Search

The local road safety program strategy has been proven effective in reducing crashes on rural roads. In this study, local road managers were surveyed about road safety activities with special emphasis on the western counties in the oil impact region. The survey provides insight for improved local road safety program delivery and monitoring.

Annually, many injuries and fatal crashes happen on local rural roads in North Dakota. The local road safety program (LRSP) strategy has been proven effective in reducing crashes on these roads. It relies on state engagement with local stakeholders to collectively reduce crash injuries in their communities. The LRSP presents a framework for stakeholders to identify, analyze, and prioritize traffic safety improvements for their roadways. In this study, local road managers were surveyed about road safety activities with special emphasis on the western counties in the oil impact region. The results reflect current practices in local government road management. It will be a resource for growing program participation and activities by providing best practices and peer knowledge sharing. One in five counties reported applying for LRSP projects over the past five years. Comparison to a previous survey shows a notable expansion in counties adopting low-cost safety strategies. The benefits of federal and state support in instituting local road safety practices were evident in funding sources and planning activities reported by counties. Several opportunities to carry this forward in proactive safety opportunities for individual counties with specific crash-type prevention strategies and for systemwide site-based safety countermeasures were evident. The survey investigation can inform other states by providing insight regarding LRSP practices and perceptions, with regard to improvements in local road safety and the potential to grow local agencies as champions in the effort.

MPC-657, Researchers examined the impacts of COVID-19 on traffic safety in three different stages to better understand the relationship between traffic demand patterns, daily confirmed cases/deaths, state policies, and public perceptions and other factors.

MPC-458, In this study, researchers used the agent-based simulation (ABS) for modeling agricultural transportation demand. With the simulation model, geographic information systems (GIS) was utilized to collect and analyze remote sensing of agricultural crops. The researchers reviewed agent-based modeling in freight and public transportation planning to fill the gap between traditional modeling efforts and emerging needs of adopting behavioral modeling for an agricultural transportation model. The study then investigated adoption of an agent-based model for a large-scale travel demand model. The objective of the proposed model was to provide a platform to analyze grain transportation movement at the micro-level, since most industry reports lack such micro-level freight analysis while supporting regional and statewide planning.

Results identify three to seven statistically significant risk factors from among 13 selected for the models. These included: seat belt use, alcohol/drug involvement, head-on collision impact, rollover event, failure to yield/stop, weather, intersection, curve, and multiple-truck involvement. Seat belt use was a significant predictor for severe injury likelihood in all models. Failure to stop or yield, rollover event, multiple truck involvement, curves and intersections were associated with increased likelihood for severe-injury to truck drivers. Severe injury to other drivers in truck-involved crashes was associated with alcohol or drug involvement, head-on and sideswipe collisions, rollover events, weather, and distracted driving.

Trucks are a vital part of the logistics system in North Dakota. Recent energy developments have generated exponential growth in the demand for truck services. With increased density of trucks in the traffic mix, it is reasonable to expect some increase in the number of crashes. Analysis shows however, that the crash-injury risk associated with trucks cannot be explained solely with the traffic growth. Recent crash data has been analyzed to better understand characteristics and contributing factors in truck-involved crash events. Comparisons of truck-involved crashes to those not involving trucks show aspects of the crashes differ. In addition, multivariate models of three driver groups were defined, including truck drivers in multiple-vehicle crashes, other vehicle drivers in multiple-vehicle crashes, and truck drivers in single-vehicle crashes. Results reveal several predictors significantly associated with an increased likelihood for severe driver injury outcomes. Seat belt use was a significant predictor for severe injury likelihood in all models. Failure to stop or yield, rollover event, multiple truck involvement, curves and intersections were associated with increased likelihood for severe injury to truck drivers. Severe injury to other drivers in truck-involved crashes was associated with alcohol or drug involvement, head-on and sideswipe collisions, rollover event, weather and distracted driving. Findings largely were consistent with previous findings indicating some differences among driver group injury predictors. Understanding factors associated with increased likelihood for severe injury by driver group can encourage targeted interventions and countermeasures, which will them improve safety by reducing incidence of severe injury crashes involving trucks. Insight into truck crashes may allow drivers and businesses to identify areas for safety performance improvement.