Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Development of accident prediction models for rural highway intersections

A study was done to develop macrolevel crash prediction models that can be used to understand and identify effective countermeasures for improving signalized highway intersections and multilane stop-controlled highway intersections in rural areas. Poisson and negative binomial regression models were fit to intersection crash data from Georgia, California, and Michigan. To assess the suitability of the models, several goodness-of-fit measures were computed. The statistical models were then used to shed light on the relationships between crash occurrence and traffic and geometric features of the rural signalized intersections. The results revealed that traffic flow variables significantly affected the overall safety performance of the intersections regardless of intersection type and that the geometric features of intersections varied across intersection type and also influenced crash type.

Further notes on the application of zero-inflated models in highway safety

The intent of this note is to succinctly articulate additional points that were not provided in the original paper (Lord et al., 2005) and to help clarify a collective reluctance to adopt zero-inflated (ZI) models for modeling highway safety data. A dialogue on this important issue, just one of many important safety modeling issues, is healthy discourse on the path towards improved safety modeling. This note first provides a summary of prior findings and conclusions of the original paper. It then presents two critical and relevant issues: the maximizing statistical fit fallacy and logic problems with the ZI model in highway safety modeling. Finally, we provide brief conclusions.

Scheduling coordination through negotiation between train service providers in an open railway access market

A schedule coordination problem involving two train services provided by different operators is modeled as an optimization of revenue intake. The coordination is achieved through the adjustment of commencement times of the train services by negotiation. The problem is subject to constraints regarding to passenger demands and idle costs of rolling-stocks from both operators. This paper models the operators as software agents having the flexibility to incorporate one of the two (and potentially more) proposed negotiation strategies. Empirical results show that agents employing different combination of strategies have significant impact on the quality of solution and negotiation time.

Limited information estimation and evaluation of DSGE models

We advance the proposition that dynamic stochastic general equilibrium (DSGE) models should not only be estimated and evaluated with full information methods. These require that the complete system of equations be specified properly. Some limited information analysis, which focuses upon specific equations, is therefore likely to be a useful complement to full system analysis. Two major problems occur when implementing limited information methods. These are the presence of forward-looking expectations in the system as well as unobservable non-stationary variables. We present methods for dealing with both of these difficulties, and illustrate the interaction between full and limited information methods using a well-known model.

Sustainability performance of construction : conceptual models of satisfaction levels in construction projects

In general, the performance of construction projects, including their sustainability performance, does not meet optimal expectations. One aspect of this is the performance of the participants who are independent and make a significance impact on overall project outcomes. Of these participants, the client is traditionally the owner of the project, the architect or engineer is engaged as the lead designer and a contractor is selected to construct the facilities. Generally, the performance of the participants is gauged by considering three main factors, namely, time, cost and quality. As the level of satisfaction is a subjective issue, it is rarely used in the performance evaluation of construction work. Recently, various approaches to the measurement of satisfaction have been made in an attempt to determine the performance of construction project outcomes - for instance, client satisfaction, customer satisfaction, contractor satisfaction, occupant satisfaction and home buyer satisfaction. These not only identify the performance of the construction project but are also used to improve and maintain relationships. In addition, these assessments are necessary for the continuous improvement and enhanced cooperation of participants. The measurement of satisfaction levels primarily involves expectations and perceptions. An expectation can be regarded as a comparative standard of different needs, motives and beliefs, while a perception is a subjective interpretation that is influenced by moods, experiences and values. This suggests that the disparity between perceptions and expectations may possibly be used to represent different levels of satisfaction. However, this concept is rather new and in need of further investigation. This chapter examines the methods commonly practised in measuring satisfaction levels today and the advantages of promoting these methods. The results provide a preliminary review of the advantages of satisfaction measurement in the construction industry and recommendations are made concerning the most appropriate methods to use in identifying the performance of project outcomes.

A review of simulation models for railway systems

With the advances in computer hardware and software development techniques in the past 25 years, digital computer simulation of train movement and traction systems has been widely adopted as a standard computer-aided engineering tool [1] during the design and development stages of existing and new railway systems. Simulators of different approaches and scales are used extensively to investigate various kinds of system studies. Simulation is now proven to be the cheapest means to carry out performance predication and system behaviour characterisation. When computers were first used to study railway systems, they were mainly employed to perform repetitive but time-consuming computational tasks, such as matrix manipulations for power network solution and exhaustive searches for optimal braking trajectories. With only simple high-level programming languages available at the time, full advantage of the computing hardware could not be taken. Hence, structured simulations of the whole railway system were not very common. Most applications focused on isolated parts of the railway system. It is more appropriate to regard those applications as primarily mechanised calculations rather than simulations. However, a railway system consists of a number of subsystems, such as train movement, power supply and traction drives, which inevitably contains many complexities and diversities. These subsystems interact frequently with each other while the trains are moving; and they have their special features in different railway systems. To further complicate the simulation requirements, constraints like track geometry, speed restrictions and friction have to be considered, not to mention possible non-linearities and uncertainties in the system. In order to provide a comprehensive and accurate account of system behaviour through simulation, a large amount of data has to be organised systematically to ensure easy access and efficient representation; the interactions and relationships among the subsystems should be defined explicitly. These requirements call for sophisticated and effective simulation models for each component of the system. The software development techniques available nowadays allow the evolution of such simulation models. Not only can the applicability of the simulators be largely enhanced by advanced software design, maintainability and modularity for easy understanding and further development, and portability for various hardware platforms are also encouraged. The objective of this paper is to review the development of a number of approaches to simulation models. Attention is, in particular, given to models for train movement, power supply systems and traction drives. These models have been successfully used to enable various ‘what-if’ issues to be resolved effectively in a wide range of applications, such as speed profiles, energy consumption, run times etc.

Wavelet based approaches and high resolution methods for complex process models

Many industrial processes and systems can be modelled mathematically by a set of Partial Differential Equations (PDEs). Finding a solution to such a PDF model is essential for system design, simulation, and process control purpose. However, major difficulties appear when solving PDEs with singularity. Traditional numerical methods, such as finite difference, finite element, and polynomial based orthogonal collocation, not only have limitations to fully capture the process dynamics but also demand enormous computation power due to the large number of elements or mesh points for accommodation of sharp variations. To tackle this challenging problem, wavelet based approaches and high resolution methods have been recently developed with successful applications to a fixedbed adsorption column model. Our investigation has shown that recent advances in wavelet based approaches and high resolution methods have the potential to be adopted for solving more complicated dynamic system models. This chapter will highlight the successful applications of these new methods in solving complex models of simulated-moving-bed (SMB) chromatographic processes. A SMB process is a distributed parameter system and can be mathematically described by a set of partial/ordinary differential equations and algebraic equations. These equations are highly coupled; experience wave propagations with steep front, and require significant numerical effort to solve. To demonstrate the numerical computing power of the wavelet based approaches and high resolution methods, a single column chromatographic process modelled by a Transport-Dispersive-Equilibrium linear model is investigated first. Numerical solutions from the upwind-1 finite difference, wavelet-collocation, and high resolution methods are evaluated by quantitative comparisons with the analytical solution for a range of Peclet numbers. After that, the advantages of the wavelet based approaches and high resolution methods are further demonstrated through applications to a dynamic SMB model for an enantiomers separation process. This research has revealed that for a PDE system with a low Peclet number, all existing numerical methods work well, but the upwind finite difference method consumes the most time for the same degree of accuracy of the numerical solution. The high resolution method provides an accurate numerical solution for a PDE system with a medium Peclet number. The wavelet collocation method is capable of catching up steep changes in the solution, and thus can be used for solving PDE models with high singularity. For the complex SMB system models under consideration, both the wavelet based approaches and high resolution methods are good candidates in terms of computation demand and prediction accuracy on the steep front. The high resolution methods have shown better stability in achieving steady state in the specific case studied in this Chapter.

GVS : A flexible, low-end, 3D visualisation framework for enhancing conceptual groundwater models for community, management and simulations

In this paper we discuss an advanced, 3D groundwater visualisation and animation system that allows scientists, government agencies and community groups to better understand the groundwater processes that effect community planning and decision-making. The system is unique in that it has been designed to optimise community engagement. Although it incorporates a powerful visualisation engine, this open-source system can be freely distributed and boasts a simple user interface allowing individuals to run and investigate the models on their own PCs and gain intimate knowledge of the groundwater systems. The initial version of the Groundwater Visualisation System (GVS v1.0), was developed from a coastal delta setting (Bundaberg, QLD), and then applied to a basalt catchment area (Obi Obi Creek, Maleny, QLD). Several major enhancements have been developed to produce higher quality visualisations, including display of more types of data, support for larger models and improved user interaction. The graphics and animation capabilities have also been enhanced, notably the display of boreholes, depth logs and time-series water level surfaces. The GVS software remains under continual development and improvement

Real-time simulation for power systems based on parallel computing - an empirical study

Abstract Computer simulation is a versatile and commonly used tool for the design and evaluation of systems with different degrees of complexity. Power distribution systems and electric railway network are areas for which computer simulations are being heavily applied. A dominant factor in evaluating the performance of a software simulator is its processing time, especially in the cases of real-time simulation. Parallel processing provides a viable mean to reduce the computing time and is therefore suitable for building real-time simulators. In this paper, we present different issues related to solving the power distribution system with parallel computing based on a multiple-CPU server and we will concentrate, in particular, on the speedup performance of such an approach.

Applying the open innovation system concept to infrastructure projects

Purpose: The goal of this conceptual paper is to provide tools to help maximise the value delivered by infrastructure projects, by developing methods to increase adoption of innovative products during construction. Methods: The role of knowledge flows in determining innovation adoption rates is conceptually examined. A promising new approach is developed. Open innovation system theory is extended, by reviewing the role of three frameworks: (1) knowledge intermediaries, (2) absorptive capacity and (3) governance arrangements. Originality: We develop a novel open innovation system model to guide further research in the area of adoption of innovation on infrastructure projects. The open innovation system model currently lacks definition of core concepts, especially with regard to the impact of different degrees and types of openness. The three frameworks address this issue and add substance to the open innovation system model, addressing widespread criticism that it is underdeveloped. The novelty of our model is in the combination of the three frameworks to explore the system. These frameworks promise new insights into system dynamics and facilitate the development of new methods to optimise the diffusion of innovation. Practical Implications: The framework will help to reveal gaps in knowledge flows that impede the uptake of innovations. In the past, identifying these gaps has been difficult given the lack of nuance in existing theory. The knowledge maps proposed will enable informed policy advice to effectively harness the power of knowledge networks, increase innovation diffusion and improve the performance of infrastructure projects. The models developed in this paper will be used in planned empirical research into innovation on large scale infrastructure projects in the Australian built environment.

A 'conceptual models' approach to organisational resilience

Gibson and Tarrant discuss the range of inter-dependant factors needed to manage organisational resilience. Over the last few years there has been considerable interest in the idea of resilience across all areas of society. Like any new area or field this has produced a vast array of definitions, processes, management systems and measurement tools which together have clouded the concept of resilience. Many of us have forgotten that ultimately resilience is not just about ‘bouncing back from adversity’ but is more broadly concerned with adaptive capacity and how we better understand and address uncertainty in our internal and external environments. The basis of organisational resilience is a fundamental understanding and treatment of risk, particularly non-routine or disruption related risk. This paper presents a number of conceptual models of organisational resilience that we have developed to demonstrate the range of inter-dependant factors that need to be considered in the management of such risk. These conceptual models illustrate that effective resilience is built upon a range of different strategies that enhance both ‘hard’ and ‘soft’ organisational capabilities . They emphasise the concept that there is no quick fix, no single process, management system or software application that will create resilience.

Ownership structure and business evaluation practices in family SMEs : evidence from empirical research in Italy

This paper presents early results from a pilot project which aims to investigate the relationship between proprietary structure of small and medium- sized Italian family firms and their owners’ orientation towards a “business evaluation process”. Evidence from many studies point out the importance of family business in a worldwide economic environment: in Italy 93% of the businesses are represented by family firms; 98% of them have less than 50 employees (Italian Association of Family Firms, 2004) so we judged family SMEs as a relevant field of investigation. In this study we assume a broad definition of family business as “a firm whose control (50% of shares or voting rights) is closely held by the members of the same family” (Corbetta,1995). “Business evaluation process” is intended here both as “continuous evaluation process” (which is the expression of a well developed managerial attitude) or as an “immediate valuation” (i.e. in the case of new shareholder’s entrance, share exchange among siblings, etc). We set two hypotheses to be tested in this paper: the first is “quantitative” and aims to verify whether the number of owners (independent variable) in a family firm is positively correlated to the business evaluation process. If a family firm is led by only one subject, it is more likely that personal values, culture and feelings may affect his choices more than “purely economic opportunities”; so there is less concern about monitoring economic performance or about the economic value of the firm. As the shareholders’ number increases, economic aspects in managing the firm grow in importance over the personal values and "value orientation" acquires a central role. The second hypothesis investigates if and to what extent the presence of “non- family members” among the owners affects their orientation to the business evaluation process. The “Cramer’s V” test has been used to test the hypotheses; both were not confirmed from these early results; next steps will lead to make an inferential analysis on a representative sample of the population.