A study was done to explore the suitability of intersection and arterial collision prediction models based on traffic conflicts, generated using the Paramics microsimulation suite and the Surrogate Safety Assessment Model (SSAM). A linear regression model and a generalized linear model with a negative binomial error structure were explored to correlate conflicts to crash rates, as well as the conflict-based models suggested by SSAM. The model predictions were compared to volume-based predictions and historical data from Toronto, Ontario, Canada. The volume- based predictions were calculated using a negative binomial generalized linear model, fitted to the same arterial and intersection sets used to fit the conflict-based models. The results show the predictions generated by a conflict-based model were comparable for intersections, but poor for arterials.

A study was done to explore the suitability of intersection and arterial collision prediction models based on traffic conflicts, generated using the Paramics microsimulation suite and the Surrogate Safety Assessment Model (SSAM). A linear regression model and a generalized linear model with a negative binomial error structure were explored to correlate conflicts to crash rates, as well as the conflict-based models suggested by SSAM. The model predictions were compared to volume-based predictions and historical data from Toronto, Ontario, Canada. The volume- based predictions were calculated using a negative binomial generalized linear model, fitted to the same arterial and intersection sets used to fit the conflict-based models. The results show the predictions generated by a conflict-based model were comparable for intersections, but poor for arterials.

Road safety is a serious societal concern in Riyadh city, Kingdom of Saudi Arabia. Because of the negative impact of traffic crashes which cause losses in the form of deaths, injuries and property damage, in addition to the pain and social tragedy affecting families of the victims, it is important for transport policy makers to reduce their impact and increase safety standards by reducing the severity and frequency of crashes in the city of Riyadh. It is therefore important to fully understand the relationship between traffic crash severity and frequency and their contributing factors so to establish effective safety policies which can be implemented to enhance road safety in Riyadh city. Data used in previous research have only consisted of basic information as there was unavailability of suitable and accurate data in Riyadh and there are very few studies that have undertaken as small area-wide crash analysis in Riyadh using appropriate statistical models. Therefore safety policies are not based on rigorous analyses to identify factors affecting both the severity and the frequency of traffic crashes. This research aims to explore the relationship between traffic crash severity and frequency and their contributing factors by using statistical models and a GIS approach. The analysis is based on the data obtained over a period of five years, namely AH 1425, 1426, 1427, 1428, and 1429 (roughly equivalent to 2004, 2005, 2006, 2007, and 2008). Injury crash severity data were classified into three categories: fatal, serious injury and slight injury. A series of statistical models were employed to investigate the factors that affect both crash severity (i.e. ordered logit and mixed logit models) and area-wide crash frequency (i.e. classical Poisson and negative binomial models). Because of a severe underreporting problem on the slight injury crashes, binary and mixed binary logistic regression models were also estimated for two categories of severity: fatal and serious crashes. The mixed binary logit model and the negative binomial model are found to be the best models for crash severity and crash frequency analyses respectively. The model estimation results suggest that the statistically significant factors in crash severity are the age and nationality of the driver who is at fault, the time period from 16.00 to 19.59, excessive speed, road surface and lighting conditions, number of vehicles involved and number of casualties. Older drivers are associated with a higher probability of having a fatal crash, and, as expected, excessive speeds were consistently associated with fatal crashes in all models. In the area-level crash frequency models, population, percentage of illiterate people, income per capita and income per adult were found to be positively associated with the frequency of both fatal and serious injury crashes whereas all types of land use such as percentages of residential use, transport utilities, and educational use in all models were found to be negatively associated with the frequency of occurrence of crashes. Results suggest that safety strategies aimed at reducing the severity and frequency of traffic crashes in Riyadh city should take into account the structure of the resident population and greater emphasis should be put on native residents and older age groups. Tougher enforcement should be introduced to tackle the issue of excessive speed. This thesis contributes to knowledge in terms of examining and identifying a range of factors affecting traffic crash severity and frequency in Riyadh city.

No description available.

Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the "site correlation"; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of "at-fault driver" or "near-side" were applied for disaggregated models. It shows that for the roadway and approach level models, the "near-side" models outperformed the "at-fault driver" models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The "site correlation" among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections.

Recent research has shown that incorporating roadway safety in transportation planning has been considered one of the active approaches to improve safety. Aggregate level analysis for predicting crash frequencies had been contemplated to be an important step in this process. As seen from the previous studies various categories of predictors at macro level (census blocks, traffic analysis zones, census tracts, wards, counties and states) have been exhausted to find appropriate correlation with crashes. This study contributes to this ongoing macro level road safety research by investigating various trip productions and attractions along with roadway characteristics within traffic analysis zones (TAZs) of four counties in the state of Florida. Crashes occurring in one thousand three hundred and forty-nine TAZs in Hillsborough, Citrus, Pasco, and Hernando counties during the years 2005 and 2006 were examined in this study. Selected counties were representative from both urban and rural environments. To understand the prevalence of various trip attraction and production rates per TAZ the Euclidian distances between the centroid of a TAZ containing a particular crash and the centroid of the ZIP area containing the at fault driver's home address for that particular crash was calculated. It was found that almost all crashes in Hernando and Citrus County for the years 2005-2006 took place in about 27 miles radius centering at the at-fault drivers' home. Also about sixty-two percent of crashes occurred approximately at a distance of between 2 and 10 miles from the homes of drivers who were at fault in those crashes. These results gave an indication that home based trips may be more associated with crashes and later trip related model estimates which were found significant at 95% confidence level complied with this hypothesized idea. Previous aggregate level road safety studies widely addressed negative binomial distribution of crashes. Properties like non-negative integer counts, non-normal distribution, over-dispersion in the data have increased suitability of applying the negative binomial technique and has been selected to build crash prediction models in this research. Four response variables which were aggregated at TAZ-level were total number of crashes, severe (fatal and severe injury) crashes, total crashes during peak hours, and pedestrian and bicycle related crashes. For each response separate models were estimated using four different sets of predictors which are i) various trip variables, ii) total trip production and total trip attraction, iii) road characteristics, and iv) finally considering all predictors into the model. It was found that the total crash model and peak hour crash model were best estimated by the total trip productions and total trip attractions. On the basis of log-likelihoods, deviance value/degree of freedom, and Pearson Chi-square value/degree of freedom, the severe crash model was best fit by the trip related variables only and pedestrian and bicycle related crash model was best fit by the road related variables only. The significant trip related variables in the severe crash models were home-based work attractions, home-based shop attractions, light truck productions, heavy truck productions, and external-internal attractions. Only two variables- sum of roadway segment lengths with 35 mph speed limit and number of intersections per TAZ were found significant for pedestrian and bicycle related crash model developed using road characteristics only. The 1349 TAZs were grouped into three different clusters based on the quartile distribution of the trip generations and were termed as less-tripped, moderately-tripped, and highly-tripped TAZs. It was hypothesized that separate models developed for these clusters would provide a better fit as the clustering process increases the homogeneity within a cluster. The cluster models were re-run using the significant predictors attained from the joint models and were compared with the previous sets of models. However, the differences in the model fits (in terms of Alkaike's Information Criterion values) were not significant. This study points to different approaches when predicting crashes at the zonal level. This research is thought to add to the literature on macro level crash modeling research by considering various trip related data into account as previous studies in zone level safety have not explicitly considered trip data as explanatory covariates.

Safety literature traditionally employs crash frequency models over aggregated data on different spatial scales – micro level (such as segment or intersection) and macro level (such as zone or block) to examine crash occurrence while crash outcome models are employed at the disaggregate level (such as crash or driver record) to examine crash consequences. However, such independent model systems ignore the embedded relationship within data across different resolutions and result in mis-specified models. Recognizing these drawbacks, the current research proposes multiple frameworks for integrating multi-level crash analysis models. Specifically, the proposed frameworks integrate (i) macro and micro level crash frequency models, (ii) aggregate and disaggregate level models to estimate crash frequency by severity, (iii) aggregate and disaggregate level models to jointly estimate crash frequency by crash type and severity, and (iv) macro, micro and disaggregate level models to estimate crash frequency by severity while accounting for hierarchical relationships among the different levels. The frameworks employ econometric building blocks including negative binomial (NB), NB-ordered probit fractional split, multinomial logit and ordered probit models while accommodating for unobserved heterogeneity. The empirical analysis is conducted using data from the City of Orlando, Florida. Several model fit measures, validation exercises and elasticity analysis augment the model analysis. The study results highlighted that all the integrated frameworks showed superior performance relative to the non-integrated (independent) model systems at corresponding analysis resolutions in terms of model fit and predictive performance. The validation exercises also highlighted the superiority of the proposed integrated frameworks. Further, capturing spatial unobserved heterogeneity and random parameter effects improved the performance of the proposed integrated frameworks. The study findings show that the application of the proposed integrated frameworks can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific solutions simultaneously.

The road transport in Kenya is the most common means oftransport for people living in both urban and rural areas. The use of bicycles for transport dates back in the pre-colonial time and has been used as a mode of transport until 2008 when the use of motorcycles became a popular mode of travel in the rural and urban areas. However, the use of bicycle as a means of travel has declined consistently over the years until now and many have shifted to the use of car, public transport and most commonly motorcycles which are popularly known as 'boda boda' in Kenya. This modal shift can be attributed to a number of factors identified as challenges in the use of bicycles as a common mode of transport in comparison to other emerging modes of transport both in rural and urban areas. However, despite this modal shift, there are a substantial number of road users who would still prefer to use the bicycle mode amid prevalence in road traffic fatalities and injuries in Kenya. The government of Kenya has established initiatives to provide safe and inclusive transport system by investing in transport infrastructure that includes cycle tracks especially for roads located in the urban cities. This has been enabled by innovation in design, mixed traffic composition, change of legislation and road design standards especially in regards to non-motorized transport in Kenya. Cycling is still low in cities in Kenya despite this effort to improve geometric design of roads. This paper explores these challenges and opportunities in cycling in Kenya focusing on Nairobi city as a case study. [From: Introduction]

Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.

Doctor of Philosophy / Civil Engineering / Sunanda Dissanayake / Safe and efficient flow of traffic through work zones must be established by improving work zone conditions. Therefore, identifying the factors associated with the severity and the frequency of work zone crashes is important. According to current statistics from the Federal Highway Administration, 2,372 fatalities were associated with motor vehicle traffic crashes in work zones in the United States during the four years from 2010 to 2013. From 2002 to 2014, an average of 1,612 work zone crashes occurred in Kansas each year, making it a serious concern in Kansas. Objectives of this study were to analyze work zone crash characteristics, identify the factors associated with crash severity and frequency, and to identify recommendations to improve work zone safety. Work zone crashes in Kansas from 2010 to 2013 were used to develop crash severity models. Ordered probit regression was used to model the crash severities for daytime, nighttime, multi-vehicle and single-vehicle work zone crashes and for work zones crashes in general. Based on severity models, drivers from 26 to 65 years of age were associated with high crash severities during daytime work zone crashes and driver age was not found significant in nighttime work zone crashes. Use of safety equipment was related to reduced crash severities regardless of the time of the crash. Negative binomial regression was used to model the work zone crash frequency using work zones functioned in Kansas in 2013 and 2014. According to results, increased average daily traffic (AADT) was related to higher number of work zone crashes and work zones in operation at nighttime were related to reduced number of work zone crashes. Findings of this study were used to provide general countermeasure ideas for improving safety of work zones.