Jian John Lu

Factors influential in making an injury severity difference to older drivers involved in fixed object-passenger car crashes

To identify factors influencing severity of injury to older drivers in fixed object-passenger car crashes, two sets of sequential binary logistic regression models were developed. The dependent variable in one set of models was driver injury severity, whereas for the other it was the crash severity (most severe injury in the crash). For each set of models, crash or injury severity was varied from the least severity level (no injury) to the highest severity level (fatality) and vice versa. The source of data was police crash reports from the state of Florida. The model with the best fitting and highest predictive capability was used to identify the influence of roadway, environmental, vehicle, and driver related factors on severity. Travel speed, restraint device usage, point of impact, use of alcohol and drugs, personal condition, gender, whether the driver is at fault, urban/rural nature and grade/curve existence at the crash location were identified as the important factors for making an injury severity difference to older drivers involved in single vehicle crashes.

Exploring Impacts of Factors Contributing to Injury Severity at Freeway Diverge Areas

A study was done to identify factors contributing to injury severity at freeway diverge areas and to evaluate impacts of the factors. Crash data and roadway information were collected at 231 freeway exit segments in Florida. Injury severity prediction models were developed by using partial proportional odds regression, which relaxes the restriction that all regression coefficients be the same across output values and allows one or more regression coefficients to differ across outcome levels. The analysis results indicated that the partial proportional odds model is more flexible and provides much better results than does the ordered probit model for fitting injury severity data. Factors that significantly influence injury severity at freeway diverge areas include length of deceleration and ramp lanes, curve and grade at diverge areas, light and weather conditions, alcohol or drug involvement, heavy-vehicle involvement, number of lanes on main lines, average daily traffic on main lines, surface condition, land type, and crash type. It can also be concluded that exit ramp types (single-lane exit ramps, single-lane exit ramps with a taper, two-lane exit ramps with an optional lane, and two-lane exit ramps without an optional lane) have no significant effects on injury severity at freeway diverge areas.

Evaluating the safety impacts of the number and arrangement of lanes on freeway exit ramps

The primary objective of this study is to evaluate the impacts of the number and arrangement of lanes on freeway exit ramps on the safety performance of freeway diverge areas. The research team collected crash data at 343 freeway segments in the state of Florida. Four different types of exit ramps were considered in this study. They were defined as type 1, type 2, type 3, and type 4 exit ramps respectively. Cross-sectional comparison was conducted for comparing crash frequency, crash rate and crash severity between different types of freeway exit ramps. Crash prediction models were developed to identify the factors that contribute to the crashes reported at selected freeway segments and to provide quantified information regarding the safety impacts of different freeway exit ramps. It was found that the ramp and freeway AADT, posted speed limit on freeway, deceleration lane length, right shoulder width, and the type of exit ramp significantly affected the safety performance of freeway diverge areas. The study demonstrated the safety benefits of using lane-balanced exit ramps. Based on the crash prediction models, replacing a type 1 exit ramp (lane-balanced) with a type 2 exit ramp (not lane-balanced) will increase crash counts at freeway diverge areas by 68.33%. Replacing a type 3 ramp (lane-balanced) with a type 4 ramp (not lane-balanced) will increase crash counts at freeway diverge areas by 32.20%.

How Lane Arrangements on Freeway Mainlines and Ramps Affect Safety of Freeways with Closely Spaced Entrance and Exit Ramps

The number and arrangement of lanes on freeways are important considerations in freeway geometric design. The objective of this study is to evaluate the safety impacts of lane arrangements on freeway segments with closely spaced entrance and exit ramps. Three different types of lane arrangements were considered. They were designated as Type A, Type B, and Type C arrangements. The research team compared crash frequency, crash rate, crash severity, and collision types for freeway segments with different types of lane arrangements. Crash prediction models were developed to relate crash counts to various explanatory variables such as traffic conditions and geometric characteristics. The crash data analysis results show that the Type C arrangement reported the lowest average crash frequency and crash rate. The Type C arrangement uses a continuous auxiliary lane to connect the entrance and exit ramps; and the auxiliary is dropped in a one-lane exit. Freeway segments with the Type B arrangement reported the highest average crash frequency, crash rate, and percentage of fatal plus severe injury crashes. Based on the crash prediction models, if other factors remain constant, a Type B arrangement results in 113% more total crashes and 102% more severe crashes than does a Type C arrangement. Crash data analysis results suggest that the Type B arrangement should be used cautiously when entrance and exit ramps are closely spaced.

Modeling Crack Deterioration of Flexible Pavements: Comparison of Recurrent Markov Chains and Artificial Neural Networks

Pavement cracking and rutting are two of the most critical distress types manifested on flexible pavements, and they often govern the overall pavement condition. Hence, many models have been developed for forecasting the deterioration of the crack condition accurately, with the traditionally preferred technique being the use of a regression relationship developed from laboratory or field statistical data, or both. However, it becomes tedious for regression techniques to predict crack performance accurately and robustly in the presence of the multitude of tributary factors, material nonlinearity, and uncertainty involved in the cracking process. With the advancement of modeling techniques, two innovative breeds of models, neural networks and recurrent Markov chains, have drawn increasing attention from researchers for their use in modeling complex phenomena such as pavement cracking. This paper compares the ability of neural networks and recurrent Markov chains to model crack performance, using the Florida...

Safety effects of the separation distances between driveway exits and downstream U-turn locations

Using U-turns as alternatives to direct left-turns is an important access management treatment which has been widely implemented in the United States to improve safety on multilane highways. The primary objective of this study is to evaluate the safety effects of the separation distances between driveway exits and downstream U-turn locations. To achieve the research objective, crash data reported at 140 street segments in the state of Florida were investigated. The selected sites were divided into three groups based on the separation distances. t-Tests and proportionality tests were performed for comparing crash frequency, crash type, and crash severity between different separation distance groups. Negative-binomial models were developed for examining the factors that contribute to the crashes reported at selected sites. The data analysis results show that the separation distances significantly impact the safety of the street segments between driveways and downstream U-turn locations. A 10% increase in separation distance will result in a 3.3% decrease in total crashes and a 4.5% decrease in the crashes which is related with right-turns followed by U-turns. The models also show that providing U-turns at a signalized intersection will result in more crashes at weaving sections. Thus, if U-turns are to be provided at a signalized intersection, a longer separation distance shall be provided.

Operational Effects of U-Turns as Alternatives to Direct Left Turns from Driveways

Recently, many state and local transportation agencies have begun considering using U-turns as alternatives to direct left turns (DLT) from driveways through installing nontraversable medians on multi-lane highways. Some of the concerns are that this proposed change may transfer operational problems to the downstream median opening and increase delay or travel time of vehicles making a left-turn egress from driveways. However, the effect of this treatment on roadway operations is not clear. A comprehensive study of operational effects of this treatment would be beneficial, both in setting design policy and in project-level design. Field studies were conducted to obtain the data necessary to quantify operational effects of this treatment at eight sites in the Tampa and Clearwater areas of Florida. About 300 h of field data were collected, including delay, travel time, traffic volume, speed, traffic control, and geometric data. An operational effects database was developed to perform statistical analyses. Delay and travel-time models of DLT and right turn plus U-turn (RTUT) were developed as a function of major and minor road-traffic-flow rates. The curves developed based on delay and travel-time models present a clear picture of the operational effects of the two types of movement. Moreover, a ratio model was developed to estimate how many drivers would prefer to make a RTUT rather than a DLT under certain traffic-flow conditions. Finally, the operations models were used to measure system performance of a full median opening versus a directional median opening from the standpoint of weighted-average total delay.

Forecasting Overall Pavement Condition with Neural Networks: Application on Florida Highway Network

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOTs pavement management system.

Capacity of U-Turns at Unsignalized Median Openings on Six-Lane Streets:

The primary objective of this study is to estimate the potential capacity of U-turns at unsignalized median openings on six-lane streets. To achieve the research objective, data were collected at seven unsignalized median openings in the Tampa Bay area of Florida. By using the maximum likelihood method and the Siegloch method, the research team estimated the critical headway and follow-up times for U-turns on six-lane streets. The critical headway was found to be 5.6 s. The follow-up time was found to be 2.3 s. With the estimated critical headway and follow-up time obtained, Harderss model was used for estimating the potential capacity of U-turns. The research team also estimated the conflicting traffic volume for U-turns. Model validation results show that the Harders model, which uses a weighted conflicting traffic volume, provides the best capacity estimates. The weighted conflicting traffic volume for U-turns on six-lane streets equals 2.2 times the average opposing major street traffic volume in each lane. Using the weighted conflicting traffic volume, Harderss model yields a mean absolute percent error (MAPE) of 17.8%. Even though the MAPE is relatively high, Harderss model does not systematically overestimate or underestimate the capacity of U-turns on six-lane streets. The results suggest that, with the parameters estimated in this study, the current capacity estimation methods provided by the Highway Capacity Manual can be applied to estimating U-turn capacity at unsignalized median openings on six-lane streets.

DRIVER AGE DIFFERENCES IN DAY AND NIGHT GAP ACCEPTANCE CAPABILITIES

Performance of Two-way Stop-controlled (TWSC) intersections is greatly affected by the gap acceptance capabilities of drivers. However, there have been no detailed studies conducted regarding driver age differences in gap acceptance capabilities under different light conditions. Therefore, this study was conducted to fill the lack of information in that area, by considering three driver age groups (old, middle, and young), two maneuvers (left-turn, and through), and two light conditions (daytime, and nighttime). Field observations were made at several TWSC intersections and data were collected at these sites regarding available and accepted gaps on the major street and age group of minor street drivers, both during daytime and nighttime. Statistical analysis conducted in this study with a 5% level of significance revealed that there were significant differences in gap acceptance capabilities among the three driver age groups under both light conditions. Only older drivers indicated statistically different gap acceptance capabilities depending on the light condition, where they illustrated longer critical gap values during nighttime.