Considering built environment and spatial correlation in modeling pedestrian injury severity

Objective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.

Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.

Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.

Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.     

Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects

Objective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR.

Methods: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model.

Results: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime.

Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.     

A study of fatal pedestrian crashes at rural low-volume road intersections in southwest China

Objective: Although intersections correspond to a small proportion of the entire roadway system, they account for a disproportionally high number of fatal pedestrian crashes, especially on rural roads situated in low- and middle-income countries. This article examines pedestrian safety at rural intersections and suggests applicable accident prevention treatments by providing an in-depth analysis of 28 fatal pedestrian crashes from 8 low-volume roads in southwest China.

Methods: The driving reliability and error analysis method (DREAM) is a method to support a systematic classification of accident causation information and to facilitate aggregation of that information into patterns of contributing factors. This is the first time that DREAM was used to analyze pedestrian–vehicle crashes and provide suggestions for road improvements in China.

Results: The key issues adversely affecting pedestrian safety can be organized in 4 distinctive thematic categories, namely, deficient intersection safety infrastructure, lack of pedestrian safety education, inadequate driver training, and insufficient traffic law enforcement. Given that resources for traffic safety investments in rural areas are limited, it is determined that the potential countermeasures should focus on low-cost, easily implementable, and long-lasting measures increasing the visibility and predictability of pedestrian movement and reducing speeding and irresponsible driving among drivers and risk-taking behaviors among pedestrians.

Conclusions: Accident prevention treatments are suggested based on their suitability for rural areas in southwest China. These countermeasures include introducing better access management and traffic calming treatments, providing more opportunities for pedestrian education, and enhancing the quality of driver training and traffic law enforcement.     

Decomposition analysis of the effects of vehicle safety technologies on the motor vehicle collision–related mortality rate from 1994 to 2015

Abstract

Objective: Though the mortality rate for motor vehicle collisions (MVCs) has been decreasing since the 1960s with the advent of the first federal seat belt laws in 1968, MVC remains a leading cause of death for individuals aged 1 to 44 years. The purpose of this study is to examine the effects of frontal (FABs) and side airbags (SABs) and electronic stability control (ESC) on the components of the MVC mortality rate.

Methods: The MVC mortality rate from 1994 to 2015 was separated into its components of exposure of vehicles, exposure of travel, collision density, injury incidence, and case fatality rate. Year was categorized on the availability of safety technology in vehicles: 1994–1997 (first-generation FABs mandated), 1998–2001 (sled-certified, second-generation FABs mandated), 2002–2006 (increasing prevalence of SABs and ESC), 2007–2011 (advanced airbags mandated), and 2012–2015 (ESC mandated, SAB in over 90% of vehicles, introduction of advanced safety systems). Relative contributions (RCs) of the components to changes in the MVC-related mortality rate were calculated as the absolute value of the component’s beta coefficient divided by the sum of the absolute values of all components’ beta coefficients. Negative binomial regression–estimated rate ratios (RRs) for the changes in the rate of each component by year category compared to the prior year category.

Results: Significant decreases in the MVC mortality rate were observed for 2007–2011 and 2012–2015. The decrease in 2007–2011 was due in most part to an 18% decrease in the injury incidence (RR?=?0.82, P?<?.0001, RC?=?63%), though there was a noted contribution by the decrease in vehicle miles traveled (RR?=?0.95, P?<?.0001, RC?=?15%). The continued decrease in mortality in 2012–2015 was due is most part to the 10% decreased case fatality rate (RR?=?0.90, P?<?.0001, RC?=?66%) because there was no significant change in the vehicle miles traveled and injury incidence.

Conclusions: The results of this study highlight the effects of vehicle safety technologies on the MVC-related mortality rate and can help direct prevention efforts. Through the study period, there was no meaningful contribution to decreases in the MVC-related mortality rate due to components related to exposure (i.e., vehicles per population and the rate of vehicle miles traveled), suggesting that prevention efforts at decreasing exposure prevalence would have little effect on the MVC-related mortality rate. Instead, prevention efforts should continue to focus on event-phase methods to decrease injury occurrence and mitigate injury severity during the collision.     

机动车尾气VOCs排放特征及减排措施研究

分析了机动车尾气挥发性有机物(VOCs)的排放特征,发现尾气VOCs排放具有明显的日变化和季节变化特征。不同区域不同车型机动车尾气VOCs成分谱略有差异,轻型汽油车尾气VOCs中芳香烃和烷烃含量较高,柴油车烷烃含量较高。尾气排放受机动车保有量、行驶里程、维护保养水平、行驶速度和燃油标准、排放标准等因素影响。从优先控制汽油车、加快机动车更新、采取本地化减排措施、加强多元管理措施、提高科研水平等方面提出了针对性的减排措施。     

水源地污染源无人机遥感监测

无人机遥感是近年新兴的航空遥感技术,随着无人机平台与载荷的不断进步,其研究应用愈加深入,已广泛应用于各行业领域,并作为卫星遥感技术的补充,愈加发挥着重要作用。为拓展无人机遥感技术应用潜力,分析无人机在环保领域应用效果,以扬州市南水北调工程东线输水水域的水源地为研究区域,基于其污染源特征及周边生态环境状况,研究无人机平台与载荷搭配(可见光与热红外)技术,监测研究区污染源,分析其对水质的潜在影响。结果表明,研究区存在多处污染源,主要为点源污染(工业点源、水产养殖、生活污水),面源污染(河道码头、水土流失、农业)和内源污染(航运船舶、桥梁交通运输、渔民生活)等,同时,发现某助剂厂隐藏在水面下的4个排污口。该项技术的实验研究,发挥了无人机平台与载荷搭配应用效果,获取了污染源的类型、数量、空间分布及其扩散形态,为水源地无人机遥感技术应用奠定了工作基础。     

机动车不同速度下外噪声变化与衰减关系研究

通过在机动车测试场进行单辆机动车行驶状况的实验,研究机动车车型大小、行驶速度快慢与噪声量大小的关系,同时研究不同距离下机动车行驶外噪声的衰减规律。数据结果显示,测试条件下,噪声量随着速度提升而增加,呈线性关系或对数关系;相同速度下,大型车的噪声量比小型车的高8.1～11.0dB;在一定距离范围内,距离每增加3.5m,机动车噪声量将衰减2.2～3.0dB。     

Application and Quality Assessment of an Instantaneous Vehicle Emission Model at Fleet Level

A developed instantaneous emission model is applied to predict emission factors for small vehicle fleets for quality assessment. Extensive vehicle measurements of pre-Euro-1 gasoline, Euro-3 gasoline, and Euro-2 diesel vehicles are available. The data were used to develop individual vehicle emission models for each car. The prediction quality for each vehicle category was determined by averaging the results obtained from the individual vehicle models. The results show that the prediction quality is improved in comparison with the individual vehicles, even with a small number of vehicles in a specific category. This indicates that the errors in the individual models are mainly random and that prediction quality, when applied to fleets of cars, is exceptionally high.     

双怠速法机动车尾气排放检测结果的影响因素分析

针对双怠速法机动车尾气排放检测过程中经常大量出现过量空气系数(λ)不合格现象,结合相关标准和工作实践对影响检测结果的因素进行分析。结果显示,取样管路的气密性差、系统响应时间未达标准、测试软件程序设置错误、高怠速转速设置不合理及由于车辆特性导致的选用标准及操作错误均会导致λ测试结果超标,影响双怠速检测法的准确性。     

An Approach Toward Understanding Wildlife-Vehicle Collisions

Among the most conspicuous environmental effects of roads are vehicle-related mortalities of wildlife. Research to understand the factors that contribute to wildlife-vehicle collisions can be partitioned into several major themes, including (i) characteristics associated with roadkill hot spots, (ii) identification of road-density thresholds that limit wildlife populations, and (iii) species-specific models of vehicle collision rates that incorporate information on roads (e.g., proximity, width, and traffic volume) and animal movements. We suggest that collision models offer substantial opportunities to understand the effects of roads on a diverse suite of species. We conducted simulations using collision models and information on Blanding’s turtles (Emydoidea blandingii), bobcats (Lynx rufus), and moose (Alces alces), species endemic to the northeastern United States that are of particular concern relative to collisions with vehicles. Results revealed important species-specific differences, with traffic volume and rate of movement by candidate species having the greatest influence on collision rates. We recommend that future efforts to reduce wildlife-vehicle collisions be more proactive and suggest the following protocol. For species that pose hazards to drivers (e.g., ungulates), identify collision hot spots and implement suitable mitigation to redirect animal movements (e.g., underpasses, fencing, and habitat modification), reduce populations of problematic game species via hunting, or modify driver behavior (e.g., dynamic signage that warns drivers when animals are near roads). Next, identify those species that are likely to experience additive (as opposed to compensatory) mortality from vehicle collisions and rank them according to vulnerability to extirpation. Then combine information on the distribution of at-risk species with information on existing road networks to identify areas where immediate actions are warranted.