A GIS-based analysis for transportation accessibility, disaster preparedness, and rural libraries’ roles in community resilience

We present a case in which we used a geographic information system (GIS) framework to gather, analyze, and compare two rural library systems’ accessibility during Hurricane Michael’s devastating strike on the Florida Panhandle in 2018. Outreach to rural communities is always challenging, but in disasters, connecting with vulnerable communities becomes nearly impossible considering widespread destruction and lack of resources to travel obstructed distances. To understand disaster access to libraries, we used GIS modeling to explore the connections among public libraries, their communities, and built environment (e.g., population densities, transportation infrastructure). Our findings identified access issues for libraries in each county, which can inform disaster preparedness, response, and recovery efforts and improve delivery of valuable resources to all community members. Implications for library directors, librarians, county emergency management officers, and affected communities using travel times between population block groups are provided

Robust Optimization of Bus Stop Placement Based on Dynamic Demand Using Meta Heuristic Approaches: A Case Study in a Developing Country

The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system

Traffic Operation and Safety Analysis on an Arterial Highway: Implications for Connected Vehicle Applications

© 2018 IEEE. This paper presents the operational and safety analysis of an arterial highway establishing benchmarks before deploying connected vehicle (CV) technologies. This is especially critical for the safety of at-risk populations such as older adults. The study corridor is on US 90 located in Tallahassee, Florida. Two operational performance measures were used in the analysis which are travel time reliability and delay. For the safety analysis, crash topology and the level of safety benefits likely to accrue in the study corridor due to the implementation of CV applications and automatic traffic signal performance measures were assessed by predicting the likelihood of crash reduction by type based on the preponderance of literature review

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas

Statistical and Spatial Analysis of Hurricane-induced Roadway Closures and Power Outages

Hurricanes lead to substantial infrastructure system damages, such as roadway closures and power outages, in the US annually, especially in states like Florida. As such, this paper aimed to assess the impacts of Hurricane Hermine (2016) and Hurricane Michael (2018) on the City of Tallahassee, the capital of Florida, via exploratory spatial and statistical analyses on power outages and roadway closures. First, a geographical information systems (GIS)-based spatial analysis was conducted to explore the power outages and roadway closure patterns in the city including kernel density estimation (KDE) and density ratio difference (DRD) methods. In order to provide a more detailed assessment on which population segments were more affected, a second step included a statistical analysis to identify the relationships between demographic- and socioeconomic-related variables and the magnitude of power outages and roadway closures caused by these hurricanes. The results indicate that the high-risk locations for roadway closures showed different patterns, whereas power outages seemed to have similar spatial patterns for the hurricanes. The findings of this study can provide useful insights and information for city officials to identify the most vulnerable regions which are under the risk of disruption. This can lead to better infrastructure plans and policies

Automated Satellite-based Assessment of Hurricane Impacts on Roadways

During extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a co-voting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 201

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population

Development of Safety Performance Functions for Restricted Crossing U-Turn (RCUT) Intersections

Conventional intersection designs are known to be problematic and unreliable when handling the complexity associated with the heavy traffic volume and travel demand on today’s roadways. Alternative innovative and safer designs such as the restricted crossing U-turn (RCUT) intersection can address these complex problems. However, there is still a gap in the literature concerning the safety performance analysis of RCUT intersections. Consistent with this goal, a comprehensive search was performed to identify RCUTs in the US and collect the relevant data needed to create appropriate safety performance functions and crash modification factors/functions for RCUTs. As such, different safety performance function (SPF) and crash modification factors/function (CMF) models were developed for signalized and unsignalized RCUTs with a focus on all crashes or fatal and injury crashes only, which span from complex models to relatively simple and easily implementable models. This can aid in creating a flexibility for safety agencies or officials that can prefer more complex models when sufficient data are available. Findings indicate that RCUTs have the potential to reduce the number of fatal and injury (F&I) crashes substantially at problematic locations and specifically illustrate that the selection of RCUT location depends significantly on the traffic volumes of major and minor approaches as well as their ratios. The developed SPFs and CMFs can be successfully used by transportation agencies to make informed decisions on the evaluation and justification of the installation of RCUTs