Two discrete choice experiments on laboratory safety decisions and practices

IntroductionThe path toward enhancing laboratory safety requires a thorough understanding of the factors that influence the safety-related decision making of laboratory personnel. Method: We developed and administered a web-based survey to assess safety-related decision making of laboratory personnel of a government research organization. The survey included two brief discrete choice experiments (DCEs) that allowed for quantitative analysis of specific factors that potentially influence safety-related decisions and practices associated with two different hypothetical laboratory safety scenarios. One scenario related to reporting a laboratory spill, and the other scenario involved changing protective gloves between laboratory rooms. The survey also included several brief self-report measures of attitude, perception, and behavior related to safety practices. Results: Risk perception was the most influential factor in safety-related decision making in both scenarios. Potential negative consequences and effort associated with reporting an incident and the likelihood an incident was detected by others also affected reporting likelihood. Wearing gloves was also affected somewhat by perceived exposure risk, but not by other social or work-related factors included in the scenarios. Conclusions: The study demonstrated the promise of DCEs in quantifying the relative impact of several factors on safety-related choices of laboratory workers in two hypothetical but realistic scenarios. Participants were faced with hypothetical choice scenarios with realistic features instead of traditional scaling techniques that ask about attitudes and perceptions. The methods are suitable for addressing many occupational safety concerns in which workers face tradeoffs in their safety-related decisions and behavior. Practical Application: Safety-related decisions regarding laboratory practices such as incident reporting and use of PPE were influenced primarily by workers’ perceptions of risk of exposure and severity of risks to health and safety. This finding suggests the importance of providing laboratory workers with adequate and effective education and training on the hazards and risks associated with their work. DCEs are a promising research method for better understanding the relative influences of various personal, social, and organizational factors that shape laboratory safety decisions and practices. The information gained from DCEs may lead to more targeted training materials and interventions.     

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.     

Passenger behavior in trains during emergency situations

IntroductionThis research presents a methodology for analyzing the behavior of people (passengers and crew) involved in emergency situations on passenger trains.MethodsThis methodological tool centers around a qualitative character study coming from Focus Groups (FG) and in-depth interviews to extract the determinant variables on passenger and crew behavior when faced with certain emergency situations on trains.ResultsThis research has led to the creation of a classification of possible behaviors associated to each type of incident and dependent on certain variables. The qualitative study was used as the basis for modeling stated preference data using logit type discrete choice models to characterize and quantify the behavior. The most important results show that the determinant variables on passenger behavior correspond to the type of emergency suffered (its degree of seriousness), the type of passenger, the reasons for the journey (demands of time), the information received during the incident, the relationship between crew and passengers, the duration of the incident and the conditions (temperature control, availability of water, occupancy of the train), the distance to the destination station, and finally, the outside weather conditions. This research was carried out using the Spanish railway network as its reference, although it is applicable to any geographical area.Impact on IndustryThe results show that the information variable should be considered in the development of future research and that the evidences of this research can be used to develop behavioral models for modeling railway passenger evacuations.     

Automated vehicles and older drivers in Canada

Introduction: As seniors represent a growing proportion of the driving population, research about how automated vehicles can help improve older driver safety and mobility is highly relevant. This paper examines the knowledge, attitudes and perceptions of older drivers towards limited self-driving vehicles (LSDVs), and how these variables can influence the likelihood that they will rely on this technology. Method: The study includes data from a previous national survey (N = 2662) about automated vehicle technology, with new analyses to test hypothetical models using structural equation modeling. Results of the first model were confirmed and built upon with a second more complex model that incorporated the construct “behavioral adaptation.” Focus groups with older drivers were also conducted (N = 38) to help reveal nuances in older drivers' knowledge, attitudes, perceptions, and behaviors regarding this technology. Results: Survey results demonstrated that feelings of safety and knowledge about LSDVs are positively related to perceived ease of use and adoption of the technology. The positive association between safety and perceived ease of use was further highlighted when comparing responses of older drivers to those of younger age groups, as older drivers were significantly less likely to agree that LSDVs were easy to use and were significantly less agreeable about feeling safe using them. Focus groups results confirmed that safety and knowledge of LSDVs are essential to the likelihood of adopting this technology, and revealed a high receptivity among older drivers to educational strategies and tools to increase their knowledge of LSDVs. Implications for educational strategies and safety benefits for older drivers are discussed. Practical applications: Results provide insight into strategies to encourage the early adoption of automated vehicles by older drivers and facilitate a safer transition towards automated vehicles that is lead by a cohort of safety-conscious drivers.     

Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications: The application of CAVs can improve the stability and safety of traffic flow.     

SESAR safety decision-making: Lessons from environmental,nuclear and defense modeling

SESAR, the ‘Single European Sky Air traffic Research’ program, envisages radical changes for European Air Traffic Management (ATM). It integrates and implements new technologies and information processing. This paper examines the safety decision-making in the implementation of SESAR projects. SESAR poses new safety problems because it adopts new paradigms for ATM safety – what lessons are there from environmental, nuclear and defense modeling? These disciplines have also had to confront the limitations of modeling the rates of rare and damaging – even catastrophic – events. A major conceptual change in SESAR is that of automated separation assurance systems. Some existing responsibilities transfer from the controller – either to the pilot or to computer systems – in a progressively phased approach. The major problem for SESAR safety validation is that mixed equipage/operations within a common airspace potentially generate new and different safety issues regarding the validation of safety predictions. A potential way forward uses high-fidelity Human In The Loop Simulations (HITLS) to generate confidence in the resilience of the ATM system. The focus changes from proving safety, i.e. through traditional kinds of validation processes, to extensive resilience testing using these simulations. The aim would be to test how resilient the system is to seeded errors, penetration testing, and crash/stress testing. This would be a high cost process because of the large investments required and the need for long sequences of testing. However, these demanding processes can provide ‘justified belief’ to the decision-maker that the changed ATM system is acceptably safe.     

考虑人群拥堵的疏散出口选择行为研究及建模

出口选择是疏散过程中最重要和复杂的决策行为之一,受到空间结构、人群分布和行人认知等多方面因素的影响,为了使疏散仿真能够合理地模拟出行人的寻路和逃生过程,提出了考虑人群拥堵的出口选择模型。该模型基于多元Logit离散选择模型,考虑到影响个体疏散时间的因素以及对不同出口类型的偏好。研究发现个体的疏散时间随着拥堵人群位置的不同而有所差异,从而以决策者而不是出口的角度定义了一个计数区域来估计影响决策者排队时间的人数。并且研究出口重新选择的触发条件和程序。将出口选择模型和程序引入BuildingEXODUS软件进行疏散仿真,通过对比显示,仿真结果更符合真实情况,特别是在具有障碍物和人群分布不均匀的场景中更显优势。     

Perceptions of safety on a shared road: Driving,cycling, or walking near an autonomous vehicle

Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.     

Characteristics of vehicles driven by teens and adults killed in crashes, 2013–2017

Introduction: Teen drivers experience higher crash risk than their experienced adult counterparts. Legislative and community outreach methods have attempted to reduce this risk; results have been mixed. The increasing presence of vehicle safety features across the fleet has driven fatality numbers down in the past decades, but the disparity between young drivers and others remains. Method: We merged Fatality Analysis Reporting System (FARS) data on fatal crashes with vehicle characteristic data from the Highway Loss Data Institute (HLDI). The analysis compared the vehicle type, size, age, and the presence of select safety features in vehicles driven by teens (ages 15–17 years) and adult drivers (ages 35–50 years) who were killed in crashes from 2013 to 2017. Results were compared with a similar analysis conducted on data from 2007 to 2012. Results: Teen drivers were more likely than their adult counterparts to be killed while driving older, smaller vehicles that were less likely to have the option to be equipped with side airbags. Discussion: Teenage drivers remain more likely to be killed while driving older, smaller vehicles than adult drivers. Parents and guardians are mainly responsible for teen vehicle choice, and should keep vehicle size, weight, and safety features in mind when placing their teen in a vehicle. Practical Application: These findings can help guide safer vehicle choice for new teen drivers.     

BLEVE: Safety distances estimation by simple models based on the Jakob number

One of the most important points in the design of inherently safe processes is to estimate reliable distances among process units at preliminary stages of the plant project to minimize losses and damages caused by the potential occurrence of technological accidents. Therefore, in this paper the achievement of simple, general, dimensionless and reliable equations (Simple Dimensionless Models SDMs) for the direct estimation of safety distances considering the occurrence of BLEVE (Boiling Liquid Expanding Vapour Explosion) event, is proposed. The developed models directly relate safety distances with critical design/operation variables (involved substance, vessel volume, target vulnerability and explosion temperature), which are easily accessible at early stages of the plant project. SDMs are achieved by analysing the influence of these simple variables on the safety distances, which are estimated using a selected rigorous model (Reference Model R_fM). This task is simplified by the incorporation of the Jakob Number as an input variable, allowing to obtain dimensionless models and simultaneously an adequate representation of the explosion conditions and the involved substances. As result, the achieved SDMs demonstrate a particularly good fit with respect to the R_fM estimations and, at the same time, reliability and versatility. As it is shown in the analysed study cases (involving critical decision variables for the process design and the system safety), the SDMs prove to be also accurate, general, and easily incorporable into more complex optimization problems (QRA analysis, design of emergency plans, safety distance estimation to minimize the probability of domino effects, optimal layout designs, among others).     

Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones

Introduction: In this paper, we present machine learning techniques to analyze pedestrian and bicycle crash by developing macro-level crash prediction models. Methods: We collected the 2010–2012 Statewide Traffic Analysis Zone (STAZ) level crash data and developed rigorous machine learning approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To our knowledge, this is the first application of DTR models in the burgeoning macro-level traffic safety literature. Results: The DTR models uncovered the most significant predictor variables for both response variables (pedestrian and bicycle crash counts) in terms of three broad categories: traffic, roadway, and socio-demographic characteristics. Additionally, spatial predictor variables of neighboring STAZs were considered along with the targeted STAZ in both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the model comparison results discovered that the prediction accuracy of the spatial DTR model performed better than the aspatial DTR model. Finally, the current research effort contributed to the safety literature by applying some ensemble techniques (i.e. bagging, random forest, and gradient boosting) in order to improve the prediction accuracy of the DTR models (weak learner) for macro-level crash count. The study revealed that all the ensemble techniques performed slightly better than the DTR model and the gradient boosting technique outperformed other competing ensemble techniques in macro-level crash prediction models.     

A conceptual framework to understand the role of built environment on traffic safety

IntroductionMany U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics. Methods: Three years (2011–2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data. Results: Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less. Conclusions: This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.     

Longitudinal safety impacts of cooperative adaptive cruise control vehicle's degradation

Introduction: The adaptive cruise control (ACC) and cooperative ACC (CACC) systems are critical parts of self-driving vehicles. The ACC vehicles detect front vehicle' information via vehicle-mounted sensors and make longitudinal reactions automatically, while CACC vehicles enhance the performance by vehicle-to-vehicle (V2V) wireless communication. However, CACC vehicles may abruptly degrade to ACC mode in reality due to various reasons, including communication failures, driver manipulations, and cyber-attacks. The sudden degradation will definitely bring negative influences on safety. Method:This study quantitatively evaluated the longitudinal safety impacts of vehicles' degradation in a CACC fleet based on microscopic simulations. The realistic CACC and ACC models proposed by the California Partners for Advanced Transit and Highways (PATH) were used for simulation experiments. The time integrated time-to-collision (TIT) was measured to quantify the collision risks. Extensive simulations were conducted via a fleet of 10 CACC vehicles and speed profiles of vehicles in different scenarios were compared. Key factors, including the leading vehicle's deceleration rate, the number of vehicles between degraded vehicles (NVDVs), threshold of TTC, and visibility were also examined via sensitivity analyses. Results and conclusions: Simulation results indicate that degradation has significant negative influences on longitudinal safety of degraded vehicles under the driving state of deceleration. Degradation at middle positions in a CACC fleet, such as fourth and fifth positions, is much safer than that at others. Moreover, nonadjacent degradation is much riskier than adjacent degradation at the front positions of a fleet. NVDVs can bring inverse impacts on safety with different degradation positions. Speed profiles imply that the hysteresis of degraded vehicles' speed control is the major reason for high collision risks. Practical applications: Appropriately, hierarchical countermeasures have the potential to reduce the longitudinal safety impacts of degradation. Findings of this study can contribute to determining the applicable length of CACC fleets.     

Can level of safety climate predict level of orientation toward safety in a decision making task?

Establishing the relationship between level of safety climate and safety performance is a current challenge. This work examines the relationship between level of safety climate and orientation toward safety in the decision making process and choice. Alternatively, this work seeks to answer the question of whether level of safety climate can predict safety-oriented decision making. A generalized safety climate questionnaire and a decision making simulation are utilized to examine this relationship. The results indicate that level of safety climate is not a significant predictor of the decision process; however, it was found to be a significant predictor of the selection of safer choices.     

Speed choice versus celeration behavior as traffic accident predictor

INTRODUCTION: The driver celeration behavior theory predicts that this variable is superior to all other variables as a predictor of individual traffic accident involvement, including the ever-important speed parameter. The study was undertaken to test this prediction. Also, it was expected that most variables would associate fairly strongly. METHOD: The use of speed choice as a predictor of individual traffic accident record was discussed, and four different variants of this variable (maximum, net mean, gross mean, and standard deviation of speed) identified. These variables were then compared to celeration behavior as predictors of accident record of bus drivers in the same set of data. RESULTS: Celeration behavior was found to be slightly superior, in accordance with the prediction made from the driver celeration behavior theory, although the differences were not significant. Furthermore, the predictor variables were found to associate fairly strongly between themselves, with the exception of gross mean speed, and to have fair stability over time, especially when aggregated. CONCLUSIONS: These results tentatively confirm some of the predictions made from the driver celeration behavior theory. As the results for accidents were in the expected direction, but not significant, and the maximum speed variable may have suffered from a ceiling effect, the conclusion is provisional. Impact on industry: The correlations found were strong enough to warrant the use of celeration behavior as a predictive variable for transportation companies in their safety work.     

Simulating the relative influence of tire,vehicle and driver factors on forward collision accident rates

Introduction: There is currently a strong focus within the automotive industry centered on traffic safety, with topics such as distracted driving, accident avoidance technologies, and autonomous vehicles. These papers tend to focus on the possible improvements from a single factor. However, there are many factors that are present in each accident, and it is important to understand the influence of each factor on the relative accident risk in order to identify the most effective approaches for improving driver safety. Rear-end accidents tend to be the most common accident type with approximately 1.8 M cases, or 31% of all accidents, in 2012, according to NHTSA. Of the rear-end accident scenarios, approximately 18–23% occur on wet surfaces. Method: A Monte Carlo Forward Collision Simulation models the conditions of a wet rear-end accident and estimates the relative impact of various vehicle collision parameters. The model takes distributions of these parameters as inputs, and outputs a risk of collision relative to a known reference case. The parameters that can be studied include: tire grip level, road grip level, vehicle velocity, following distances, and the presence of vehicle technologies (ABS, FCW & AEB). Distributions of some of these parameters have been improved thanks to Naturalistic Driving Study data from SHRP2. Results: This study shows that these vehicle systems have a large impact on safety and can change the amount of influence attributed to other parameters such as tire grip levels. As the use of automated vehicle systems expands, so will the influence of tire grip performance levels on collision risks. Practical Applications: It is more important than ever for consumers and auto manufacturers to consider tire performance levels. Therefore, the tire industry should continue to focus on wet grip as a key performance related to safety and should strive to continue to improve tire performance.     

Occupational injury rates among Norwegian farmers: A sociotechnical perspective

Introduction: This study addressed relative injury risk among Norwegian farmers, who are mostly self-employed and run small farm enterprises. The aim was to explore the relative importance of individual, enterprise, and work environment risks for occupational injury and to discuss the latent conditions for injuries using sociotechnical system theory. Method: Injury report and risk factors were collected through a survey among Norwegian farm owners in November 2012. The response rate was 40% (n = 2,967). Annual work hours were used to calculate injury rates within groups. Poisson regression using the log of hours worked as the offset variable allowed for the modeling of adjusted rate ratios for variables predictive of injury risk. Finally, safety climate measures were introduced to assess potential moderating effects on risk. Results: Results showed that the most important risk factors for injuries were the design of the workplace, type of production, and off-farm work hours. The main results remained unchanged when adding safety climate measures, but the measures moderated the injury risk for categories of predominant production and increased the risk for farmers working with family members and/or employees. An overall finding is how the risk factors were interrelated. Conclusions: The study identified large structural diversities within and between groups of farmers. The study drew attention to operating conditions rather than individual characteristics. The farmer’s role (managerial responsibility) versus regulation and safety climate is important for discussions of injury risk. Practical Applications: We need to study sub-groups to understand how regulation and structural changes affect work conditions and management within different work systems, conditioned by production. It is important to encourage actors in the political-economic system to become involved in issues that were found to affect the safety of farmers.     

Modeling drivers’ reaction when being tailgated: A Random Forests Method

Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles’ reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle’s reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers’ reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.     

The effects of left-turn traffic-calming treatments on conflicts and speeds in Washington,DC

Introduction: Left-turning vehicles pose considerable safety risks to pedestrians at intersections. Left-turn traffic-calming treatments are designed to slow left-turn traffic. This study examined the effects of one type of left-turn calming, the hardened-centerline treatment, on the numbers of conflicts between left-turning vehicles and pedestrians and left-turn speeds in Washington, DC. Method: Numbers of conflicts between left-turning vehicles and pedestrians, as well as left-turn speeds, were collected at selected intersections in Washington, DC, where the hardened centerline was installed, as well as at control intersections in the city where no treatment was installed, before and after installation. Poisson regression evaluated the change in numbers of conflicts associated with the hardened-centerline treatment. The effect of the treatment on left-turn speeds was estimated by a log-linear regression model, and the effect on the odds of left-turning vehicles exceeding 15 mph was estimated by a logistic regression model. Results: The treatment was associated with a 70.5% reduction in conflicts between left-turning vehicles and pedestrians, a 9.8% reduction in mean left-turn speeds, and a 67.1% reduction in the odds of left-turning vehicles exceeding 15 mph. All the reductions were statistically significant. Conclusions: The study demonstrates that the hardened-centerline treatment can reduce conflicts between left-turning vehicles and pedestrians, and slow down left-turn traffic at intersections. Practical applications: The treatment should be added to the toolbox for communities looking to improve pedestrian safety at intersections.     

Preferred modes of travel among older adults: What factors affect the choice to walk instead of drive?

Introduction

There are many factors that influence older adults' travel choices. This paper explores the associations between mode of travel choice for a short trip and older adults' personal characteristics.

Methods

This study included 406 drivers over the age of 64 who were enrolled in a large integrated health plan in the United States between 1991 and 2001. Bivariate analyses and generalized linear modeling were used to examine associations between choosing to walk or drive and respondents' self-reported general health, physical and functional abilities, and confidence in walking and driving.

Results

Having more confidence in their ability to walk versus drive increased an older adult's likelihood of walking to make a short trip by about 20% (PR = 1.22; 95% CI: 1.06-1.40), and walking for exercise increased the likelihood by about 50% (PR = 1.53; 95% CI = 1.22-1.91). Reporting fair or poor health decreased the likelihood of walking, as did cutting down on the amount of driving due to a physical problem.

Discussion

Factors affecting a person's decision to walk for exercise may not be the same as those that influence their decision to walk as a mode of travel. It is important to understand the barriers to walking for exercise and walking for travel to develop strategies to help older adults meet both their exercise and mobility needs. Impact on Industry: Increasing walking over driving among older adults may require programs that increase confidence in walking and encourage walking for exercise.