Missouri-Specific Crash Prediction Model for Signalized Intersections

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

Application of Bayesian informative priors to enhance the transferability of safety performance functions

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

A Study of Safety Impacts of Different Types of Driveways and their Density

Crash prediction is a suitable alternative to an unreliable accident record for the purpose of conducting traffic safety studies. This paper's goal is to find the impact of relative delay rate on the rate of crashes at arterial roads in the city of Karbala. The study area is Fatima Al-Zahraa Street, which is an urban arterial with a length of 1.40 km. The traffic volume data had been calculated manually using video camera recordings for five days (Jan 25th to 29th, 2023) with an average of 16 hours per day. A simulation model was run using PTV VISSIM software for the purpose of calculating the relative delay rate and service level. The crash rate was predicted using the Highway Safety Manual (HSM). Through the use of linear regression analysis, it was possible to determine a linear link between the relative delay rate and the rate of crashes that occurred during peak hours, and this relationship is consistent with previous literature and research.

Predicting highway safety performance in quantitative terms on the basis of a set of highway geometric design and traffic control features is the core of the ongoing Highway Safety Manual (HSM) development. While great efforts have been made to develop safety performance models for different types of highways, evaluating the applicability of the models with crash data from states that were not used in the original model development is equally important. This step is critical in future HSM applications because of many noticeable differences in crash data recording practice and driving behaviors across the various states. Potential application concerns need to be evaluated fully and addressed before the manual is officially published. This paper presents the application experience of the safety prediction models and methodology for segments of two-lane rural roads as proposed for inclusion in Part III of the HSM. The study used data for state routes in Louisiana. The results indicate that the prediction models performed reasonably well: there were small differences between the observed and predicted crash frequencies, with calibration parameters applied, for all segments on rural two-lane highways in Louisiana. The results at different analysis levels (from individual segments to a network of segments) are summarized, and several application issues such as the level of effort versus degree of accuracy, simplification of the calibration process, and the need for a reliability measurement are discussed.

Predicting highway safety performance in quantitative terms on the basis of a set of highway geometric design and traffic control features is the core of the ongoing Highway Safety Manual (HSM) development. While great efforts have been made to develop safety performance models for different types of highways, evaluating the applicability of the models with crash data from states that were not used in the original model development is equally important. This step is critical in future HSM applications because of many noticeable differences in crash data recording practice and driving behaviors across the various states. Potential application concerns need to be evaluated fully and addressed before the manual is officially published. This paper presents the application experience of the safety prediction models and methodology for segments of two-lane rural roads as proposed for inclusion in Part III of the HSM. The study used data for state routes in Louisiana. The results indicate that the prediction models performed reasonably well: there were small differences between the observed and predicted crash frequencies, with calibration parameters applied, for all segments on rural two-lane highways in Louisiana. The results at different analysis levels (from individual segments to a network of segments) are summarized, and several application issues such as the level of effort versus degree of accuracy, simplification of the calibration process, and the need for a reliability measurement are discussed.

Calibration and development of safety performance functions for two-way stop-control intersections on rural two-lane highways in Louisiana

More than 25% of all roadway fatalities in the U.S. are associated with a horizontal curve, and the average crash rate for horizontal curves is about three times that of other types of highway segments. A focus on horizontal curves can prove to be a cost-effective approach to reducing safety issues. Accurate crash prediction models (CPMs) on horizontal curves can help roadway safety practitioners assess and prioritize safety improvements. Although many CPMs have been developed, there are no extant studies that compare different CPMs on a singular, real-world, large-scale, and comprehensive dataset to evaluate their capability for horizontal curve crash prediction. This study critically evaluated commonly used CPMs, including multiple linear regression (MLR), Poisson regression (PR), negative binomial regression (NBR), support vector machine, random forest (RF), and fully connected neural network (FCNN) models, on rural curves extracted from 18,000 centerline miles of Georgia, U.S.’s state-maintained routes and statewide historical crash data set from 2013 to 2021. Results show PR and NBR models outperform MLR by around 6%. Moreover, the FCNN and RF models further improved this performance by around an additional 6% over the PR and NBR models. Overall, machine learning (ML)-based models outperform generalized linear regression models. The results prove ML-based models can be recommended to transportation agencies to forecast horizontal curve crashes more accurately.

This study tries to develop a Korea highway safety evaluation proto type model for quantified safety evaluation. We develop a base model and crash modification factors (CMFs) through reviewing the procedures and methodologies in Highway Safety Manual. At the same time, we try to carefully consider collectable data and roadway environment of Korea that are different from the USA. CMFs are developed through models and expert's judgement. Developed CMFs are for driveway density, pedestrian crosswalk, existence of median, terrain types, and land-use types. Also, verification of the developed base model and CMFs were performed using two routes of Korea's rural two-lane highways while comparing with roadway safety design synthesis and Crash Prediction Module (CPM) of Interactive Highway Safety Design Model (IHSDM). Applicability analysis was then performed in two aspects of predictability and adaptability of the proto type model. As a result, CMFs and predicted accident estimates of CPM are similar with the developed Korea proto type model. However, the base model and CMFs of the developed proto type for Korea are more precise than the others when evaluating sections where the number of actual accidents is higher. It is found that considering CMF for land-use type is justifiable when developing a Korea highway safety evaluation proto type model. This study validates the possibility of developing a full scale of Korea highway safety evaluation model.

New highway geometric design methods for minimizing vehicular fuel consumption and improving safety

Crash prediction based on random effect negative binomial model considering data heterogeneity

Effect of increasing the freeway posted speed limit on entry ramp speed-change lane crash frequency

Protected-permitted left-turn (PPLT) control strategies at signalized intersections are used to improve operational efficiency; however, only limited research has been done in modeling the delay for such phasing. The development and validation of uniform delay models for exclusive left-turn lanes with PPLT phases are presented. The proposed models are based on the arrival-based delay method, which directly considers the effects of platoon arrival rate and platoon duration in model development. Two arrival flow rates (one for platoon and one for nonplatoon) and two saturation flow rates (one for protected phase and one for permitted interval) were used to develop the delay models for PPLT. Results of a comparison of the control delays from the proposed models and the Highway Capacity Manual models with simulation results from CorSim indicated that the proposed models estimate the control delay more accurately.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots