User equilibrium traffic network assignment with stochastic travel times and late arrival penalty

The classical Wardrop user equilibrium (UE) assignment model assumes traveller choices are based on fixed, known travel times, yet these times are known to be rather variable between trips, both within and between days; typically, then, only mean travel times are represented. Classical stochastic user equilibrium (SUE) methods allow the mean travel times to be differentially perceived across the population, yet in a conventional application neither the UE or SUE approach recognises the travel times to be inherently variable. That is to say, there is no recognition that drivers risk arriving late at their destinations, and that this risk may vary across different paths of the network and according to the arrival time flexibility of the traveller. Recent work on incorporating risky elements into the choice process is seen either to neglect the link to the arrival constraints of the traveller, or to apply only to restricted problems with parallel alternatives and inflexible travel time distributions. In the paper, an alternative approach is described based on the ‘schedule delay’ paradigm, penalising late arrival under fixed departure times. The approach allows flexible travel time densities, which can be fitted to actual surveillance data, to be incorporated. A generalised formulation of UE is proposed, termed a Late Arrival Penalised UE (LAPUE). Conditions for the existence and uniqueness of LAPUE solutions are considered, as well as methods for their computation. Two specific travel time models are then considered, one based on multivariate Normal arc travel times, and an extended model to represent arc incidents, based on mixture distributions of multivariate Normals. Several illustrative examples are used to examine the sensitivity of LAPUE solutions to various input parameters, and in particular its comparison with UE predictions. Finally, paths for further research are discussed, including the extension of the model to include elements such as distributed arrival time constraints and penalties

Skin autofluorescence assessment of cardiovascular risk in people with severe mental illness

Background People with severe mental illness (SMI) show significantly shorter life expectancy, mostly due to more prevalent cardiovascular disease. Although age is a prominent contributor to contemporary risk assessment and SMI usually affects younger people, these assessments still do not reveal the actual risk. By assessing advanced glycation end products (AGEs), cardiovascular risk can be assessed independent of age. Aims To establish whether detection of AGEs with the AGE-reader will give a more accurate cardiovascular risk assessment in people with SMI. Method We compared assessment with the AGE-reader with that of the Systematic Coronary Risk Evaluation (SCORE) table in a group of 120 patients with SMI. Results The AGE-reader showed an increased cardiovascular risk more often than the SCORE table, especially in the youngest group. Conclusions Because of its ease of use and substantiation by studies done on other chronic diseases, we advocate use of the AGE-reader in daily care for patients with SMI to detect cardiovascular risk as early as possible. However, the findings of the current study should be evaluated with caution and should be seen as preliminary findings that require confirmation by a prospective longitudinal cohort study with a substantial follow-up observation period. (c) The Royal College of Psychiatrists 2018

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Computation of Equilibrium Distributions of Markov Traffic-Assignment Models

Markov traffic-assignment models explicitly represent the day-to-day evolving interaction between traffic congestion and drivers' information acquisition and choice processes. Such models can, in principle, be used to investigate traffic flows in stochastic equilibrium, yielding estimates of the equilibrium mean and covariance matrix of link or route traffic flows. However, in general these equilibrium moments cannot be written down in closed form. While Monte Carlo simulations of the assignment process may be used to produce “empirical” estimates, this approach can be extremely computationally expensive if reliable results (relatively free of Monte Carlo error) are to be obtained. In this paper an alternative method of computing the equilibrium distribution is proposed, applicable to the class of Markov models with linear exponential learning filters. Based on asymptotic results, this equilibrium distribution may be approximated by a Gaussian process, meaning that the problem reduces to determining the first two multivariate moments in equilibrium. The first of these moments, the mean flow vector, may be estimated by a conventional traffic-assignment model. The second, the flow covariance matrix, is estimated through various linear approximations, yielding an explicit expression. The proposed approximations are seen to operate well in a number of illustrative examples. The robustness of the approximations (in terms of network input data) is discussed, and shown to be connected with the “volatility” of the traffic assignment process

An Indirect Measure of Implicit Sexual Assertiveness: Reliability and Validity of the Sexual Assertiveness IAT in Young Adults in The Netherlands

In the present study we investigated the reliability and validity of an Implicit Association Test of sexual assertiveness (the SA-IAT) in a sample of young adults (n = 159). The D600 algorithm was used to calculate implicit sexual assertiveness scores. Explicit sexual assertiveness was measured using a selection of items from the Hurlbert Index of Sexual Assertiveness. Personality traits were assessed using the revised, short version of the Eysenck Personality Questionnaire. The internal consistency of the SA-IAT was evaluated based on split-half reliability, and found acceptable with α = 0.61 for the practice trials, and α = 0.70 for the test trials, after correction for attenuation. Convergent and divergent validity were evaluated using correlation analysis. Correlation with explicit sexual assertiveness was found to be low, as expected. Divergent validity of the SA-IAT was evaluated against the personality traits of extraversion, neuroticism, and social desirability. Except for a significant correlation of implicit sexual assertiveness with extraversion in the full sample and the female subsample, implicit sexual assertiveness and personality traits were not found to share variance, as expected