Analysis of crash severities using nested logit model—Accounting for the underreporting of crashes

Recent studies in the area of highway safety have demonstrated the usefulness of logit models for modeling crash injury severities. Use of these models enables one to identify and quantify the effects of factors that contribute to certain levels of severity. Most often, these models are estimated assuming equal probability of the occurrence for each injury severity level in the data. However, traffic crash data are generally characterized by underreporting, especially when crashes result in lower injury severity. Thus, the sample used for an analysis is often outcome-based, which can result in a biased estimation of model parameters. This is more of a problem when a nested logit model specification is used instead of a multinomial logit model and when true shares of the outcomes-injury severity levels in the population are not known (which is almost always the case). This study demonstrates an application of a recently proposed weighted conditional maximum likelihood estimator in tackling the problem of underreporting of crashes when using a nested logit model for crash severity analyses.     

Modeling school bus seat belt usage: Nested and mixed logit approaches

School bus seat belt usage has been of great interest to the school transportation community. Understanding factors that influence students’ decisions about wearing seat belts or not is important in determining the most cost-effective ways to improve belt usage rate, and thus the seat belt safety benefits. This paper presents a rigorous empirical analysis on data from Alabama School Bus Pilot Project using discrete choice modeling framework. In order to collect relevant information on individual student-trips, a new data collection protocol is adopted. Three choice alternatives are considered in the study: wearing, not wearing, and improperly wearing seat belts. A student's choice probabilities of these alternatives are modeled as functions of the student's characteristics and trip attributes. The coefficients of the variables in the functions are estimated first using standard multinomial logit model. Moreover, to account for potential correlations among the three choice alternatives and individual-level preference and response heterogeneity among users, nested and mixed logit models are employed in the investigation. Eight significant influence factors are identified by the final models. Their relative impacts are also quantified. The factors include age, gender and the home county of a student, a student's trip length, time of day, seat location, presence and active involvement of bus aide, and two levels of bus driver involvement. The impact of the seat location on students’ seat belt usage is revealed for the first time by this study. Both hypotheses that some of the choice alternatives are correlated and that individual-level heterogeneity exists are tested statistically significant. In view of this, the nested and the mixed logit model are recommended over the standard multinomial logit model to describe and predict students’ seat belt usage behaviors. The final nested logit model uncovers a correlation between improper wearing and not wearing, indicating there are some unknown or unobserved contributing factors that are common to these two choices. In the final random-parameter mixed logit model, individual preference heterogeneity is captured by random coefficients of county variables. Individual response heterogeneity is reflected in the random effect of a driver's remarks on students’ seat belt usage. Both recommended models are helpful in predicting seat belt usage rate quantitatively for given circumstances, and will provide valuable insights in practice of school transportation management.     

Modeling rear-end collisions including the role of driver's visibility and light truck vehicles using a nested logit structure

This paper presents an analysis of the effect of the geometric incompatibility of light truck vehicles (LTV)--light-duty trucks, vans, and sport utility vehicles--on drivers' visibility of other passenger cars involved in rear-end collisions. The geometric incompatibility arises from the fact that most LTVs ride higher and are wider than regular passenger cars. The objective of this paper is to explore the effect of the lead vehicle's size on the rear-end crash configuration. Four rear-end crash configurations are defined based on the type of the two involved vehicles (lead and following vehicles). Nested logit models were calibrated to estimate the probabilities of the four rear-end crash configurations as a function of driver's age, gender, vehicle type, vehicle maneuver, light conditions, driver's visibility and speed. Results showed that driver's visibility and inattention in the following (striker) vehicle have the largest effect on being involved in a rear-end collision of configuration CarTrk (a regular passenger car striking an LTV). Possibly, indicating a sight distance problem. A driver of a smaller car following an LTV, have a problem seeing the roadway beyond the LTV, and therefore would not be able to adjust his/her speed accordingly, increasing the probability of a rear-end collision. Also, the probability of a CarTrk rear-end crash increases in the case that the lead vehicle stops suddenly.     

Highway accident severities and the mixed logit model: an exploratory empirical analysis

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.     

Examining driver behavior at the onset of yellow in a traffic simulator environment: Comparisons between random parameters and latent class logit models

This study involves an examination of driver behavior at the onset of a yellow signal indication. Behavioral data were obtained from a driving simulator study that was conducted through the National Advanced Driving Simulator (NADS) laboratory at the University of Iowa. These data were drawn from a series of events during which study participants drove through a series of intersections where the traffic signals changed from the green to yellow phase. The resulting dataset provides potential insights into how driver behavior is affected by distracted driving through an experimental design that alternated handheld, headset, and hands-free cell phone use with “normal” baseline driving events. The results of the study show that male drivers ages 18–45 were more likely to stop. Participants were also more likely to stop as they became more familiar with the simulator environment. Cell phone use was found to some influence on driver behavior in this setting, though the effects varied significantly across individuals. The study also demonstrates two methodological approaches for dealing with unobserved heterogeneity across drivers. These include random parameters and latent class logit models, each of which analyze the data as a panel. The results show each method to provide significantly better fit than a pooled, fixed parameter model. Differences in terms of the context of these two approaches are discussed, providing important insights as to the differences between these modeling frameworks.     

Effects of excessive speeding and falling asleep while driving on crash injury severity in Ethiopia: A generalized ordered logit model analysis

The severity of injury from vehicle crash is a result of a complex interaction of factors related to drivers’ behavior, vehicle characteristics, road geometric and environmental conditions. Knowing to what extent each factor contributes to the severity of an injury is very important. The objective of the study was to assess factors that contribute to crash injury severity in Ethiopia. Data was collected from June 2012 to July 2013 on one of the main and busiest highway of Ethiopia, which extends from the capital Addis Ababa to Hawassa. During the study period a total of 819 road crashes was recorded and investigated by trained crash detectors. A generalized ordered logit/partial proportional odds model was used to examine factors that might influence the severity of crash injury. Model estimation result suggested that, alcohol use (Coef. = 0.5565; p-value = 0.017), falling asleep while driving (Coef. = 1.3102; p-value = 0.000), driving at night time in the absence of street light (Coef. = 0.3920; p-value = 0.033), rainfall (Coef. = 0.9164; p-value = 0.000) and being a minibus or vans (Coef. = 0.5065; p-value = 0.013) were found to be increased crash injury severity. On the other hand, speeding was identified to have varying coefficients for different injury levels, its highest effects on sever and fatal crashes. In this study risky driving behaviors (speeding, alcohol use and sleep/fatigue) were a powerful predictor of crash injury severity. Therefore, better driver licensing and road safety awareness campaign complimented with strict police enforcement can play a pivotal role to improve road safety. Further effort needed as well to monitor speed control strategies like; using the radar control and physical speed restraint measures (i.e., rumble strips).     

A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey

A retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police between January 2008 and December 2013 which are classified into three injury severity categories: fatal, injury, and no injury. Based on this classification, a multinomial logit analysis is performed to determine the risk factors affecting the severity of traffic injuries. The estimation results reveal that the following factors increase the probability of fatal injuries: drivers over the age of 65; primary-educated drivers; single-vehicle accidents; accidents occurring on state routes, highways or provincial roads; and the presence of pedestrian crosswalks. The results also indicate that accidents involving cars or private vehicles or those occurring during the evening peak, under clear weather conditions, on local city streets or in the presence of traffic lights decrease the probability of fatal injuries. This study comprises the most comprehensive database ever created for a Turkish sample. This study is also the first attempt to use an unordered response model to determine risk factors influencing the severity of traffic injuries in Turkey.     

A flexible brand choice model based on neural net methodology A comparison to the linear utility multinomial logit model and its latent class extension

Brand choice models as a rule have a linear (deterministic) utility function, i.e. they conceive utility as linear combination of predictors like price, sales promotion variables, brand name and other product attributes. To discover nonlinear effects on brands' utilities in a flexible way we specify deterministic utility by means of a certain type of neural net. This feedforward multilayer perceptron is able to approximate any continuous multivariate function and its derivatives with the desired level of precision. In an empirical study the neural net based choice model leads to better out-of-sample results than homogeneous and heterogeneous versions of linear utility MNL models. On the other hand the latent class variant of the linear utility MNL model attains better fit values for estimation data than the neural net model. The neural net approach implies different choice elasticities for most predictors and identifies nonlinear effects (like interaction effects, thresholds, saturation effects). Received: November 3, 2000 / Accepted: March 7, 2002     

A multi-agent virtual enterprise model and its simulation with Swarm

Virtual enterprise (VE) is a considerably complex organization in the world. More efforts have been made to establish such organization, among which multi-agent modelling and simulation are regarded as a promising approach. In this paper, MAS theory and technology are briefly reviewed. A novel multi-agent VE model is provided with a comparison with related work. Swarm as an outstanding multi-agent simulation programming tool is introduced. A modelling and simulation platform is developed with Swarm. The framework and main components of the platform are illustrated. The procedure of modelling VE on this platform is proposed. A case study using the platform on VE establishment in manufacturing domain is presented, and several common formulae to measure the performance of manufactures are developed in this case study.     

Estimation and test for the parameters of the arrhenius model

Estimation and test procedures are given for the parameters of the Arrhenius model. The assumed failure distribution is of exponential or Weibull type with fixed and temperature independent shape parameter. The approach is based on failure terminated experiments on at least two different tempera-ture levels with pre-assigned and equal number of failures for each temperature level. Estimation consists of two steps, modified maximum likelihood estimation followed by ordinary least squares regression. Statistical tests are defined on acceptance regions given in the particular parameter space by circles or ellipses depending only on the experimental design.     

Estimation under the Burr type XII failure model based on censored data: a comparative study

Summary Based on type-2 censored samples, the maximum likelihood, uniformly minimum variance unbiased, Bayes and empirical Bayes estimators of one of the two shape parameters (k) and reliability functionR(t) of the Burr type XII failure model are computed and compared. Computations show that when the censoring sizer=10, the EBE’s of κ andR(t),t=0.9, are better than the corresponding UMVUE’s for as few asm ^*=7 past samples fork andm ^*=11 past samples forR(0.9), in the sense of having smaller estimated risks, when the gamma conjugate prior is used.     

Design of manufacturing systems by a hybrid approach with neural network metamodelling and stochastic local search

Metamodels are models of simulation models. Metamodels are able to estimate the simulation responses corresponding to a given combination of input variables. A simulation metamodel is easier to manage and provides more insights than simulation alone. Traditionally, the multiple regression analysis is utilized to develop the metamodel from a set of simulation experiments. Simulation can consequentially benefit from the metamodelling in post-simulation analysis. A backpropagation (BP) neural network is a proven tool in providing excellent response predictions in many application areas and it outperforms regression analysis for a wide array of applications. In this paper, a BP neural network is used to generate metamodels for simulated manufacturing systems. For the purpose of optimal manufacturing systems design, mathematical models can be formulated by using the mapping functions generated from the neural network metamodels. The optimization model is then solved by a stochastic local search approach, simulated annealing (SA), to obtain an optimal configuration with respect to the objective of the systems design. Instead of triggering the detailed simulation programs, the SA-based optimization procedure evaluates the simulation outputs by the neural network metamodels. By using the SA-based optimization algorithm, the solution space of the studied problem is extensively exploited to escape the entrapment of local optima while the number of time consuming simulation runs is reduced. The proposed methodology is illustrated to be both effective and efficient in solving a manufacturing systems design problem through an example.     

Measuring productive performance using binary and ordinal output variables: the case of the Swedish fire and rescue services

Fire protection is an example of a complex production process. This study measures efficiency by constructing binary and ordinal output variables from information on residential fires in Sweden about how a fire spreads from when the fire and rescue brigade arrives to when a fire is suppressed. The motivations behind this study are that there are only a few studies trying to estimate production efficiency for fire and rescue services, that data on a more detailed level is interesting for some public services, and there is a need to be able to measure efficiency differences even if only a binary or ordinal output variable is available. Using a logit random parameter model, the random effects are interpreted as efficiency differences. The conclusions are that fire and rescue services with a more flexible fire organisation with first response persons, working in collaboration with other municipalities and with larger populations are more efficient.     

Applicability of the Kissinger’s formula and comparison with the McNabb-Foster model in simulation of thermal desorption spectrum

The Kissinger’s formula has been analyzed theoretically and numerically to be applicable in simulation of not only the detrapping-controlled thermal desorption but also the diffusion-controlled thermal desorption provided that a sufficient pre-exposure before thermal desorption is carried out in the diffusion-controlled desorption. The desorption activation energy or the binding energy can be evaluated by a single thermal desorption spectrum from a single type of trap site. In the case of detrapping-controlled desorption the constant parameter A in the Kissinger’s formula approaches the pre-exponential factor of detrap parameter, p⁰, in the McNabb-Foster model as specimen size becomes smaller. In the case of diffusion-controlled desorption where local equilibrium may be maintained and an effective diffusivity can be expressed, the A value changes according to A = α²D₀ where D₀ is the pre-exponential factor of effective diffusivity and α is a geometrical parameter of specimen.     

A model for simulating the grinding and classification cyclic system of waste PCBs recycling production line

Crushing and separating technology is widely used in waste printed circuit boards (PCBs) recycling process. A set of automatic line without negative impact to environment for recycling waste PCBs was applied in industry scale. Crushed waste PCBs particles grinding and classification cyclic system is the most important part of the automatic production line, and it decides the efficiency of the whole production line. In this paper, a model for computing the process of the system was established, and matrix analysis method was adopted. The result showed that good agreement can be achieved between the simulation model and the actual production line, and the system is anti-jamming. This model possibly provides a basis for the automatic process control of waste PCBs production line. With this model, many engineering problems can be reduced, such as metals and nonmetals insufficient dissociation, particles over-pulverizing, incomplete comminuting, material plugging and equipment fever.     

Understanding sustained usage of health and fitness apps: Incorporating the technology acceptance model with the investment model

With the increasing popularity and adoption of health/fitness apps, more research attention is needed to examine users' behavior in order to promote sustained usage of the apps. The purpose of this study is to understand health and fitness app users' decision-making process by integrating the Technology Acceptance Model (TAM) and the Investment Model (IM). Three hundred forty-six responses were collected from health and fitness app users in China. A confirmatory factor analysis and a structural equation modeling analysis were conducted. Results show that continuance intention to use the apps is significantly affected by constructs included in the two models. Specifically, perceived ease of use of the apps has a positive effect on perceived usefulness, which further affects users' intention to continue using the apps. In addition, perceived usefulness and perceived ease of use of the apps have significant effects on users' satisfaction, investment size, and quality of available alternatives, and these three constructs affect users' relationship commitment to the apps, which, in turn, influences users' continuing intention. The integrated model provides new insights into the app users' decision-making process and suggests practical implications for app providers, such as developing strategies to evoke and maintain users’ interest in their apps, keeping users satisfied by personalizing their experience, and designing features that keep users invested in the apps.     

Exact model reduction with delays: closed-form distributions and extensions to fully bi-directional monomolecular reactions

In order to systematically understand the qualitative and quantitative behaviour of chemical reaction networks, scientists must derive and analyse associated mathematical models. However, biochemical systems are often very large, with reactions occurring at multiple time scales, as evidenced by signalling pathways and gene expression kinetics. Owing to the associated computational costs, it is then many times impractical, if not impossible, to solve or simulate these systems with an appropriate level of detail. By consequence, there is a growing interest in developing techniques for the simplification or reduction of complex biochemical systems. Here, we extend our recently presented methodology on exact reduction of linear chains of reactions with delay distributions in two ways. First, we report that it is now possible to deal with fully bi-directional monomolecular systems, including degradations, synthesis and generalized bypass reactions. Second, we provide all derivations of associated delays in analytical, closed form. Both advances have a major impact on further reducing computational costs, while still retaining full accuracy. Thus, we expect our new methodology to respond to current simulation needs in pharmaceutical, chemical and biological research.     

Crack propagation analyses with CTOA and cohesive model: Comparison and experimental validation

Two numerical models, namely an R-curve approach based on the crack tip opening angle (CTOA) and a cohesive model, are compared regarding their ability to predict ductile crack extension in thin aluminium sheets, which can be simulated under the assumption of plane stress. The experimental database is presented, the measuring techniques for the various quantities (optically and with clip gauges) are shown and the identification and validation of the respective model parameters are explained. A general concept for their identification is then derived for the case of thin walled structures under Mode I conditions.In order to investigate the performance of the models under different constraint conditions and the transferability of their parameters, C(T) specimens are used for parameter identification and M(T) specimens for validation. It is shown that for both models a single set of parameters describes the mechanical behaviour of both types of specimens. Cross-checking the two models, the crack tip opening angle is determined from the cohesive model calculations and compared with the experimental values.     

Finite-size effects in surface tension: Thermodynamics and the Gaussian interface model

It has been suggested by Kayser that finite-size effects associated with capillary waves might play a significant role in some surface tension measurements; for capillary rise between plates a distance D apart, an effect varying as 1/D and apparently observable in measurements, was proposed. In reconsidering this problem, one must analyze the thermodynamics of finite-size corrections to surface tension. In particular, one sees that capillary rise between plates does not measure the interfacial free energy density but, rather, a derivative of the interfacial free energy with respect to a system dimension. The quantity needed to draw definite conclusions, the finite-size residual free energy, can be calculated within the harmonic or Gaussian capillary wave model in d spatial dimensions with the aid of Poisson summation techniques and should yield the correct leading asymptotic behavior. For d=3 and experimentally relevant parameter values, the results are independent of the short-wavelength cutoff needed in the model and can be checked against the theory of conformai covariance at two-dimensional critical points. It is found that the finite-size effects in capillary-rise measurements of surface tension vary as 1/D ² (with a universal coefficient) but are too small to be seen in current experiments.Invited paper presented at the Tenth Symposium on Thermophysical Properties, June 20–23, 1988, Gaithersburg, Maryland, U.S.A.