Rural seniors are highly dependent on their automobile to meet their trip making needs, yet the effects of aging can make access to the vehicle difficult or impossible over time.

No abstract

In 2010, the Illinois Department of Transportation began implementing the flashing yellow arrow (FYA) at intersections operating with protected–permissive left-turn (PPLT) control. Research was conducted to evaluate the safety-effectiveness of FYAs at 86 intersections and 164 approaches in central Illinois. The effectiveness evaluation was performed with 3 years of before-and-after FYA installation crash data and the empirical Bayes method. In the before condition, the left-turn signals operated with a circular green display indicating the permissive interval of PPLT control using a five-section signal head. In the after condition, the FYA replaced the circular green display for the permissive interval of PPLT with a four-section signal head. Supplemental traffic signs were mounted on the mast arm adjacent to the left-turn signal at over half of the FYA installations. The results of the comprehensive safety evaluation of the FYA for PPLT control are presented. Analyses were also performed to assess the effects of the FYA supplemental signs and the effects of the FYA overall on two subsets of at-fault drivers: older drivers (age 65+) and younger drivers (age 16 to 21). The resulting mean crash modification factors for the targeted crash types ranged from 0.589 to 0.714. The findings of this research support the continued use of FYAs for PPLT control to improve safety at signalized intersections in central Illinois.

Evolutionary graph analytics have attracted attention from many research communities with the main purpose of understanding the changing pattern of real-world networks through evolutionary analysis of graph metrics and dynamic interactions between entities. Graphs of real-world networks evolve as new nodes and edges continually appear and disappear in the structure but, more importantly, their metrics such as density, average path length and network diameter also evolve. Uncovering and understanding hidden patterns in an evolving network requires evolutionary analysis of the network over different temporal resolutions. Evolutionary graph analytics have been explored for use in different types of networks including web citation and co-authorship networks [1-4], online social networks [5-10], biology and disease networks [11-14], as well as in communication networks [15-20]. All networks do not evolve at the same rate; some

Car-based volunteer driver programs have emerged as important providers of transportation in many rural and low-density locations in which automobile dependence is high and transit access is negligible. The degree of this importance is not known, yet it can be expected that demand for these services will increase commensurate with the transportation needs of the aging population. The challenge remains that there is limited technical guidance for existing and new programs to anticipate and respond to the anticipated increases in travel demand from an aging population. It is also unclear how these programs can fit into transportation engineering and planning. This paper presents aggregate results of travel data collected for one year using a uniform data reporting approach by seven car-based volunteer driver programs in New Brunswick, Canada. Users 65 years and older accounted for 49–65% of members, and programs on average made 2.0 stops (trip end) per drive (trip chain), drove 39 km per stop, 61 km per drive, and had an occupancy of 1.5 riders per drive. A total of 88% of drives had 3 or fewer stops, and one drive was cancelled for every 10–11 offered. A total of 49.3% of all stops (excluding home) were for health purposes, with 20.7% for education/work. Seasonal, daily, and hourly trends were observed. Volunteers in groups with <100 riders provided more hours/drive but fewer hours/year than those in larger groups. These data can provide a basis for a more sophisticated approach to program planning and could be useful in forecasting for future demand.