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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 reasonabl...
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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 truckinvolved 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.
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Severe injury involvements on arterial roads account for a quarter of the total severe injuries reported statewide. Crash severity analysis was conducted and consisted of six road entity models and twenty crash type models. The da...
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Severe injury involvements on arterial roads account for a quarter of the total severe injuries reported statewide. Crash severity analysis was conducted and consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on high-speed multilane roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate unsignalized intersection crash analysis. Injury severity from rear-end and fixed object crashes was best modeled by combined analysis of pure segment and unsignalized intersection crashes. The most important contributing factors found in the overall analysis included driver-related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle-related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off-roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway-related variables included speed limit, adt per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. Two additional models of crashes for urban and rural areas were successfully developed. The land use models goodness of fit was substantially better than any other combination by road entity or the overall model.
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This report presents the analysis conducted to identify the factors that contribute to severe and fatal crash occurrence on multilane corridors. The authors preliminary investigation using simultaneous ordered probit model provide...
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This report presents the analysis conducted to identify the factors that contribute to severe and fatal crash occurrence on multilane corridors. The authors preliminary investigation using simultaneous ordered probit model provided enough evidence that a fixed influence area of intersections for all of the corridors is not justified. With the increase of an intersection's influence area, crash types that are more specific to segments get included and change the crash pattern for the overall intersection. Therefore, for investigation purposes, to treat the corridors in their entirety will result in much more insightful results than when treating the segments and intersections separately. The crash data were grouped into six major types as follows: (1) rear-end, (2) head-on, (3) angle/turning, (4) sideswipe, (5) crashes involving slow moving vehicles (e.g. cycles, mopeds, etc.), and (6) crashes involving single vehicles. Binary severity classification models were developed by using non-parametric conditional inference trees. Parameters like alcohol/drug use came out to be significant across all crash types and clusters. Lane changing on corridors with high truck traffic was found to be risky from a severity point of view. Poor pavement conditions and high permitted speed limits increased the likelihood of severe rear-end crashes. Non-use of safety equipment also increased the severity level provided the crash had occurred. Based on the results of the overall investigation certain recommendations were made taking the 4 Es (Engineering, Education, Enforcement and Emergency Management) into consideration.
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This study examines the safety effects of the improvements made on multi-lane arterials. The improvements were divided into two categories: (1) corridor level improvements, and (2) intersection improvements. Empirical Bayes method...
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This study examines the safety effects of the improvements made on multi-lane arterials. The improvements were divided into two categories: (1) corridor level improvements, and (2) intersection improvements. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the improvement projects. The Safety Performance Functions (SPFs) used in this study are negative binomial crash frequency models that use the information on average daily traffic (adt), length of the segments, speed limit, and number of lanes for corridors. For intersections, the explanatory variables used are adt, number of lanes, speed limit on major road, and number of lanes on the minor road. The results of the analysis show that the resulting changes in safety following corridor level improvements vary widely. The overall effectiveness of each improvement type was positive in terms of reducing total, severe and rear-end crashes, except for roadway resurfacing projects, where the total number of crashes slightly increased. In all it can be concluded that FDOT is doing a good job in selecting the sites for treatment and it is very successful in improving the safety of the sections being treated although the main objective(s) of the treatments are not necessarily safety related.
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This part of the study examines the locations of high trends of severe crashes (incapacitating and fatal crashes) on multilane corridors in the state of Florida at two levels, county level and roadway level. The Geographic Informa...
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This part of the study examines the locations of high trends of severe crashes (incapacitating and fatal crashes) on multilane corridors in the state of Florida at two levels, county level and roadway level. The Geographic Information System (GIS) tool, which is used frequently in traffic safety research, was utilized in this study to identify those locations. At the roadway level, seven counties were chosen for the analysis based on their high severe crash trends, metropolitan size and geographical location. Several GIS maps displaying the safety level of multilane corridors in the seven counties were generated. The GIS maps were based on a ranking methodology that we developed and which evaluated the safety condition of road segments and signalized intersections separately. The GIS maps were supported by tables which provided the milepoints of the most hazardous locations on the roadways. The results of the roadway level analysis found that the worst corridors were located in Pasco, Pinellas and Hillsborough counties.
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It is shown that only a moderate MSI/ASI relationship appears to exist and that the MSI is probably not a severity-discerning characteristic of full-seals tests. Low, constant AS1 values over the range of MSI values indicate that ...
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It is shown that only a moderate MSI/ASI relationship appears to exist and that the MSI is probably not a severity-discerning characteristic of full-seals tests. Low, constant AS1 values over the range of MSI values indicate that guardrails are performing their intended purposes.
This volume includes technical documentation of work done in the study. Volume II contains related appendices.
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Available accident filch are used to generate a 412-accidcnt data base of guardrail impacts. This base is analyzed to develop a statistical model for predicting accident severity index (ASI) as a function of vehicle type or weight...
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Available accident filch are used to generate a 412-accidcnt data base of guardrail impacts. This base is analyzed to develop a statistical model for predicting accident severity index (ASI) as a function of vehicle type or weight, impact speed, and impact angle.
Reported full-scale test results are used to generate a 91-test data base of guardrail full-scale crash tests. Mathematical severity index (HSI) is calculated! For each test as the resultant of the reported maximum SO-ns vehicle lateral and longitudinal accelerations the statistical model is applied to each te9t to predict, the corresponding ASI. These pairs of MSl/ASI values are used to determine the relationship between the two indexes.
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