Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

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.

Prediction of pull-out force of multi-walled carbon nanotube (MWCNT) in sword-in-sheath mode

The pull-out force of some outer walls against other inner walls in multi-walled carbon nanotubes (MWCNTs) was systematically studied by molecular mechanics simulations. The obtained results reveal that the pull-out force is proportional to the square of the diameter of the immediate outer wall on the sliding interface, which highlights the primary contribution of the capped section of MWCNT to the pull-out force. A simple empirical formula was proposed based on the numerical results to predict the pull-out force for an arbitrary pull-out in a given MWCNT directly from the diameter of the immediate outer wall on the sliding interface. Moreover, tensile tests for MWCNTs with and without acid-treatment were performed with a nanomanipulator inside a vacuum chamber of a scanning electron microscope (SEM) to validate the present empirical formula. It was found that the theoretical pull-out forces agree with the present and some previous experimental results very well.

The increased popularity of mopeds and motor scooters : exploring usage patterns and safety outcomes

Increased use of powered two-wheelers (PTWs) often underlies increases in the number of reported crashes, promoting research into PTW safety. PTW riders are overrepresented in crash and injury statistics relative to exposure and, as such, are considered vulnerable road users. PTW use has increased substantially over the last decade in many developed countries. One such country is Australia, where moped and scooter use has increased at a faster rate than motorcycle use in recent years. Increased moped use is particularly evident in the State of Queensland which is one of four Australian jurisdictions where moped riding is permitted for car licence holders and a motorcycle licence is not required. A moped is commonly a small motor scooter and is limited to a maximum design speed of 50 km/h and a maximum engine cylinder capacity of 50 cubic centimetres. Scooters exceeding either of these specifications are classed as motorcycles in all Australian jurisdictions. While an extensive body of knowledge exists on motorcycle safety, some of which is relevant to moped and scooter safety, the latter PTW types have received comparatively little focused research attention. Much of the research on moped safety to date has been conducted in Europe where they have been popular since the mid 20th century, while some studies have also been conducted in the United States. This research is of limited relevance to Australia due to socio-cultural, economic, regulatory and environmental differences. Moreover, while some studies have compared motorcycles to mopeds in terms of safety, no research to date has specifically examined the differences and similarities between mopeds and larger scooters, or between larger scooters and motorcycles. To address the need for a better understanding of moped and scooter use and safety, the current program of research involved three complementary studies designed to achieve the following aims: (1) develop better knowledge and understanding of moped and scooter usage trends and patterns; and (2) determine the factors leading to differences in moped, scooter and motorcycle safety. Study 1 involved six-monthly observations of PTW types in inner city parking areas of Queensland’s capital city, Brisbane, to monitor and quantify the types of PTW in use over a two year period. Study 2 involved an analysis of Queensland PTW crash and registration data, primarily comparing the police-reported crash involvement of mopeds, scooters and motorcycles over a five year period (N = 7,347). Study 3 employed both qualitative and quantitative methods to examine moped and scooter usage in two components: (a) four focus group discussions with Brisbane-based Queensland moped and scooter riders (N = 23); and (b) a state-wide survey of Queensland moped and scooter riders (N = 192). Study 1 found that of the PTW types parked in inner city Brisbane over the study period (N = 2,642), more than one third (36.1%) were mopeds or larger scooters. The number of PTWs observed increased at each six-monthly phase, but there were no significant changes in the proportions of PTW types observed across study phases. There were no significant differences in the proportions or numbers of PTW type observed by season. Study 2 revealed some important differences between mopeds, scooters and motorcycles in terms of safety and usage through analysis of crash and registration data. All Queensland PTW registrations doubled between 2001 and 2009, but there was an almost fifteen-fold increase in moped registrations. Mopeds subsequently increased as a proportion of Queensland registered PTWs from 1.2 percent to 8.8 percent over this nine year period. Moped and scooter crashes increased at a faster rate than motorcycle crashes over the five year study period from July 2003 to June 2008, reflecting their relatively greater increased usage. Crash rates per 10,000 registrations for the study period were only slightly higher for mopeds (133.4) than for motorcycles and scooters combined (124.8), but estimated crash rates per million vehicle kilometres travelled were higher for mopeds (6.3) than motorcycles and scooters (1.7). While the number of crashes increased for each PTW type over the study period, the rate of crashes per 10,000 registrations declined by 40 percent for mopeds compared with 22 percent for motorcycles and scooters combined. Moped and scooter crashes were generally less severe than motorcycle crashes and this was related to the particular crash characteristics of the PTW types rather than to the PTW types themselves. Compared to motorcycle and moped crashes, scooter crashes were less likely to be single vehicle crashes, to involve a speeding or impaired rider, to involve poor road conditions, or to be attributed to rider error. Scooter and moped crashes were more likely than motorcycle crashes to occur on weekdays, in lower speed zones and at intersections. Scooter riders were older on average (39) than moped (32) and motorcycle (35) riders, while moped riders were more likely to be female (36%) than scooter (22%) or motorcycle riders (7%). The licence characteristics of scooter and motorcycle riders were similar, with moped riders more likely to be licensed outside of Queensland and less likely to hold a full or open licence. The PTW type could not be identified in 15 percent of all cases, indicating a need for more complete recording of vehicle details in the registration data. The focus groups in Study 3a and the survey in Study 3b suggested that moped and scooter riders are a heterogeneous population in terms of demographic characteristics, riding experience, and knowledge and attitudes regarding safety and risk. The self-reported crash involvement of Study 3b respondents suggests that most moped and scooter crashes result in no injury or minor injury and are not reported to police. Study 3 provided some explanation for differences observed in Study 2 between mopeds and scooters in terms of crash involvement. On the whole, scooter riders were older, more experienced, more likely to have undertaken rider training and to value rider training programs. Scooter riders were also more likely to use protective clothing and to seek out safety-related information. This research has some important practical implications regarding moped and scooter use and safety. While mopeds and scooters are generally similar in terms of usage, and their usage has increased, scooter riders appear to be safer than moped riders due to some combination of superior skills and safer riding behaviour. It is reasonable to expect that mopeds and scooters will remain popular in Queensland in future and that their usage may further increase, along with that of motorcycles. Future policy and planning should consider potential options for encouraging moped riders to acquire better riding skills and greater safety awareness. While rider training and licensing appears an obvious potential countermeasure, the effectiveness of rider training has not been established and other options should also be strongly considered. Such options might include rider education and safety promotion, while interventions could also target other road users and urban infrastructure. Future research is warranted in regard to moped and scooter safety, particularly where the use of those PTWs has increased substantially from low levels. Research could address areas such as rider training and licensing (including program evaluations), the need for more detailed and reliable data (particularly crash and exposure data), protective clothing use, risks associated with lane splitting and filtering, and tourist use of mopeds. Some of this research would likely be relevant to motorcycle use and safety, as well as that of mopeds and scooters.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

The relationship between Bayesian motor unit number estimation and histological measurements of motor neurons in wild-type and SOD1 G93A mice

Objective: To assess the relationship between Bayesian MUNE and histological motor neuron counts in wild-type mice and in an animal model of ALS. Methods: We performed Bayesian MUNE paired with histological counts of motor neurons in the lumbar spinal cord of wild-type mice and transgenic SOD1 G93A mice that show progressive weakness over time. We evaluated the number of acetylcholine endplates that were innervated by a presynaptic nerve. Results: In wild-type mice, the motor unit number in the gastrocnemius muscle estimated by Bayesian MUNE was approximately half the number of motor neurons in the region of the spinal cord that contains the cell bodies of the motor neurons supplying the hindlimb crural flexor muscles. In SOD1 G93A mice, motor neuron numbers declined over time. This was associated with motor endplate denervation at the end-stage of disease. Conclusion: The number of motor neurons in the spinal cord of wild-type mice is proportional to the number of motor units estimated by Bayesian MUNE. In SOD1 G93A mice, there is a lower number of estimated motor units compared to the number of spinal cord motor neurons at the end-stage of disease, and this is associated with disruption of the neuromuscular junction. Significance: Our finding that the Bayesian MUNE method gives estimates of motor unit numbers that are proportional to the numbers of motor neurons in the spinal cord supports the clinical use of Bayesian MUNE in monitoring motor unit loss in ALS patients. © 2012 International Federation of Clinical Neurophysiology.

Quantitative studies of lower motor neuron degeneration in amyotrophic lateral sclerosis : Evidence for exponential decay of motor unit numbers and greatest rate of loss at the site of onset

Objective: To use our Bayesian method of motor unit number estimation (MUNE) to evaluate lower motor neuron degeneration in ALS. Methods: In subjects with ALS we performed serial MUNE studies. We examined the repeatability of the test and then determined whether the loss of MUs was fitted by an exponential or Weibull distribution. Results: The decline in motor unit (MU) numbers was well-fitted by an exponential decay curve. We calculated the half life of MUs in the abductor digiti minimi (ADM), abductor pollicis brevis (APB) and/or extensor digitorum brevis (EDB) muscles. The mean half life of the MUs of ADM muscle was greater than those of the APB or EDB muscles. The half-life of MUs was less in the ADM muscle of subjects with upper limb than in those with lower limb onset. Conclusions: The rate of loss of lower motor neurons in ALS is exponential, the motor units of the APB decay more quickly than those of the ADM muscle and the rate of loss of motor units is greater at the site of onset of disease. Significance: This shows that the Bayesian MUNE method is useful in following the course and exploring the clinical features of ALS. 2012 International Federation of Clinical Neurophysiology.

'It ain't over till it's over' : Yogi Berra bias on prediction markets

The recent expansion of prediction markets provides a great opportunity to test the market efficiency hypothesis and the calibration of trader judgements. Using a large database of observed prices, this article studies the calibration of prediction markets prices on sporting events using both nonparametric and parametric methods. While only minor bias can be observed during most of the lifetime of the contracts, the calibration of prices deteriorates very significantly in the last moments of the contracts’ lives. Traders tend to overestimate the probability of the losing team to reverse the situation in the last minutes of the game.

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).