A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets.

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

The changing accuracy of traffic forecasts.

Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy-traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-021-10182-8.

Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models.

OBJECTIVE: Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes. METHODS: For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers. RESULTS: Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves. CONCLUSIONS: Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.

Use of n-fold cross-validation to evaluate three methods to calculate heavy truck annual average daily traffic and vehicle miles traveled.

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.