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Out-of-home mobility has been identified as essential for older adults to access activities in their community that keep them healthy and well. As individuals grow older, they are more likely to experience medical conditions and other health-related changes that can affect their ability to drive. Balancing older peoples’ continued mobility against their own and others’ safety has been always a challenge. Therefore, it is important to understand elderly people’s everyday mobility issues and challenges/factors associated with driving cessation. This paper aims to identify and quantify factors contributing to older adults’ satisfaction with their everyday mobility using ordered probit (OP) modeling. Its underlying objective is to provide better understanding of factors that may influence older adults to stop driving. The data used in the analysis was obtained through an online survey of 1,000 Canadians aged 65+. The results of the OP model indicate that several factors affect older adults’ satisfaction with their everyday mobility including driving a car to reach destinations, ability to drive alone (without help from others), experiencing difficulties/challenges with driving, having a physical health issue restricting the ability to walk, fear of falling while walking, and inability to visit local places as often as desirable. The findings also suggest that factors influencing older drivers’ decisions to retire from driving (for those already retired from driving) are different from those perceived by people still driving. The study’s results provide insights about policies and countermeasures that can be implemented to improve the safety and mobility of the aging population.

Accessible and safe mobility is critical for those aged 65 years and older to maintain their health, quality of life, and well-being. Being able to move beyond one’s home and participate in activities in older adulthood requires consideration of both transportation needs and preferences. This paper aims to address a gap in evidence with respect to understanding factors that can affect older adults’ perceptions and willingness to use autonomous vehicles. In addition, it examines how these factors compare with those of younger adults to better understand the potential implications of this technology on mobility and quality of life. Using responses of those aged 65+ to a national survey of Canadians, structural equation modeling (SEM) was used to identify and quantify factors significantly associated with older adults’ willingness to use autonomous vehicles. The SEM results suggest that factors such as using other modes of transit (e.g., sharing rides as passenger, bicycle, public transit, commuter rail, ride and car sharing) as well as distance traveled by automobile, income, gender (being male), and living in urban areas, were all positively associated with older adults’ perceptions of using autonomous driving features. The findings also suggest that older Canadians are more concerned about autonomous vehicles than younger Canadians. This study provides valuable insights into factors that can affect the preferences of Canadians when it comes to autonomous technology in their automobiles. Such results can inform the way in which transportation systems are designed to ensure the needs of users are considered across both age and ability.

Red light running crashes have always been a major concern to both researchers and practitioners. In Abu Dhabi, United Arab Emirates, the red light crashes contribute to about 60% of the total severe crashes at signalized intersections. This fact called for a project in 2013, for which new red light cameras (RLCs) were planned to cover 150 intersections in Abu Dhabi City. With the completion of the second stage of this project, 36 signalized intersections have been covered either fully (all intersection approaches) or partially (only major approaches) with a total of 108 RLCs. These RLCs are now in place and have been fully functioning since January 2014. The primary aim of this study was to examine the impact of the installed RLCs on the traffic safety performance in the city. To waive the effects of other factors, two groups were created; they include a study group (36 signalized intersections with RLCs) and a comparison group (36 similar intersections without RLCs). The results indicate a reduction of about 40% to 52% for the number of crashes and 48% to 60% for the number of fatalities and injuries. The majority of at-fault drivers were young male drivers from Asian countries with read-and-write educational level. Moreover, crashes, fatalities, and injuries were modeled using the negative binomial technique. Among 13 tested variables describing the traffic exposure, traffic violations, number of lanes, and coverage of intersections by RLCs, only the number of lanes was found significant in two significant models.

Red light running crashes have always been a major concern to both researchers and practitioners. In Abu Dhabi, United Arab Emirates, the red light crashes contribute to about 60% of the total severe crashes at signalized intersections. This fact called for a project in 2013, for which new red light cameras (RLCs) were planned to cover 150 intersections in Abu Dhabi City. With the completion of the second stage of this project, 36 signalized intersections have been covered either fully (all intersection approaches) or partially (only major approaches) with a total of 108 RLCs. These RLCs are now in place and have been fully functioning since January 2014. The primary aim of this study was to examine the impact of the installed RLCs on the traffic safety performance in the city. To waive the effects of other factors, two groups were created; they include a study group (36 signalized intersections with RLCs) and a comparison group (36 similar intersections without RLCs). The results indicate a reduction of about 40% to 52% for the number of crashes and 48% to 60% for the number of fatalities and injuries. The majority of at-fault drivers were young male drivers from Asian countries with read-and-write educational level. Moreover, crashes, fatalities, and injuries were modeled using the negative binomial technique. Among 13 tested variables describing the traffic exposure, traffic violations, number of lanes, and coverage of intersections by RLCs, only the number of lanes was found significant in two significant models.

Traffic crashes involving heavy trucks long have been a major concern in the field of traffic safety because of their great effect on accident severity. The emirate of Abu Dhabi, capital of the United Arab Emirates, features a unique situation: several roads designed mainly for truck movement. Even though those roads were constructed more than 10 years ago to decrease the severity of truck-related crashes, no prior studies have examined their effects on traffic safety improvements. The goals of this study were to understand better the nature, characteristics, and causes of heavy truck crashes occurring in Abu Dhabi; to identify the factors associated with crash severities; and to examine the probability of truck crashes involving fatalities on truck roads versus on mixed-vehicle roads. Data were analyzed from a sample of 1,426 heavy truck–related crashes with reported fatalities or injuries that occurred in Abu Dhabi between 2007 and 2013. First, conditional distributions, two-way analysis, and odds ratios were performed. Second, ordered probit and structural equation models were developed. Results indicated that the likelihood of truck crashes involving fatalities was 35% higher on truck roads than on mixed-vehicle roads. In addition, findings showed that human error, driver education, location, road type, and road speed variables were significant in affecting the severity of heavy truck–related crashes. Finally, practical suggestions on how to reduce the number of heavy truck–related crashes in Abu Dhabi are presented and discussed.

Traffic crashes involving heavy trucks long have been a major concern in the field of traffic safety because of their great effect on accident severity. The emirate of Abu Dhabi, capital of the United Arab Emirates, features a unique situation: several roads designed mainly for truck movement. Even though those roads were constructed more than 10 years ago to decrease the severity of truck-related crashes, no prior studies have examined their effects on traffic safety improvements. The goals of this study were to understand better the nature, characteristics, and causes of heavy truck crashes occurring in Abu Dhabi; to identify the factors associated with crash severities; and to examine the probability of truck crashes involving fatalities on truck roads versus on mixed-vehicle roads. Data were analyzed from a sample of 1,426 heavy truck–related crashes with reported fatalities or injuries that occurred in Abu Dhabi between 2007 and 2013. First, conditional distributions, two-way analysis, and odds ratios were performed. Second, ordered probit and structural equation models were developed. Results indicated that the likelihood of truck crashes involving fatalities was 35% higher on truck roads than on mixed-vehicle roads. In addition, findings showed that human error, driver education, location, road type, and road speed variables were significant in affecting the severity of heavy truck– related crashes. Finally, practical suggestions on how to reduce the number of heavy truck–related crashes in Abu Dhabi are presented and discussed.