MIXED-USE SAFETY ON RURAL FACILITIES IN THE PACIFIC NORTHWEST: Consideration of Vehicular, Non-Traditional, and Non-Motorized Users

In the United States, one in 12 households do not own a personal automobile and approximately 13% of those who are old enough to drive do not. Trips by these individuals are being made in one of many other possible modes, creating the need to “share space” between many forms of travel. The goal of this project is to: improve safety and minimize the dangers for all transportation mode types while traveling in mixed-use environments on rural facilities through the development and use of engineering and education safety measures. To that end, this report documents three specific efforts by the project team. First, a comprehensive literature review of mixed-use safety issues with consideration of non-motorized and non-traditional forms of transportation. Second, a novel analysis of trauma registry data. Third, development, execution and analysis of the Pacific Northwest Transportation Survey geared toward understanding safety perceptions of mixed-use users. Most notably, findings indicate that ATVs (and similar non-traditional-type vehicles) are used on or near roads 24% of the time and snowmachines are used on or near roads 23% of the time. There are significantly more (twice as many) ATV-related on-road traumas in connected places than isolated places in Alaska and three times more traumas in highway connected places than in secondary road connected places. Comparably, bicycles had 449 on-road traumas between 2004 and 2011 whereas ATVs had 352 on-road traumas. Users of all modes who received formalized training felt safer in mixed-use environments than those who reported having no training at all

Reaching Out to Tribal Communities: Lessons Learned and Approaches to Consider

When transportation safety decision-making is desired, the involvement and engagement with a community is essential. A streamlined delivery of a project or program is more likely to occur when active dialogue and an exchange of ideas occurs in advance and occurs frequently. This is particularly important in tribal communities, who value sustained relationships and represent the focus population of this study. The research team, on six separate occasions, met with local and regional tribal leaders to explore and discuss transportation safety needs within and outside tribal communities, as well as discern the recommended approaches to foster ongoing dialogue about these needs. In all cases these discussions closely correlated with existing research studies or activities; transportation safety and equity is not seen as separate from other tribal foci and community needs. Specific recommendations to consider, in no particular order, included the following: invest respectfully enough time for people to talk; tribes think long-term and consider the impact of any decision from a long-term viewpoint so an iterative process and re-sharing of ideas is critical; the power of decision is in the hands of the tribe and its members; do not lump tribes together as each tribe is sovereign and unique and every community should be expected to think differently; all tribes are unique as is the environmental and social context; to disseminate information widely and iteratively, do so when there is a large group or event; be sure to understand the Tribal governance, decision making, and organizational structure; know who is the tribal Chairman or Chairwoman; and develop an emic and etic understanding of the community

SQUASH: Simple QoS-Aware High-Performance Memory Scheduler for Heterogeneous Systems with Hardware Accelerators

Modern SoCs integrate multiple CPU cores and Hardware Accelerators (HWAs) that share the same main memory system, causing interference among memory requests from different agents. The result of this interference, if not controlled well, is missed deadlines for HWAs and low CPU performance. State-of-the-art mechanisms designed for CPU-GPU systems strive to meet a target frame rate for GPUs by prioritizing the GPU close to the time when it has to complete a frame. We observe two major problems when such an approach is adapted to a heterogeneous CPU-HWA system. First, HWAs miss deadlines because they are prioritized only close to their deadlines. Second, such an approach does not consider the diverse memory access characteristics of different applications running on CPUs and HWAs, leading to low performance for latency-sensitive CPU applications and deadline misses for some HWAs, including GPUs. In this paper, we propose a Simple Quality of service Aware memory Scheduler for Heterogeneous systems (SQUASH), that overcomes these problems using three key ideas, with the goal of meeting deadlines of HWAs while providing high CPU performance. First, SQUASH prioritizes a HWA when it is not on track to meet its deadline any time during a deadline period. Second, SQUASH prioritizes HWAs over memory-intensive CPU applications based on the observation that the performance of memory-intensive applications is not sensitive to memory latency. Third, SQUASH treats short-deadline HWAs differently as they are more likely to miss their deadlines and schedules their requests based on worst-case memory access time estimates. Extensive evaluations across a wide variety of different workloads and systems show that SQUASH achieves significantly better CPU performance than the best previous scheduler while always meeting the deadlines for all HWAs, including GPUs, thereby largely improving frame rates

Bootstrapping Cointegrating Regressions

In this paper, we consider bootstrapping cointegrating regressions. It is shown that the method of bootstrap, if properly implemented, generally yields consistent estimators and test statistics for cointegrating regressions. We do not assume any specific data generating process, and employ the sieve bootstrap based on the approximated finite-order vector autoregressions for the regression errors and the firrst differences of the regressors. In particular, we establish the bootstrap consistency for OLS method. The bootstrap method can thus be used to correct for the finite sample bias of the OLS estimator and to approximate the asymptotic critical values of the OLS-based test statistics in general cointegrating regressions. The bootstrap OLS procedure, however, is not efficient. For the efficient estimation and hypothesis testing, we consider the procedure proposed by Saikkonen (1991) and Stock and Watson (1993) relying on the regression augmented with the leads and lags of differenced regressors. The bootstrap versions of their procedures are shown to be consistent, and can be used to do inferences that are asymptotically valid. A Monte Carlo study is conducted to investigate the finite sample performances of the proposed bootstrap methods.