Train overcrowding: investigating the use of better information provision to mitigate the issues

Crowded trains are a feature of many railway networks, and adversely affect both train passengers and rail operators. For passengers, the lack of space or inability to get a seat can lead to a lack of physical comfort, reduced productivity and increased stress. Crowded trains can also lead to problems boarding and alighting, increasing dwell times and making it harder for operators to provide a reliable service. It is therefore desirable to reduce crowding levels, but it isn’t always practical to achieve this by increasing capacity and other measures need to be considered. Some passengers have shown willingness to change their behavior to avoid crowding, for example by waiting for a later train, and measures to encourage such behavioral changes more widely could be beneficial overall. Better information provision could be one such measure, and a stated preference survey was undertaken on a commuter and airport service in order to investigate this further. It was found that the provision of information about crowding levels and seating availability on alternative trains would encourage some passengers to wait for a less crowded train. While the willingness of passengers to wait for a later train varied with both trip purpose and with the origin station, the findings suggest that real-time information would improve the passenger experience and could form the basis of a revenue neutral demand-management system. The implications for station design are particularly pertinent for countries such as the USA where significant investment in new passenger rail systems is expecte

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable tim

Making in-class skills training more effective: the scope for interactive videos to complement the delivery of practical pedestrian training

Skills and awareness of young pedestrians can be improved with on-street practical pedestrian training, often delivered in schools in the United Kingdom by local authorities with the intention of improving road safety. This training is often supplemented by in-class paper based worksheet activities which are seen to be less effective than practical training in that they focus on knowledge acquisition rather than directly improving the correct application of safe pedestrian skills at the roadside. Previous research indicates that interactive video tools have the potential to develop procedural skills whilst offering an engaging road safety educational experience, which could positively impact on road crossing behaviour.In this paper, the design and development of a hazard-identification interactive road safety training video targeting child road crossing skills is presented. The interactive video was shown to be an engaging training resource for 6-7 year old children. The tool’s scope for improving pedestrians’ roadside skills is considered along with the wider implications for interactive video to aid safety training in other areas

An automated signalized junction controller that learns strategies from a human expert

An automated signalized junction control system that can learn strategies from a human expert has been developed. This system applies machine learning techniques based on logistic regression and neural networks to affect a classification of state space using evidence data generated when a human expert controls a simulated junction. The state space is constructed from a series of bids from agents, which monitor regions of the road network. This builds on earlier work which has developed the High Bid auctioning agent system to control signalized junctions using localization probe data. For reference the performance of the machine learning signal control strategies are compared to that of High Bid and the MOVA system, which uses inductive loop detectors. Performance is evaluated using simulation experiments on two networks. One is an isolated T-junction and the other is a two junction network modelled on the High Road area of Southampton, UK. The experimental results indicate that machine learning junction control strategies trained by a human expert can outperform High Bid and MOVA both in terms of minimizing average delay and maximizing equitability; where the variance of the distribution over journey times is taken as a quantitative measure of equitability. Further experimental tests indicate that the machine learning control strategies are robust to variation in the positioning accuracy of localization probes and to the fraction of vehicles equipped with probes

Crossing the macro-micro divide in systems ergonomics

This paper attempts to further explore the concept of mesoergonomics and the implications this has for the use of the systems approach within ergonomics. The concept has been applied within the field of organisational behaviour to understand a wide variety of complex work contexts and the interaction between individual, group and organisational levels of analysis. More recently, researchers in human factors and ergonomics have similarly argued that there is a need for holistic, integrated accounts of the relationship between macro- and micro- system levels (Karsh, 2003). In order to go some way toward achieving this, we outline two case studies drawn from health care (infection control, electronic medical records) and analyze these using mesoergonomic constructs. The case studies are used to outline a set of steps towards a more general framework for mesoergonomic research

Polyolefin–polar block copolymers from versatile new macromonomers

A new metallocene-based polymerization mechanism is elucidated in which a zirconium hydride center inserts α-methylstyrene at the start of a polymer chain. The hydride is then regenerated by hydrogenation to release a polyolefin containing a single terminal α-methylstyrenyl group. Through the use of the difunctional monomer 1,3-diisopropenylbenzene, this catalytic hydride insertion polymerization is applied to the production of linear polyethylene and ethylene–hexene copolymers containing an isopropenylbenzene end group. Conducting simple radical polymerizations in the presence of this new type of macromonomer leads to diblock copolymers containing a polyolefin attached to an acrylate, methacrylate, vinyl ester, or styrenic segments. The new materials are readily available and exhibit interfacial phenomena, including the mediation of the mixing of immiscible polymer blends

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Design for Mobile Mental Health:An Exploratory Review

A large number of mobile mental health apps are available to the public but current knowledge about requirements of designing such solutions is scarce, especially from sociotechnical and user centred points of view. Due to the significant role of mobile apps in the mental health service models, identifying the design requirements of mobile mental health solutions is crucial. Some of those requirements have been addressed individually in the literature, but there are few research studies that show a comprehensive picture of this domain. This exploratory review aims to facilitate such holistic understanding. The main search keywords of the review were identified in a cross-disciplinary requirements workshop. The search was started by finding some core references in the healthcare databases. A wider range of references then has been explored using a snowball method. Findings showed that there is a good understanding of individual design requirements in current literature but there are few examples of implementing a combination of different design requirements in real world products. The design processes specifically developed for mobile mental health apps are also rare. Most studies on operational mobile mental health apps address major mental health issues while prevention and wellbeing areas are underdeveloped. In conclusion, the main recommendations for designing future mobile mental health solutions include: moving towards sociotechnical and open design strategies, understanding and creating shared value, recognizing all dimensions of efficacy, bridging design and medical research and development, and considering an ecosystem perspective