predict the fire resistance rating of LSF walls based on the two proposed set of equations for the load ratio–hot flange temperature and the time–temperature relationships.

Investigations on road noise level spatial variability within a specially designed acoustic balcony

An investigation into the spatial distribution of road traffic noise levels on a balcony is conducted. A balcony constructed to a special acoustic design due to its elevation above an 8 lane motorway is selected for detailed measurements. The as-constructed balcony design includes solid parapets, side walls, ceiling shields and highly absorptive material placed on the ceiling. Road traffic noise measurements are conducted spatially using a five channel acoustic analyzer, where four microphones are located at various positions within the balcony space and one microphone placed outside the parapet at a reference position. Spatial distributions in both vertical and horizontal planes are measured. A theoretical model and prediction configuration is presented that assesses the acoustic performance of the balcony under existing traffic flow conditions. The prediction model implements a combined direct path, specular reflection path and diffuse reflection path utilizing image source and radiosity techniques. Results obtained from the prediction model are presented and compared to the measurement results. The predictions are found to correlate well with measurements with some minor differences that are explained. It is determined that the prediction methodology is acceptable to assess a wider range of street and balcony configuration scenarios.