Valerian Kwigizile

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.

Inconsistencies of Ordered and Unordered Probability Models for Pedestrian Injury Severity

Data on crashes between a single vehicle and a pedestrian recorded in Florida from 2004 to 2008 were used to identify the factors affecting the level of pedestrian injury severity, given that an accident had occurred and to assess the consistency of the ordered (ordered probit) and the unordered (multinomial logit) models. Both models were applied to the same data set. For the impact of individual variables on the levels of pedestrian injury severity to be discerned for the ordered probit and the multinomial logit models, the marginal effects were calculated. The results of a comparison of the two models indicated that the two models were consistent when they suggested the impact of individual factors on the lowest and the highest levels of injury severity (no injury or a possible injury and fatal injury, respectively) but suggested opposing impacts for some factors on intermediate levels of injury severity (nonincapacitating and incapacitating injuries). Such an inconsistency has implications for pedestrian safety measures and policies that are based on the models. Therefore, cautious selection of ordered and unordered probability models should be exercised with the use of a trade-off between recognition of the ordered pedestrian injury outcomes and loss of the flexibility in specification offered by unordered probability models. However, because the models are consistent for determination of the impact of variables on the lowest and the highest outcomes, pedestrian safety measures and policies should be derived on the basis of these outcomes.

Effects of Rain on Traffic Operations on Florida Freeways

Although the correlation between traffic variables and weather appears to be intuitive, quantifying the effects that weather, especially rain, has on driver response in travel speeds and traffic demands is needed to evaluate practical aspects of traffic operations. Previous studies have researched driver responses to inclement weather on freeways located in northern regions of the United States and Canada. However, driver familiarity with local weather conditions is a factor that should be considered in determining inclement weather effects on traffic variables. The focus of this research was to examine driver response to rain precipitation on freeways located in the southeastern regions of the United States to determine whether results from previous studies were general indicators or location specific in nature. To study the impacts of rain precipitation on hourly mean speeds and traffic volumes, hourly weather data and traffic sensor data were collected for two freeway segments in Jacksonville, Florida. The study investigated conditions such as wet versus dry (rain or no rain) and dry versus rain intensity (no rain or light, moderate, or heavy rain) for each segment. The results indicated that mean travel speeds decreased during rainfall events and speed reductions increased with increasing rain intensity. Reductions found for light rainfall events were within the range of previous studies; however, speed reductions during moderate to heavy rains varied widely. The results also indicated that the hour of the day was a factor in the degree of motorists’ speed reduction. Traffic volumes also declined during rainy conditions, with significant reductions during peak hours.

Quantifying the impact of adaptive traffic control systems on crash frequency and severity: Evidence from Oakland County, Michigan.

INTRODUCTION Despite seeing widespread usage worldwide, adaptive traffic control systems have experienced relatively little use in the United States. Of the systems used, the Sydney Coordinated Adaptive Traffic System (SCATS) is the most popular in America. Safety benefits of these systems are not as well understood nor as commonly documented. METHOD This study investigates the safety benefits of adaptive traffic control systems by using the large SCATS-based system in Oakland County, MI known as FAST-TRAC. This study uses data from FAST-TRAC-controlled intersections in Oakland County and compares a wide variety of geometric, traffic, and crash characteristics to similar intersections in metropolitan areas elsewhere in Michigan. Data from 498 signalized intersections are used to conduct a cross-sectional analysis. Negative binomial models are used to estimate models for three dependent crash variables. Multinomial logit models are used to estimate an injury severity model. A variable tracking the presence of FAST-TRAC controllers at intersections is used in all models to determine if a SCATS-based system has an impact on crash occurrences or crash severity. RESULTS Estimates show that the presence of SCATS-based controllers at intersections is likely to reduce angle crashes by up to 19.3%. Severity results show a statistically significant increase in non-serious injuries, but not a significant reduction in incapacitating injuries or fatal accidents.

Modeling Signalized-Intersection Safety with Corner Clearance

Signalized intersections next to each other on the same arterial share some unobservable information, such as traffic flow and roadway characteristics. This study investigated the impact of access management techniques on crash counts at signalized intersections. The analysis was performed using crash data from 275 signalized intersections in southern Nevada. The panel data random-effect model was used to account for the unobserved factors for each unique arterial. It was found that the negative binomial (NB) regression models were the best in reflecting the dispersion in the crash data. Therefore, the random-effects negative binomial model (RENB) was applied to investigate the relationship between crash occurrence and access-management techniques. The results of the panel data RENB models were compared with those from the pooled NB models, which did not account for the panel data structure. Evaluation of the goodness-of-fit of the models developed indicated that the random-effect negative binomial model was the best-fit for the data at hand. The results from the panel data RENB showed that nine variables significantly affecting the safety at signalized intersections were the average length of corner clearance, traffic flow, land-use types, number of left-turn lanes for main streets, number of through lanes for main and minor streets, posted speed limit on main and minor streets, and grades of legs.

Identifying Access Management Factors Associated With Safety of Urban Arterials Mid-Blocks: A Panel Data Simultaneous Equation Models Approach

Objective: The objective of this study was to evaluate the safety impact of selected access management techniques in urban areas because access management techniques play an important role in urban roadway safety on the roadway network. Methods: In order to correct the interdependency between safety and mobility for heterogeneous mid-block segments, simultaneous equation models were adopted. The panel data structure of the model was used to address the heterogeneity issue for mid-block segments along a corridor. The integrated random coefficient simultaneous equation models were proposed to interpret both issues. Results: The models developed were used to identify influential factors. The length of mid-block segments, driveway density, and median opening density were among the significant factors found to be associated with crash rate on mid-block segments. Conclusions: From the results it can be concluded that the access management techniques, mid-block segment length, driveway density, and median opening density are significant factors that influence safety on mid-block segments. The longer the distance between signals, driveways, and median openings, the fewer the potential crashes are. In addition to these access management techniques, land use, especially the commercial land type, influences the safety on mid-block segments.

Evaluation of Speed Monitoring Displays for Work Zones in Las Vegas, Nevada:

Speed monitoring displays (i.e., speed trailers) have been evaluated in many states for reducing vehicular speeds in work zones. This study evaluated the enhancements to speed trailers regarding message size, the use of flashing, and the presence of more than one speed trailer in work zones. Field tests were conducted at two sites in the Las Vegas, Nevada, area, and traffic data were collected for statistical analysis. Regression models were developed to estimate the speeding likelihood and vehicle speeds on the basis of the free-flow speed data. The results indicated that the size of displayed messages and the use of flashing did show significant impact on speeding likelihood and speed reduction for vehicles in work zones. The extent of the impact varied for vehicle classification, the lanes they were operating in, and the day or night in which they were deployed. This study recommended larger message size and the use of flashing signs for speed trailers. More than one speed trailer was recommended for additional speed reduction.

Investigation of the AP-42 Sampling Method

Abstract The AP-42 method has been recommended by the U.S. Environment Protection Agency to collect dust emission data. According to this method, the number of sampling sites needs to be determined first. At these sites, the dust will be collected based on plots drawn on the road surface. Apparently, there has been no systematic rule to follow to determine the number of sampling sites. In addition, it is not known whether the required number of plots and their sizes are validated based on real data. Mobile sampling technology can collect dust emission data at very close space intervals, which to some extent can be viewed as being close to actual dust emission data continuously distributed over roadway segments. With such data available, this study investigated the number of sampling sites and the number of plots and their sizes based on the optimal allocation sampling method and the Monte Carlo simulation method. The results from the optimal allocation method indicated that most of the sampling sites should be drawn from the local roads because the variance of emission and proportion of road segments of this roadway classification are significantly bigger than other roadway classifications. This observation may lead to the application of other cost-effective sampling approaches. The results from the Monte Carlo simulation method imply that clear patterns of improved estimation of emission factors versus plot number and size can be observed only for three roadway classifications, not for other classifications. This result indicates that the AP-42 method may not be applicable to some roadway classifications, and thus different data collection methods such as the mobile sampling technology may be necessary.

Identifying Influencing Factors on Paved Roads Silt Loading

Abstract The factors that influence the increase or decrease of silt loadings on paved roadways have not been fully quantitatively investigated. They were identified in this study based on the quarterly silt loading sampling data collected from 20 sites by the Clark County Department of Air Quality and Environmental Management in Southern Nevada for the period from 2000 to 2003. The silt loading and associated data collected over these years at one sampling site may inherently possess site-specific characteristics that can be better incorporated by using panel data models. The factors that are identified as significant are the presence of curbs and gutters, shoulder type, pavement conditions, and the presence of construction activities in the vicinity of roadways. The presence of curbs and gutters, stabilized shoulders, and good pavement conditions would result in decreased silt loadings. Conversely, the presence of construction activities within the immediate vicinity of sampled areas would result in increases of silt loadings on the roadway surfaces. Based on the analysis of the results, it was recommended that constructing curbs, gutters and stabilized shoulders, preventing or reducing construction track-out from construction activity, and improving pavement conditions be the preferred control measures to reduce silt loading on paved roadways.

SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.