Search

• This paper investigates the correlation between pedestrian crashes and sidewalk gaps within the Central Florida region. • The presence of sidewalk is one of the main factors that have significant impact on the expected number of pedestrian crashes along a roadway segment. • The number of crashes increases with the increase in the gap length where shorter segments less than 2 miles in length with no sidewalks had no crashes. • The likelihood of a pedestrian crash along roadways with no sidewalk is 1.67 times greater than the likelihood of a crash with the presence of a sidewalk. • The likelihood of a pedestrian crash per mile along roadways with no sidewalk is 3 times greater than the likelihood of a crash per mile with the presence of a sidewalk. In recent years, pedestrian safety has emerged to be of major concern to selected States in the US and at the national level. With the transition to the mobility of people, pedestrian safety has become more prevalent for governmental agencies to address and prioritize for strategic implementation. It was crucial to address the pedestrian-vehicular conflict within Florida as it was reported as having the highest four pedestrian incident locations in the country. Many pedestrian sidewalks in Florida are not continuous and there is a concern among planners and engineers in the Florida Department of Transportation (FDOT) that these gaps constitute discontinuity of flow and are potentially posing threats to pedestrian safety. This paper investigates the correlation between pedestrian safety and sidewalk gaps within the Central Florida region. A sidewalk gap is defined as a length of a roadway segment with missing or absent sidewalk between existing sidewalks (before and after) along the same segment. "Missing" sidewalks are locations where the sidewalk is not only absent, but there is a desire to have one constructed or it would be used if it were constructed. The missing sidewalk can be on one side of the road or both sides. A wide array of Geographic Information Systems (GIS) information associated with the sidewalk gaps and the safety data were secured by FDOT. The data included all the Roadway Characteristics Inventory (RCI) within the district. Crash data combined with other geometric design data, traffic data, and census data were used to develop a statistical model. The results showed that presence of sidewalk along roadway segments is one of the main factors that have significant impact on the expected number of pedestrian crashes at a specific location. Other factors included daily traffic volumes, roadway category, specifically along urban two-way divided and undivided arterials with four to six lanes as well as the population within half-mile radius surrounding the crash location. The model results revealed that the Incident Risk Ratio (IRR) of a pedestrian crash along roadways with no sidewalk is 1.67 times greater than the likelihood of a crash with the presence of a sidewalk at 95% Confidence Interval (C.I.). The analysis also concluded that the likelihood of a pedestrian crash per mile along roadways with no sidewalk is three times greater than the likelihood of a crash per mile with the presence of a sidewalk. [ABSTRACT FROM AUTHOR]

Traditional mainline toll plaza (TMTP) is considered the most high-risk location on the toll roads. Conversion from TMTP or hybrid mainline toll plaza (HMTP) to an all-electronic toll collection (AETC) system has demonstrated measured improvement in traffic operations and environmental issues. However, there is a lack of research that quantifies the safety impacts of these new tolling systems. This study evaluated the safety effectiveness of the conversion from TMTP or HMTP to AETC system. An extensive data collection was conducted that included hundred mainline toll plazas located on more than 750 miles of toll roads in Florida. Various observational before-after studies including the empirical Bayes method were applied. The results indicated that the conversion from the TMTP to an AETC system resulted in an average crash reduction of 76, 75, and 68% for total, fatal-and-injury and property damage only (PDO) crashes, respectively; for rear end and lane change related (LCR) crashes the average reductions were 80 and 74%, respectively. The conversion from HMTP to AETC system enhanced traffic safety by reducing crashes by 24, 28 and 20% of total, fatal-and-injury, and PDO crashes respectively; also, for rear end and LCR crashes, the average reductions were 15 and 22%, respectively. Overall, this paper provided an up-to-date safety impact of using different toll collection systems. The results proved that the AETC system significantly improved traffic safety for all crash categories; and changed toll plazas from the highest risk on Expressways to be similar to regular segments. [ABSTRACT FROM AUTHOR]

• This paper develops SPFs at different aggregation levels for freeways by using microscopic traffic detector data. • The effects of average speed and speed variation on crash frequencies are revealed at different aggregation levels. • The time-of-day operation status of HOV lanes is introduced to the disaggregated SPFs. • The safety performance of HOV lanes at different aggregation levels are evaluated and compared. • The transferability of SPFs at different aggregation levels between Florida, Virginia, and California is evaluated. Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geometric characteristics. However, the high level of aggregation may lead to a failure to capture the temporal variation in traffic characteristics (e.g., traffic volume and speed) and crash frequencies. In this study, SPFs at different aggregation levels were developed based on microscopic traffic detector data from California, Florida, and Virginia. More specifically, five aggregation levels were considered: (1) annual average weekday hourly traffic (AAWDHT), (2) annual average weekend hourly traffic (AAWEHT), (3) annual average weekday peak/off-peak traffic (AAWDPT), (4) annual average day of the week traffic (AADOWT), and (5) annual average daily traffic (AADT). Model estimation results showed that the segment length and volume, as exposure variables, are significant across all the aggregation levels. Average speed is significant with a negative coefficient, and the standard deviation of speed was found to be positively associated with the crash frequency. It is noteworthy that the operation of the high occupancy vehicle (HOV) lanes was found to have a positive effect on crash frequency across all the aggregation levels. The model results also showed that the AAWDPT and AADOWT models consistently performed better (the improvements range from 3.14%–16.20%) than the AADT-based SPF, which implies that the differences between the day of the week and peak/off-peak periods should be considered in the development of crash prediction models. The model transferability results indicated that the SPFs between Florida and Virginia are transferrable, while the models between California and the other two states are not transferrable. [ABSTRACT FROM AUTHOR]

• This study developed a procedure to rank unsignalised urban intersections for safety improvement. • The procedure identifies and ranks risk factors by safety inspections. • The procedure assesses a Safety Index (SI) for vehicles and pedestrians. • The procedure was validated in a sample of eighty-nine urban intersections in Florida. • The correlation between the SI scores and the Empirical Bayes estimates was significant. Methods based on crash data analysis are effective in identifying intersections with a potential for safety improvement. However, it is well recognized that crash data suffer from several shortcomings and that there are clues to safety other than crash occurrence. The systemic approach is an alternative method to address safety issues. In this approach, a transportation agency is able to identify priority locations based on the presence of risk factors rather than actual crashes. To promote wider use of the systemic safety approach, this paper aims at developing and validating a procedure to rank unsignalised urban intersections for safety improvement based on the evaluation of risk factors by road safety inspections. The procedure assesses a Safety Index (SI) that measures the safety performance of unsignalised urban intersections. The SI is formulated by combining two components of risk: the exposure of road users to road hazards (Exposure) and the risk factors, which increase the probability of involvement in crashes (Risk Index). The SI is made of two elements, one related to vehicles and one to pedestrians. Twenty-three detailed safety issues and ten general safety issues are assessed to compute the vehicle Risk Index and the pedestrian Risk Index. Safety issues were selected considering that they are common issues and that effective remedial measures exist and have already proven their effectiveness. Finally, criteria for identifying and ranking safety issues were defined. The SI has two main practical applications. High risk intersections, where safety measures that can reduce crash frequency exist, can be identified and ranked by the SI. Specific safety issues, that give more contribution to unsafety, are pointed out in order to give indication about more appropriate safety measures according to the systemic safety approach. The procedure was validated with a sample of eighty-nine urban intersections located in Orange County (Florida, U.S.). For these intersections, the SI scores, the Empirical Bayes (EB) safety estimates, and the potential for improvement (PFI) were compared. The correlation between the SI scores and the EB estimates was highly significant both for vehicles (R2 = 0.66) and pedestrians (R2 = 0.58) as well as for the total crashes (R2 = 0.68). The results from the Spearman's rank-correlation analysis provided further validation for the SI indicating that ranking from the SI and the EB estimates agree at the 99.9% confidence level for vehicles (ρ s = 0.78), pedestrians (ρ s = 0.93), and total (ρ s = 0.93). [ABSTRACT FROM AUTHOR]

• Given a flexible framework for count data regression, the Poisson-Tweedie distribution was used to model crash frequency. • A series of models with fixed and varying dispersion were developed under different values of power parameter in variance. • The performance of a model varied by how the dispersion parameter was formulated. • The dispersion parameter values were found to be smaller in models fitted with higher value of the power parameter. • The variation in expected crash frequency and site ranking linked to different dispersion and power parameters was examined. This paper describes a study that applies the Poisson-Tweedie distribution in developing crash frequency models. The Poisson-Tweedie distribution offers a unified framework to model overdispersed, underdispersed, zero-inflated, spatial, and longitudinal count data, as well as multiple response variables of similar or mixed types. The form of its variance function is simple, and can be specified as the mean added to the product of dispersion and mean raised to the power P. The flexibility of the Poisson-Tweedie distribution lies in the domain of P, which includes positive real number values. Special cases of the Poisson-Tweedie distribution models include the linear form of the negative binomial (NB1) model with P equal to 1.0, the geometric Poisson (GeoP) model with P equal to 1.5, the quadratic form of the negative binomial (NB2) model with P equal to 2.0, and the Poisson Inverse Gaussian (PIG) model with P equal to 3.0. A series of models were developed in this study using the Poisson-Tweedie distribution without any restrictions on the value of the power parameter as well as with specific values of the power parameter representing NB1, GeoP, NB2, and PIG models. The effects of fixed and varying dispersion parameters (i.e., dispersion as a function of covariates) on the variance and expected crash frequency estimates were also examined. Three years (2012–2014) of crash data from urban three-leg stop-controlled intersections and urban four-leg signalized intersections in the state of Florida were used to develop the models. The Poisson-Tweedie models or the GeoP models were found to perform better when the dispersion parameter was constant or fixed. With the varying dispersion parameter, the NB2 and PIG models were found to perform better, with both performing equally well. Also, the fixed dispersion parameter values were found to be smaller in the models with a higher value of the power parameter. The variation across the models in their estimates of weight factor, expected crash frequency, and potential for safety improvement of hazardous sites based on the empirical Bayes method was also discussed. [ABSTRACT FROM AUTHOR]

• Injury severity of large truck rollover crashes were studied in the state of Florida. • Heterogeneity was explored using a random parameter ordered logit model. • Lighting conditions and medium driving speed (25–50 mph) showed significant variations in their impacts on injury severity. • Careless driving and abnormal driver condition increased the likelihood of more severe outcomes when driving speed is 25–50 mph. • Speeding and vision obstruction led to more severe outcomes in insufficient lighting conditions. Large truck rollover crashes present significant financial, industrial, and social impacts. This paper presents an effort to investigate the contributing factors to large truck rollover crashes. Specific focus was placed on exploring the role of heterogeneity and the potential sources of heterogeneity regarding their impacts on injury-severity outcomes. The data used in this study contained large truck rollover crashes that occurred between 2007 and 2016 in the state of Florida. A random parameter ordered logit (RPOL) model was applied. Various driver, vehicle, roadway, and crash attributes were explored as potential predictors in the model. Their impacts were examined for the presence of heterogeneity. Interaction effects were then added to the random variables in order to detect potential sources of heterogeneity. Model results showed that the impacts of lighting conditions and driving speed had significant variation across observations, and this variation could be attributed to driver actions and driver conditions at the time of the crash, as well as driver vision obstruction. Findings from this study shed light on the direction, magnitude, and randomness of the factors that contribute to large truck rollover crashes. Findings associated with heterogeneity could help develop more effective and targeted countermeasures to improve freight safety. Driver education programs could be planned more efficiently, and advisory and warning signs could be designed in a more insightful manner by taking into account specific roadway attributes, such as sandy surfaces, downhill, curved alignment, unpaved shoulders, and lighting conditions. [ABSTRACT FROM AUTHOR]