Determination of the force characteristic of head car’s passive safety system – large road vehicle interaction in a collision

One of the priorities of the National Economic Strategy of Ukraine for the Period up to 2030 is the development of the transport sector, in particular railway vehicle renewal, the introduction of high-speed railway passenger transport, and railway traffic safety improvement. The home motor-car trains must be renewed in compliance with new home standards harmonized with European ones, among which one should mention the Ukrainian State Standard DSTU EN 15227, which specifies the passive safety of a passenger train in its emergency collisions with different obstacles. New car designs must provide not only effective up-to-date braking systems to prevent emergency collisions, but also passive safety systems with energy-absorbing devices. The main purpose of these devices is to reduce the longitudinal forces in the intercar connections and the car accelerations to an acceptable level for the three collision scenarios specified in the DSTU EN 15227. The Department of Statistical Dynamics and Multidimensional Mechanical Systems Dynamics, Institute of Technical Mechanics of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, developed a passive protection concept for home high-speed passenger trains in emergency collisions by the DSTU EN 15227 scenarios, proposals on the passive protection of a motor-car train head car, and honeycomb designs of lower- and upper-level energy-absorbing devices (EAD 1 and UL EAD, respectively), which are integrated into the head car front part and serve to damp the major part of the impact energy in front collisions with obstacles. This paper considers DSTU EN 15227 Scenario 3: a collision of a reference motor-car train at a speed of 110 km/h at a railway crossing with a large 15 t road vehicle, which is simulated as a large-size deformable obstacle (LSDO). The aim of the paper is to determine the force characteristic of the interaction of energy-absorbing devices mounted on the head car front part with a large road vehicle in a collision to assess the compliance of the proposed passive protection with the normative requirements. Finite-element models were constructed to analyze the plastic deformation of the elements of the EAD 1 – LSDO, UL EAD – LSDO, and EAD 1 – UL EAD –LSDO systems in a collision with account for geometric and physical nonlinearities, steel dynamic hardening as a function of the impact speed, and varying contact interaction between the elements of the systems considered. The studies conducted made it possible to determine the force characteristics of energy-absorbing device – obstacle interaction and the total characteristic of the contact force between two lower-level devices and two upper-level ones as a function of the obstacle center of mass displacement in a collision. The proposed mathematical models and the calculated force characteristics may be used in the study of the dynamics of a reference motor-car train – large road vehicle collision with the aim to assess the compliance of the passive protection of the home head car under design with the DSTU EN 15227 requirements.

Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.

P.184 Off-road vehicle fatalities and alcohol in patients with major traumatic brain injury: the risk of impaired driving

Background: Intoxicated patients injured in off road vehicle (ORV) crashes have higher rates of traumatic brain injury (TBI) and intensive care unit (ICU) admission, as well as prolonged ICU length of stay. This study evaluated the impact of alcohol intoxication on mortality among major TBI patients injured in off-road vehicle crashes. Methods: A retrospective analysis (2002-2014) of off-road vehicle injuries in Nova Scotia resulting in major TBI was performed. ORVs included ATVs, snowmobiles, and dirt bikes. A logistic regression model was constructed to test for in-hospital mortality and adjusted for age, Abbreviated Injury Scale (AIS) Head, Injury Severity Score, and blood alcohol concentration (BAC). Results: There were 176 drivers and passengers of off-road vehicles. Overall mortality was 28%. BAC testing was performed in 61% patients; 85% of pre-hospital deaths were BAC positive (mean BAC=31 ± 17.39 mmol/L) and 70% in-hospital deaths were BAC positive (mean BAC=26 ± 23.12 mmol/L). After adjusting for confounders, high injury severity and intoxication increased the likelihood of in-hospital mortality. Conclusions: These findings demonstrate that alcohol intoxication is a significant risk factor for mortality among off-road vehicle collisions; for every mmol/L change in BAC, there was a 10% increase in the chance of in-hospital mortality.