CAR FOLLOWING TECHNIQUES: THE ROLE OF THE HUMAN FACTOR RECONSIDERED

[EN] Engineering and psychophysiological car following models emerge in the late 1950s (Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and explanatory mechanisms, but both assume a fundamental tenet: following each other, drivers invariably attempt to couple, keeping safety distance. More recent models focus on the spontaneous emergence of traffic jams that results from the properties of a system of interacting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., (2008) have successfully recreated the conditions that allow the observation of the typical soliton wave going backwards through several car clusters. When certain speed, density and inter-vehicular distance join, so do traffic jams. Some of us have built upon these and other factors (e.g., wave movement in nature) exploring the mathematical properties of a system with three incognita that also needs three variables to be solved (Melchor & Sánchez, 2014). Two canonical car-following techniques emerge as a consequence: Driving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers actually understand and follow either way, or do they stick to a basic normative driving behavior? This paper summarizes the results after three experimental studies done with a driving simulator. Several performance measures from individual drivers (accelerations, decelerations, average speed, distance to leader, and so on) were taken. As an overall indicator, results consistently announce in the three studies that DI trips consume less fuel (about 20%) than DD ones.Blanch Micó, MT.; Lucas Alba, A.; Bellés Rivera, T.; Ferruz Gracia, AM.; Melchor-Galán, Ó.; Delgado Pastor, L.; Ruíz Jimenez, F.... (2016). CAR FOLLOWING TECHNIQUES: THE ROLE OF THE HUMAN FACTOR RECONSIDERED. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 851-858. https://doi.org/10.4995/CIT2016.2015.3341OCS85185

Car following: Comparing distance-oriented vs. inertia-oriented driving techniques

The rationale behind most car-following (CF) models is the possibility to appraise and formalize how drivers naturally follow each other. Characterizing and parametrizing Normative Driving Behavior (NDB) became major goals, especially during the last 25 years. Most CF models assumed driver propensity for constant, safe distance is axiomatic. This paper challenges the idea of safety distance as the main parameter defining a unique (or natural) NDB. Instead, it states drivers can adapt to reactive and proactive car following. Drawing on recent CF models close to the Nagoya paradigm and on other phenomena (e.g., wave movement in Nature), we conceived car following by Driving to keep Inertia (DI) as an alternative to Driving to keep Distance (DD). On a driving simulator, three studies (N ¼ 113) based on a repeated-measures experimental design explored the efficiency of these elementary techniques by measuring individual driver performance (e.g., accelerations, decelerations, average speed, distance to leader). Drivers easily grasped and applied either technique and easily switched back and forth between the two. As an overall indicator, all the studies revealed DI trips use about 20% less fuel than DD trips do.Support came from Fundación Universitaria Antonio Gargallo y Obra Social Ibercaja, Spain (grant 2015/B011

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries