Priyanka Alluri

Analyzing pedestrian crash injury severity at signalized and non-signalized locations

This study identifies and compares the significant factors affecting pedestrian crash injury severity at signalized and unsignalized intersections. The factors explored include geometric predictors (e.g., presence and type of crosswalk and presence of pedestrian refuge area), traffic predictors (e.g., annual average daily traffic (AADT), speed limit, and percentage of trucks), road user variables (e.g., pedestrian age and pedestrian maneuver before crash), environmental predictors (e.g., weather and lighting conditions), and vehicle-related predictors (e.g., vehicle type). The analysis was conducted using the mixed logit model, which allows the parameter estimates to randomly vary across the observations. The study used three years of pedestrian crash data from Florida. Police reports were reviewed in detail to have a better understanding of how each pedestrian crash occurred. Additionally, information that is unavailable in the crash records, such as at-fault road user and pedestrian maneuver, was collected. At signalized intersections, higher AADT, speed limit, and percentage of trucks; very old pedestrians; at-fault pedestrians; rainy weather; and dark lighting condition were associated with higher pedestrian severity risk. For example, a one-percent higher truck percentage increases the probability of severe injuries by 1.37%. A one-mile-per-hour higher speed limit increases the probability of severe injuries by 1.22%. At unsignalized intersections, pedestrian walking along roadway, middle and very old pedestrians, at-fault pedestrians, vans, dark lighting condition, and higher speed limit were associated with higher pedestrian severity risk. On the other hand, standard crosswalks were associated with 1.36% reduction in pedestrian severe injuries. Several countermeasures to reduce pedestrian injury severity are recommended.

Minimum sample sizes for estimating reliable Highway Safety Manual (HSM) calibration factors

ABSTRACT The Highway Safety Manual (HSM) assists state and local agencies in improving highway safety by moving toward statistically proven quantitative analyses. The manual recommends using Empirical Bayes (EB) method with locally derived calibration factors to predict the agencys safety performance. It recommends deriving calibration factors using randomly selected 30 to 50 roadway sites that experienced a minimum of 100 crashes per year. Given the fact that the minimum sample size is a function of sample variance, this recommendation is clearly questionable as roadway characteristics of different roadway types are likely to have different levels of homogeneity. This research used Florida data to determine the minimum sample sizes to estimate reliable calibration factors for the following three facility types: rural two-lane roads, rural multilane highways, and urban and suburban arterials. The analysis was based on the data collected from more than 7,000 miles of segments and more than 1,000 intersections in Florida. The minimum sample size was determined such that there is a high probability that the calibration factor estimated from a sample is within 5% to 10% of the actual calibration factor calculated from the entire data set. For all the site subtypes, it was found that the minimum sample size of 30 to 50 sites, as recommended by the HSM, is insufficient to achieve the desired accuracy. Moreover, the sample sizes required for estimated calibration factors to lie within 5% of the actual calibration factors is almost double the sample sizes required for estimated calibration factors to lie within 10% of the actual calibration factors. The results also showed that the generalized one-size-fits-all approach of using a sample size of 30 to 50 sites is not appropriate as different facility types require different sample sizes depending on several factors, such as the extent of data variability, population size, crash experience, and so on, to estimate reliable calibration factors.

Full Versus Simple Safety Performance Functions: Comparison Based on Urban Four-Lane Freeway Interchange Influence Areas in Florida

The empirical Bayes approach adopted in the Highway Safety Manual and the SafetyAnalyst software application require the use of safety performance functions (SPFs). SafetyAnalyst adopts a form of SPF, known as the simple SPF, that relates crash experience to traffic volume only. It is a flow-only model that is calibrated by using all sites irrespective of their base geometric conditions. Full SPFs, however, relate crash occurrence to roadway geometric characteristics in addition to traffic characteristics. This study compared the simple SPFs provided in SafetyAnalyst with full SPFs in two safety applications: crash prediction performance and identification of high-crash locations. To compare the prediction performance, the simple and full SPFs were estimated with data collected on urban four-lane freeway interchange influence areas in Florida. Models were estimated for both total crashes and fatal and injury crashes. The mean absolute deviance and the mean square prediction error were used to assess and compare the prediction performance of the two models, and the variation in ranking the high-crash locations with each model was also examined. The results showed that the two models yielded very similar performance of crash prediction and network screening. This empirical result supports the use of the flow-only SPF model adopted in SafetyAnalyst, whose development requires much less effort compared with that for full SPFs.

Estimating Annual Average Daily Traffic for Local Roads for Highway Safety Analysis

Annual average daily traffic (AADT) is a required input to the newly released SafetyAnalyst software application. Further, AADT is also required to calculate crash rates. Traditionally, AADTs are estimated by using a mix of permanent and temporary traffic counts collected in the field. Because field collection of traffic counts is expensive, it is usually performed only for major roads and only a small percentage of nonstate local roads have reliable AADT data. A method is presented to estimate AADTs for local roads by using the travel demand modeling method. A major component of the method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The generated trips are then distributed to existing traffic count sites by using a parcel-level trip distribution gravity model. The all-or-nothing trip assignment method is then applied to assign the trips between the parcels and the traffic count sites onto the local roadway network to yield estimates of AADTs. The estimated AADTs were compared with those from an existing regression-based method using actual traffic counts from Broward County, Florida. The results show that the proposed method produces significantly lower mean absolute percentage errors.

Safety Performance of G4 (1S) W-Beam Guardrails versus Cable Median Barriers on Florida's Freeways

Different roadside safety hardware including guardrails and cable barriers have been installed on freeways to prevent run-off-the-road and median crossover crashes. This article compares the safety performance of G4 (1S) strong-post W-beam guardrails and cable barriers installed in the medians on freeways in Florida. The comparison is based on the percentages of barrier and median crossovers by vehicle type and crash severity. A crash is categorized as barrier crossover crash if the errant vehicle crosses the barrier during the crash. If after crossing the barrier the errant vehicle clears the median and traverses into the opposite travel lanes, it becomes a median crossover crash. To obtain a more precise measurement of barrier performance, this study considers only crashes involving vehicles hitting a barrier. A total of 678.9 miles of freeways with G4 (1S) W-beam guardrails and 101.0 miles of freeways with cable barriers were identified. Police reports of 8,536 crashes from years 2006 to 2010 at these locations were reviewed. Z-test and odds ratio were used to compare the safety performance of cable median barriers and guardrails. Overall, guardrails performed slightly better than cable barriers in terms of barrier and median crossover crashes. However, cable median barriers were found to result in fewer severe injury crashes.

A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications

The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agencys safety performance. The models were developed using data from only a few U.S. states. To account for the effects of the local attributes and temporal factors on crash occurrence, agencies are required to calibrate the HSM-default models for crash predictions. The manual suggests updating calibration factors every two to three years, or preferably on an annual basis. Given that the calibration process involves substantial time, effort, and resources, a comprehensive analysis of the required calibration factor update frequency is valuable to the agencies. Accordingly, the objective of this study is to evaluate the HSMs recommendation and determine the required frequency of calibration factor updates. A robust Bayesian estimation procedure is used to assess the variation between calibration factors computed annually, biennially, and triennially using data collected from over 2400 miles of segments and over 700 intersections on urban and suburban facilities in Florida. Bayesian model yields a posterior distribution of the model parameters that give credible information to infer whether the difference between calibration factors computed at specified intervals is credibly different from the null value which represents unaltered calibration factors between the comparison years or in other words, zero difference. The concept of the null value is extended to include the range of values that are practically equivalent to zero. Bayesian inference shows that calibration factors based on total crash frequency are required to be updated every two years in cases where the variations between calibration factors are not greater than 0.01. When the variations are between 0.01 and 0.05, calibration factors based on total crash frequency could be updated every three years.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besags model and the Lerouxs model, in crash prediction. The Besags models, which differ from the Lerouxs models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.

Safety Impacts of Access Management Features near Roundabouts

Roundabouts can better facilitate U-turns and provide better safety, especially in reducing severe crashes, than can traditional intersections. Roundabouts, instead of traditional signalized intersections, increasingly are being installed on busy arterial streets. Even though roundabouts gradually are becoming integral to regional transportation networks, no literature addresses the safety impacts of access management features such as driveways and median openings along corridors with roundabouts. The existing access management guidelines for signalized intersections are not directly applicable to roundabouts because traffic operations are different at the two types of intersections. In this study the safety impact of access features near roundabouts was investigated. In particular, three potential safety concerns associated with roundabouts were studied in commercial areas in Florida: (a) the impact of driveway corner clearances on roundabout safety, (b) the safety impact of median openings near roundabouts, and (c) the safety at roundabouts that provided direct access to activity centers. Data for the analysis reflected 131 roundabouts in commercial areas in Florida. Police reports for more than 1,000 crashes that occurred in roundabout influence areas between 2007 and 2011 were reviewed carefully to identify crash patterns and causes related to specific roundabout designs, con-figurations, and access features. Recommendations are made for access features near roundabouts in commercial areas.

Use of Movable Bus Stop Loading Pads: Feasibility and Design Alternatives

The Americans with Disabilities Act (ADA) of 1990 requires bus stops to be accessible for individuals with disabilities. At a minimum, bus stops must have firm, stable, slip-resis tant loading pads with connected sidewalks and curb ramps. Consequently, the typical approach of transit agencies has been to install permanent concrete loading pads at bus stops. This study explored alternatives to conventional concrete pads with movable pads that could be installed quickly, resulting in savings in construction and labor costs and minimizing both disruptions to traffic and impacts to abutting businesses. Potential design alternatives in terms of materials and structural support for these pads were evaluated. The review focused on existing and alternative design materials, especially in applications other than for transit purposes. Six materials were evaluated based on their structural performance, long-term durability, adaptability, life cycle cost, aesthetics, and safety and accessibility of transit riders with mobility devices. Of the six materials, plastic lumber and metal were found to have the highest potential to replace conventional designs. Two design alternatives that rely on the concept of bridge construction were introduced, both of which consist of four major components: foundation, slab, beam, and connections. These new design alternatives are anticipated to minimize maintenance of traffic and the need for heavy machinery to excavate, fill, and/or compact the soil.

Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.