Modeling injury severity of crashes involving golf carts: A case study of The Villages, Florida.

OBJECTIVE: Crashes involving golf carts (GCs) have been on an increasing trend in recent years, particularly in the United States. This study focuses on analyzing GC crashes in the Florida community known as The Villages, one of the largest GC-oriented communities in the nation and worldwide. The objective was to evaluate the injury severity of crashes involving GCs in a retirement community where GCs are a common mode of transportation. METHODS: The ordinal logistic regression (OLR) and Decision Tree Ensemble (DTE) models were used to analyze the injury severity of 616 GC-related crashes. Models' accuracy parameters were used to check their reliability. RESULTS: The analysis revealed that GC crash severity is influenced by various factors. Factors found to be significant by the OLR model in determining injury severity include ejection of one or more occupants from the GC, the extent of damage to the GC, GC speed prior to the crash, roadway characteristics (including divided roadways, traffic control devices, paved shoulders, and T-intersections), and roll-over incidents. The OLR model demonstrated an overall accuracy of approximately 71% in predicting injury severity. The DTE model performed better, with an overall accuracy of 78%. The OLR model's findings were supported by the DTE model, which identified estimated GC speed, occupant(s) ejection from the GC, estimated GC vehicle damage, intersection type, and type of shoulder as the most important factors influencing GC crash severity. CONCLUSIONS: Understanding these factors is vital for transportation agencies to develop effective strategies to reduce the severity of GC crashes, ensuring the safety of GC users. This study provides recommendations to transportation agencies on measures to improve the safety of GCs.

What are the leading causes of fatal and severe injury crashes involving older pedestrian? Evidence from Bayesian network model.

INTRODUCTION: Identifying factors contributing to the risk of older pedestrian fatal/severe injuries, along with their possible interdependency, is the first step towards improving safety. Several previous studies focused on identifying the influence of individual factors while ignoring their interdependencies. This study investigated the leading risk factors associated with older pedestrian fatalities/severe injuries by identifying the interdependency relationship among variables. METHOD: A Bayesian Logistic Regression (BLR) model was developed to identify significant factors influencing pedestrian fatalities and severe injuries, followed by a Bayesian Network (BN) model to reveal the interdependency relationship among the statistically significant variables and crash severity. Furthermore, the probabilistic inference was conducted to identify the leading cause of fatal and severe injuries involving older pedestrians. The models were developed with data from 913 pedestrian crashes involving older pedestrians at signalized intersections in Florida from 2016 through 2018. RESULTS: Vehicle maneuver, lighting condition, road type, and shoulder type were directly associated with older pedestrian fatality/severe injury. Vehicle maneuver (going straight ahead) was the most significant factor in influencing the severity of crashes involving older pedestrians. The interdependency of vehicle moving straight, nighttime condition, and two-way divided roadway with curbed shoulders was associated with the highest likelihood of fatal and severe-injury crashes involving older pedestrians. CONCLUSIONS: The Bayesian Network revealed the interdependency between variables associated with fatal and severe injury-crashes involving older pedestrians. The interdependency relationship with the highest likelihood to cause fatalities/severe-injuries comprised factors with the significant individual contribution to the severity of crashes involving older pedestrians. Practical applications: The interdependencies among variables identified in this research could help devise targeted engineering, education, and enforcement strategies that could potentially have a greater effect on improving the safety of older pedestrians.

Evaluating the effect of ramp metering on freeway safety using real-time traffic data.

Ramp metering relieves traffic congestion, reduces delay, and maintains the capacity flow on freeways. Due to its operational mechanism, ramp metering can also improve freeway safety. While the operational benefits of ramp metering have extensively been quantified, research on its safety effects is sparse. This study focused on evaluating the effects of ramp metering on the safety performance of the freeway mainline. It developed a crash risk prediction model for segments downstream of the entrance ramps when ramp metering is activated. The study was based on a corridor with system-wide ramp metering along I-95 in Miami, Florida. Real-time traffic, crash, and ramp metering operations data collected from 2016 to 2018 were used in the analysis. The study adopted a matched crash and non-crash case approach to evaluate the crash risk when ramp meters were activated and deactivated. A penalized logistic regression model was developed using a bootstrap resampling technique to estimate the effects of ramp metering activation and select important variables that could predict crash risk when ramp meters were activated. Results indicated that ramp metering improves safety along the freeway corridor by reducing the crash risk downstream of the entrance ramps. During ramp metering activation, the crash risk on segments downstream of the entrance ramps 5 min later can be predicted using the difference in the average lane speeds between upstream and downstream detectors, the average traffic volume in the lanes at the downstream and upstream detectors, and the coefficient of variation of speed between lanes in the upstream detectors. Also, the coefficient of variation of occupancy downstream could predict the crash risk 15 min later. The study results could be used by transportation agencies when evaluating the deployment of ramp meters. Moreover, the developed crash risk prediction model could be used in real-time to help agencies identify the increased crash risk and provide appropriate warning information to the upstream traffic.

Evaluating the impact of Road Rangers in preventing secondary crashes.

Many transportation agencies utilize freeway service patrols (FSPs) to quickly identify and respond to incidents. The objectives of FSP are to minimize the incident duration and increase safety at the incident scene. The current research explored the safety benefits of Florida's FSP program known as Road Rangers - harnessed from lowering the likelihood of secondary crashes (SCs) - compared to other responding agencies. The analysis was done on 6088 incidents that occurred on freeways in Jacksonville, Florida, from 2015 through 2017. Since SCs were not explicitly identified in the SunGuide® incident database, the study adopted a data-driven technique that used BlueToad® speed data to identify them. Once SCs were identified, a model was developed to identify factors influencing their occurrence. Factors such as an increase in equivalent hourly traffic volume, incident impact duration, and the percent of lanes closed significantly increased the likelihood of a SC. Besides, moderate/severe incidents, crash events, weekdays, peak hours, shoulder blockage, and incidents involving towing showed a high likelihood of resulting in a SC. Of practical importance, the model results revealed that a minute increase in incident impact duration increased the SC probability by 1.2 percent, with other factors held constant. Based on a 16-minutes decrease in incident impact duration, the Road Rangers program could lessen the probability of SCs by 21 percent, compared to other agencies. These findings could be beneficial to incident managers, responders, and researchers in evaluating the program's performance.

Prediction of vehicle occupants injury at signalized intersections using real-time traffic and signal data.

Intersections are among the most dangerous roadway facilities due to the existence of complex movements of traffic. Most of the previous intersection safety studies are conducted based on static and highly aggregated data such as average daily traffic and crash frequency. The aggregated data may result in unreliable findings because they are based on averages and might not necessarily represent the actual conditions at the time of the crash. This study uses real-time event-based detection records, and crash data to develop predictive models for the vehicle occupants' injury severity. The three-year (2017-2019) data were acquired from the arterial highways in the City of Tallahassee, Florida. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were used to identify the important factors on the vehicle occupants' injury severity prediction. The performance comparison of the two classifiers revealed that the XGBoost has a higher balanced accuracy score than RF. Using the XGBoost classifier, five topmost influential factors on injury prediction were identified. The factors are the manner of the collision, through and right-turn traffic volume, arrival on red for through and right-turn traffic, split failure for through traffic, and delays for through and right-turn traffic. Moreover, the partial dependency plots of the influential variables are presented to reveal their impact on vehicle occupant injury prediction. The knowledge gained from this study will be useful in developing effective proactive countermeasures to mitigate intersection-related crash injuries in real-time.

Severity of motorcycle crashes in Dar es Salaam, Tanzania.

OBJECTIVE: Motorcycles are a common mode of transportation in low- and middle-income countries. Tanzania, in particular, has experienced an increased use of motorcycles in the last decade. In Dar es Salaam, motorcycles provide door-to-door travel and often operate where more conventional services are uneconomical or physically impossible to maneuver. Although motorcycles play a crucial role in improving mobility in the city, they have several safety issues. This study focuses on identifying factors influencing the severity of motorcycle crashes. METHOD: A multinomial logit analysis was conducted to identify factors influencing the severity of motorcycle crashes in Dar es Salaam, Tanzania. The severity categories were fatal, severe injury, minor injury, and property damage only (PDO). The analysis was based on a total of 784 motorcycle crashes that occurred from 2013 to 2016. RESULTS: The following factors were found to increase the probability of a fatality: Speeding, driving under the influence, head-on impact, presence of horizontal curves, reckless riding, off-peak hours, violations, and riding without a helmet. The results indicate that crashes occurring on weekdays, during peak hours, at intersections, involving a rear-end impact, in daylight, on street roads, and under clear weather conditions decrease the probability of a fatality. However, minor injury and PDO crashes were found to be associated with crashes occurring during peak hours, at intersections, and on street roads, as well as failure to yield right-of-way. CONCLUSIONS: Several countermeasures are recommended based on the study findings. The recommended countermeasures focus on the holistic safety improvement strategies constituting the three Es of highway safety, namely, engineering, education, and enforcement.

Investigating proximity of crash locations to aging pedestrian residences.

Many campaigns promote walking for recreation, work, and general-purpose trips for health and environmental benefits. This study investigated factors that influence the occurrence of crashes involving elderly pedestrians in relation to where they reside. Using actual pedestrian residential addresses, a Google integrated GIS-based method was developed for estimating distances from crash locations to pedestrian residences. A generalized linear mixed model (GLMM) was used to evaluate the effect of factors associated with residences, such as age group, roadway features, and demographic characteristics on the proximity of crash locations. Results indicated that the proximity of crash locations to pedestrian residences is influenced by the pedestrian age, gender, roadway traffic volume, seasons of the year, and pedestrian residence demographic characteristics. The findings of this study can be used by transportation agencies to develop plans that enhance aging pedestrian safety and improve livability.

Likelihood estimation of secondary crashes using Bayesian complementary log-log model.

Secondary crashes (SCs) occur within the spatial and temporal impact range of a primary incident. They are non-recurring events and are major contributors to increased traffic delay, and reduced safety, particularly in urban areas. However, the limited knowledge on the nature of SCs has largely impeded their mitigation strategies. The primary objective of this study was to develop a reliable SC risk prediction model using real-time traffic flow conditions. The study data were collected on a 35-mile I-95 freeway section for three years in Jacksonville, Florida. SCs were identified based on travel speed data archived by the Bluetooth detectors. Bayesian random effect complementary log-log model was used to link the probability of SCs with real-time traffic flow characteristics, primary incident characteristics, environmental conditions, and geometric characteristics. Random forests technique was used to select the important variables. The results indicated that the following variables significantly affect the likelihood of SCs: average occupancy, incident severity, percent of lanes closed, incident type, incident clearance duration, incident impact duration, and incident occurrence time. The study results have the potential to proactively prevent SCs.

Planning sample sizes for before-after accident comparisons.

Before-and-after safety studies are becoming more desirable in lieu of traditional cross-sectional studies in establishing crash modification factors, especially after the introduction of the first version of the Highway Safety Manual (2010). We present a simple method for estimating necessary sample sizes to obtain a target precision or power when the effect is represented as a proportional change in a Poisson rate. We also show that necessary sample sizes are not very different when full Bayesian models are implemented.

Effect of bus size and operation to crash occurrences.

This paper evaluates roadway and operational factors considered to influence crashes involving buses. Factors evaluated included those related to bus sizes and operation services. Negative binomial (NB) and multinomial logit (MNL) models were used in linearizing and quantifying these factors with respect to crash frequency and injury severities, respectively. The results showed that position of the bus travel lane, presence or absence of on-street shoulder parking, posted speed limit, lane width, median width, number of lanes per direction and number of vehicles per lane has a higher influence on bus crashes compared to other roadway and traffic factors. Wider lanes and medians were found to reduce probability of bus crashes while more lanes and higher volume per lane were found to increase the likelihood of occurrences of bus-related crashes. Roadways with higher posted speed limits excluding freeways were found to have high probability of crashes compared to low speed limit roadways. Buses traveling on the inner lanes and making left turns were found to have higher probability of crashes compared to those traveling on the right most lanes. The same factors were found to influence injury severity though with varying magnitudes compared to crash frequency.