No description available.

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Disagreements regarding to what degree right-turn lanes improve or worsen the safety of intersections and driveways provided the motivation and the need for this study. The objectives of this study were to: a) carry out an in-depth study to determine the safety impacts of right-turn movements in different contexts, and b) develop safety-based volume warrants for right-turn lanes if safety indeed improves. Lack of adequate study on the applicability of past warrants and guidelines for the specific context of right-turn movements made from major uncontrolled approaches at unsignalized intersections, and particularly driveways, on two-lane roadways provided the scope for this study. Five-year historical data of statewide traffic crashes reported on Minnesota’s twolane trunk highways were analyzed using binary/multinomial logistic regressions. Conflicts due to right turns were analyzed by fitting least squares conflict prediction models based on the data obtained from field surveys and traffic simulations. The safety impacts of rightturn lanes were determined through crash-conflict relationships, crash injury severity, and crash and construction costs. The study found that the probabilities of right-turn movement related crash ranged from 1.6 to 17.2% at intersections and from 7.8 to 38.7% at driveways. Rear-end, samedirection- sideswipe, right-angle and right-turn crash types constituted 96% of right-turn movement related crashes. Rear-end crash probabilities varied from 13.7 to 46.4% at approaches with right-turn lanes and from 37.9 to 76.9% otherwise. The ratios of rearend/ same-direction-sideswipe crashes to conflicts were 0.759 x 10^6 at approaches with right-turn lanes and 1.547 x 10^6 otherwise. Overall, right-turn lanes reduced right-turn movement related crash occurrences and conflicts by 85% and 80%, respectively. Right-turn lanes also reduced crash injury severity, hence, reducing the economic cost by 26%. Safety benefits, in dollars, realized with the use of right-turn lanes at driveways were 29% and 7% higher compared to those at intersections at low and high speed conditions respectively for similar traffic conditions. Depending on roadway conditions, interest rate and construction costs, the safety-based volume thresholds ranged from 3 to 200 right turns per hour during the design hour at intersection approaches, and from 2 to 175 right turns at driveway approaches.

Intersections often experience severe rutting in the asphalt concrete layers due to slow moving, high loads, acceleration, deceleration and turning. This thesis aims to investigate the effectiveness of grouted macadam, open graded asphalt with its voids filled with cement grout, as a pavement material at intersections. This was done by investigating the properties of grouted macadam through a literature review and performing a life-cycle cost analysis comparing grouted macadam and asphalt concrete as pavement materials at an intersection. Grouted macadam is found to be similar to asphalt concrete concerning the relation between stiffness and temperature and frequency as well as fatigue behaviour. The main differences are that grouted macadam is stiffer, stronger and not prone to rutting. Thus it would be suitable to address rutting problems. The main drawbacks are that construction demands extra time and precision and that it is expensive compared to asphalt concrete. The life-cycle cost analysis showed that assuming that the grouted macadam has a service life of twenty years and rehabilitation of the asphalt concrete in the form of mill and refill takes place every fourth year the life-cycle costs are approximately the same. A sensitivity analysis was performed that showed that local variations can have large impact on the life-cycle costs. The main conclusion is that grouted macadam can be effective as a pavement material at intersections that experiences severe rutting and frequently reoccurring rehabilitations.

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Cycling provides individual and societal benefits, such as improved health [1], faster intra-urban commuting [2], lower C02 emissions [3] and all in all lower societal costs [4] compared to most other traffic modes. However, the national average of the cycling mode share was only around 10% in 2008 and has not increased remarkably ever since [5]. Several studies indicate that the lack of subjective safety may be a crucial reason to refuse using the bicycle [6, 7].While there is evidence on how to improve subjective safety through infrastructure on road sections [8], there is none concerning intersections or roundabouts yet. To close that gap, we investigate subjective safety at junctions depending on different infrastructure designs. [From: Introduction]

The probability of a cycling crash is much higher at intersections as along the road. A number of reasons contribute to this difference, for example car drivers overlooking cyclists when taking a turn. There have been attempts to quantify the risk at prototypical, un-signaled intersections featuring different levels of cycling infrastructure, as well as cyclists' perception of risk of these intersections. However, these attempts are limited to regular, four-arm intersections, although irregular intersections featuring both a higher and a lower nwnber of anns as weil as odd angles are likely to pose additional challenges for cyclists. There appears tobe little research on the question how the complexity and layout of such intersection affects cyclists perception of risk, as weil as their allocation of attention towards the different arms of an intersection. In, we presented a first approach to taclde this issue in a virtua1 reality (VR) based setup. We found evidence that tbe type oftum affected the subjective risk (e.g. with. a higher risk associated with situations requiring a sharp turn or to continue to an offset road), but no effects of the general position of an intersection arm in relation to the cyclist' traveling trajectory. However, the repeated exposure to the same intersection in this stu.dy limits the conclusiveness of the findings. We thus developed a more flexible virtual environment allowing us to investigate the attention allocation and risk. perception at various types of intersections.

Bicycle usage is increasing in urban (as well as rural) areas, which increases demand for better and safer infrastructure. Whilst the total number ofbicycle fatalities in European countries has been stable over the last ten years (:::: 2.000 fatalities per year for all European Union member states), bicycle fatalities and injuries in Germany have been increasing in this time. About two-thirds of all bicycle crashes in Germany occur at intersections, this proportion is highe:r than in Denmark and the N ethe:rlands (three-fi:fths). lntersections are tbus of high relevance for bicyclists' safety andin addition, they require sophisticated research methods because of their complex designs and the high numbers and types of uscr interactions and conflicts compared to street sections. This study analyses determinants of bicycle crashes at 269 signalised intersections in two major eitles in Germany (Dresden, Munich) as the basis for developing evidence-based recomm.endations for improving bicyclists' safety at existing intersections and for ensuring high safety levels at newly planned intersections from the very beginning. This study is part ofthe research project SiRou (nrvp.de/21520). The project is funded by the German Federal Ministry for Digital and Transport within the National Cycling Plan 2020(NRVP).

Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by type of collision as well as injury level, at signalized intersections. The first objective was to determine if there is a difference between important variables for models based on individual crash types or severity levels and aggregated models. The second objective of this research was to investigate the quality and completeness of the crash data and the effect that incomplete data has on the final results. A detailed and thorough data collection effort was necessary for this research to ensure the quality and completeness of this data. Multiple agencies were contacted and databases were crosschecked (i.e. state and local jurisdictions/agencies). Information (including geometry, configuration and traffic characteristics) was collected for a total of 832 intersections and over 33,500 crashes from Brevard, Hillsborough and Seminole Counties and the City of Orlando. Due to the abundance of data collected, a portion was used as a validation set for the tree-based regression. Hierarchical tree-based regression (HTBR) and ordered probit models were used in the analyses. HTBR was used to create models for the expected number of crashes for collision type as well as injury level. Ordered probit models were only used to predict crash severity levels due to the ordinal nature of this dependent variable. Finally, both types of models were used to predict the expected number of crashes. More specifically, tree-based regression was used to consider the difference in the relative importance of each variable between the different types of collisions. First, regressions were only based on crashes available from state agencies to make the results more comparable to other studies. The main finding was that the models created for angle and left turn crashes change the most compared to the model created from the total number of crashes reported on long forms (restricted data usually available at state agencies). This result shows that aggregating the different crash types by only estimating models based on the total number of crashes will not predict the number of expected crashes as accurately as models based on each type of crash separately. Then, complete datasets (full dataset based on crash reports collected from multiple sources) were used to calibrate the models. There was consistently a difference between models based on the restricted and complete datasets. The results in this section show that it is important to include minor crashes (usually reported on short forms and ignored) in the dataset when modeling the number of angle or head-on crashes and less important to include minor crashes when modeling rear-end, right turn or sideswipe crashes. This research presents in detail the significant geometric and traffic characteristics that affect each type of collision. Ordered probit models were used to estimate crash injury severity levels for three different types of models; the first one based on collision type, the second one based on intersection characteristics and the last one based on a significant combination of factors in both models. Both the restricted and complete datasets were used to create the first two model types and the output was compared. It was determined that the models based on the complete dataset were more accurate. However, when compared to the tree-based regression results, the ordered probit model did not predict as well for the restricted dataset based on intersection characteristics. The final ordered probit model showed that crashes involving a pedestrian/bicyclist have the highest probability of a severe injury. For motor vehicle crashes, left turn, angle, head-on and rear-end crashes cause higher injury severity levels. Division (a median) on the minor road, as well as a higher speed limit on the minor road, was found to lower the expected injury level. This research has shed light on several important topics in crash modeling. First of all, this research demonstrated that variables found to be significant in aggregated crash models may not be the same as the significant variables found in models based on specific crash types. Furthermore, variables found to be significant in crash type models typically changed when minor crashes were added to complete the dataset. Thirdly, ordered probit models based on significant crash-type and intersection characteristic variables have greater crash severity prediction power, especially when based on the complete dataset. Lastly, upon comparison between tree-based regression and ordered probit models, it was found that the tree-based regression models better predicted the crash severity levels.

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.