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ItemESTIMATING THE EFFECT OF VEHICLE SPEEDS ON BICYCLE AND PEDESTRIAN SAFETY ON THE GEORGIA ARTERIAL ROADWAY NETWORK(Georgia Institute of Technology, 2020-07-09) Arias, Daniel F.Despite a decreasing trend in overall crashes, bicyclist and pedestrian fatalities have increased steadily since 2009 in the United States (Cicchino & Hu 2016). A large body of research suggests vehicle speeds are a key contributing factor for crashes (Elvik et al. 2019). Furthermore, vehicle impact speed has been identified as the principal determinant of severity and death in the event of a pedestrian crash (Tefft 2013). However, there have been few studies of bicycle or pedestrian crash probability that incorporate detailed vehicle speed data. Newly available probe vehicle data in the state of Georgia makes it possible to study the relationship between bicycle and pedestrian crashes and speed across the network of Georgia arterial roadways. The analysis uses INRIX® speed data and the Georgia DOT crash database and relates these data in a Negative Binomial crash count model for the year 2017. Models using speed percentiles (85th, 50th and 15th) and models using speed differences (85th - 50th and 50th - 15th percentile) are compared. A small set of covariates are included. This study shows that the high speed difference (85th - 50th percentile) is a robust indicator of bicycle and pedestrian crash frequency on Georgia arterial roadways. The high speed difference outperformed the low speed difference (50th - 15th percentile), suggesting that the high end of the distribution is more important to crash prediction than the low end. Additionally, speed percentile models showed no clear, intuitive relationship to bicycle and pedestrian crashes. In light of these results, planners and policymakers should identify arterial roadways with high speeds, high spread of speeds at the top end of the distribution, and high bicyclists and pedestrian activity. To do so, a complete bicycle and pedestrian count data collection effort is needed. These target roadways should be considered for treatments which prioritize the reduction of the fastest speeds and limitation of exposure for unprotected road users. Finally, the practice of setting the speed limit at the 85th percentile speed (NTSB 2017) should end. Road user safety must supplant vehicle throughput and access to create a sustainable, equitable and just transportation system in Georgia.
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ItemModeling the impact of road grade on vehicle operation, vehicle energy consumption, and emissions(Georgia Institute of Technology, 2018-08-08) Liu, HaobingMotor vehicle emissions and their impacts on local air pollutant concentrations are a primary concern in cities. Properly quantifying energy and emissions is the key step in identifying the major sources of air pollution, evaluating whether transportation activities are consistent with air quality goals, and providing decision makers with reference for implementation of new policies for sustainable development. Mathematical models are commonly used to predict vehicle energy consumption and emissions. Vehicle-specific power (VSP) is widely used in such models to evaluate engine load, and it is represented as a function of vehicle mass, vehicle dynamic parameters (rolling/drag coefficient), driving behavior (speed and acceleration) and road conditions (gravitational acceleration and road gradient). In the U.S. Environmental Protection Agency’s (USEPA’s) MOVES (MOtor Vehicle Emission Simulator) model, speed and VSP levels are tied to vehicle energy consumption and emission rates. Detailed and accurate speed-acceleration joint distributions (SAJDs, also known as Watson plots) can be used to reflect onroad activity required for calculating the distribution of activities in MOVES VSP and speed bins, and thus for estimating vehicle energy consumption and emissions. Road grade is also a critical variable that affects engine operations, as uphill grades require that the engine perform additional work against gravity in the direction of vehicle motion (while downhill grades obtain an energy benefit). Real-world vehicle speed and acceleration can be easily collected using low-cost global positioning system (GPS) data loggers, on-board diagnostics (OBD) system data loggers, and smartphones apps. But, The effect of road grade is usually ignored in emission modeling. On the other hand, very little attention has been paid to the interaction between real-world road grade and onroad activity patterns and the resulting impact on energy use and emissions. However, road grade is expected to impact vehicle operations due to drivers’ response to uphill and downhill driving, or due to vehicle mechanical performance. It is currently unclear that how speed and accelerations vary across different road grade levels, and how the interaction of driver behavior and road grade affect engine power, energy consumption, and emissions modeling. This study is directed at answering two issues: 1): how road grade impacts vehicle speed and acceleration distributions, and how such distributions vary across vehicle types, roadway types, traffic conditions, etc., and 2): how significant the impact of integrating grade interactions is with respect to energy, emissions, and air quality modeling.
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ItemSafety analysis of centerline rumble strips along rural two-lane undivided highways in Georgia(Georgia Institute of Technology, 2017-12-14) Pena, Marisha S.Vehicle crashes involving crossing over the roadway centerlines are among the most severe types of collisions nationwide. To address this issue, the Georgia Department of Transportation (GDOT) started implementing centerline rumble strips (CLRS) in rural locations across Georgia in 2005 and 2006. CLRS produce both an audible and tactile warning to alert drivers of impending lane departure into the lane of oncoming traffic. As of 2015, approximately 200 miles of CLRS have been installed by GDOT as a countermeasure for crossover crashes along rural two-lane undivided highways. This study evaluates the safety impacts of CLRS deployments in Georgia by analyzing two years of before and two years of after periods to evaluate the safety impacts associated with nine treatment sites and a control group of comparison sites with similar traffic and physical characteristics. The study dataset consisted of 154 target crashes along 126.46 miles of CLRS treatment sites and 1,391 crashes along control group sites. The empirical Bayes method was used to develop a crash modification factor for CLRS of 0.66, indicating a 34% reduction in crashes involving centerline crossings associated with the installation of centerline rumble strips. The sample size of fatal and injury crashes was too small to obtain separate crash modification factors for fatal crashes and injury crashes. The favorable crash modification factor (0.66) found in this study supports wider use of centerline rumble strips as a safety measure to address crashes involving vehicles that cross the centerline of the roadway. In addition to the safety analysis, this study also provided insights into the crash reporting process by conducting a comprehensive manual review of more than 17,000 crash reports. Approximately 6% of target crashes were found to be misclassified due to coding errors.
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ItemSensitivity analysis of operational performance under conventional diamond interchange and diverging diamond interchange(Georgia Institute of Technology, 2017-12-11) Park, Sung JunRapidly growing traffic volumes and changes in traffic patterns over time have forced many intersections and interchanges into sub-optimal operation. Diverging diamond interchange (DDI) is one of many innovative interchange designs currently being proposed and implemented to better accommodate these changes. This study compares the operational performance of a conventional diamond interchange (CDI) and a DDI at different traffic volumes and turning movement combinations, and explores conditions for which one interchange design may be more advantageous over the other. To achieve this objective, traffic simulation models built using the microscopic simulation software, VISSIM, and procedures involving the Critical Lane Volume (CLV) method were used to conduct sensitivity analyses at different traffic conditions and to explore differences in delay, travel time, queue length, number of stops, and volume-to-capacity ratio between the two interchange designs studied. The results of the study show that the DDI will have better operational performance at high cross street traffic volumes with high left-turn ratio (above 50%), while the CDI will perform better at low cross street traffic volumes with low left-turn ratio (below 30%). The through/left proportion where the CDI and DDI has similar performance is dependent to the cross street cross sections. This study is one of the first to examine in detail the parameters and conditions that are best accommodated by the DDI related to conventional interchanges. Findings from this study can support planning and decision making processes associated with the implementation of DDIs.