International Conference on Transportation and Development 2022: Transportation Safety

Automotive companies have been developing advanced driver assistance systems (ADAS) that are aimed at addressing driver errors to ensure safer driving conditions. These warning and automated systems are aimed at enhancing safety by either warning the drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane or a constant headway. However, the effects of such ADAS have been seldom ventured especially on the overall driving behavior. Further, the effect of a warning system on driver behavior may vary from that of an automated system as the level of assistance these systems provide is different. This research evaluates drivers’ response to scenarios when driving vehicles with warning systems like lane departure warning (LDW), blind spot warning (BSW), and over speed warning (OSW) compared to vehicles with adaptive cruise control (ACC) and lane keep assist (LKA). The responses are also compared when driving vehicles that do not have any ADAS. Rural, urban, and freeway driving scenarios were developed in a driver simulator and tested on 51 participants in the age groups of 16 years to 65 years. The results show that both automated and warning systems affect the driving behavior. The type of driving scenario was observed to impact the type and level of response from the drivers to warning and automated systems. Automated systems were also observed to reduce the variation in driving behavior across different drivers compared to both warning systems and no ADAS conditions. Safer vehicle handling, lane-following, and car-following behaviors were observed for drivers provided with automated systems compared to drivers provided with warning systems and drivers not provided with any ADAS.

Surrogate safety measures (SSMs) are an effective alternative to assess traffic safety in the absence of crash count data. Time-based and distance-based safety surrogate measures are extensively used for traffic conflict reporting. However, the threshold selection for time-based SSMs and stopping distance calculation for distance-based SSMs are mostly made based on response time assumptions. There is little research that have used calibrated response time from real world traffic to measure time-based and distance-based SSMs. This research analyzed the shortcomings of applying time and distance-based SSMs in mixed traffic environment, using real-world vehicular trajectory data. In this context, mixed traffic is defined when traditional human-driven vehicles (TVs) share the road with autonomous vehicles (AVs). We find that the surrogate safety measures’ threshold selection for mixed traffic is an unexplored area that needs to be addressed. Furthermore, our findings point to the need for the use of calibrated response time when using time-based and distance-based SSMs.

The high number of people experiencing homelessness living in and around the country’s metropolitan areas is a critical issue for state Departments of Transportation (DOTs), as the issue of homelessness has an immediate effect on the operations and safety of state transportation infrastructure. Homeless encampments often form on DOT rights of way, particularly beneath bridges, bridge abutments, and other sheltering areas. These encampments can pose safety risks to DOT and construction employees who must access those areas and pose dangers to both motorists and those experiencing homelessness as well. To address this issue, a survey was conducted of state DOTs to better understand their policies and practices regarding homeless encampments. We conducted literature review and interviews to transportation professionals to supplement the information in the survey and analyzed the results. This study’s goals are to raise awareness of the issues surrounding homelessness on state rights of way and to recommend guidance to help state DOTs create more equitable policies to address homeless encampments. The results are intended to increase safety for all who access and use state rights of way, as well as support more equitable treatment for those experiencing homelessness.

This study presents an in-service performance evaluation (ISPE) of strong-post W-beam guardrails and high-tension median cable barriers on Interstate 65 (I-65) in the state of Alabama. Seven-year crash records (2010–2016), barrier-related features, and roadway design variables were collected in the study. Extensive effort was made through reviewing in detail police crash reports and capturing features rarely-explored in previous studies, e.g., barrier offset or lateral distance to median edge, barrier run length, left shoulder width, blockout type, embankment distance, type of guardrail hit, and number of cables/strands. The study compared ISPE of median cable barrier to strong-post W-beam guardrail systems on I-65 in Alabama and compared the safety performance of roadside W-beam guardrails to median W-beam guardrails. In addition, network screening has been undertaken to pinpoint the top segments with high barrier-related crashes (both cable barrier-related and guardrail-related) along I-65. A total of 1,090 crashes were used in the ISPE. Median cable barriers had significantly lesser severe crash involvement (3.7%) compared to median W-beam guardrails (7%). Reducing the short lateral offset from median edge for median barriers showed lower severe crash percentage for cable barriers than for W-beam guardrails. Median guardrail and median cable barrier systems had comparable success rate (close to 90%) in preventing pick-up truck crossovers. Roadside guardrails showed better performance than median guardrails along curved sections (4.1% vs. 15.2% severe crashes). One geometric design recommendation is designing cable barriers with relatively shorter run length (less than 0.57 mi) since such length had the least severe crash involvement.

Developing effective transportation safety programs for tribal lands is important due to higher crash rates and crash severities in those areas. Correspondingly, a comprehensive tribal crash reporting and crash data quality are imperative to analyze and identify high crash locations and to develop and fund effective tribal safety programs. This study presents quantitative and spatial data analyses to validate the effectiveness of tribal crash data elements on the Wisconsin police crash report with respect to three attributes: location, jurisdiction, and law enforcement agency. The results indicate that these tribal attributes are coupled with other standard location attributes, which negatively affects data quality for further analysis. The findings recommend that adding an independent subfield in the national standard crash classification for tribal elements and adding a specific tribal road type to the roadway data elements would be more effective for tribal crash reporting and analysis, which facilitates further safety analysis regarding all roadway types within tribal lands.

A high injury network (HIN) consists of mapping roadway corridors of a network and evaluating the associated number of people killed and severely injured in traffic crashes. Adoption of a HIN is recommended in Vision Zero, which is a strategy aimed at eliminating all traffic fatalities and severe injuries, while increasing safe, healthy, and equitable mobility for all road users. As part of the adoption of Vision Zero, a practical and repeatable process was developed for the city of Madison, Wisconsin. Accounting for data and resources available, the methodology consisted of network segmentation into intersections and segments, crash data collection, and evaluation of crash frequency analysis using the equivalent property damage only (EPDO). Safety levels were defined based on percentile thresholds by facility type. The results of the analysis are maps of the network with intersections, segments, and corridors—color coded according to the level of safety.

Studying traffic flow breakdown and its duration is critical for freeway operation efficiency. In this paper, a breakdown probability model and a breakdown duration model were established based on the survival analysis method, respectively. The product limit method was used for the breakdown probability model, where the breakdown likelihood was calculated as functions of both mainline flow and the combination of mainline and ramp flow. We also consider the impact of different ranges of ramp flow and truck proportion. The log-rank test results showed that the group with the lowest ramp flow (less than 132 veh/h) affected the breakdown probability significantly differently from the other three groups. However, there was no significant difference in the impact of various ranges of truck proportion. A Cox PH model was constructed to reveal the hazardous variables that influence breakdown duration. The increase in average upstream volume during the breakdown episode, the standard deviation of the downstream occupancy, and the difference of upstream speed will reduce the breakdown duration. While the difference in occupancy and speed between upstream and downstream will prolong the duration. The results of this paper have guiding significance for alleviating the congestion caused by traffic flow breakdown.

Contrast pavement markings on light colored are believed to provide superior guidance and delineation for the motorist in a variety of lighting and climatic conditions. Resent research suggests a quantifiable safety benefit linked to contrast markings related to departure type crashes. This study evaluates the current use and designs of contrast pavement markings through the deployment of a nationwide survey that targeted departments of transportation and highway agencies and identifies the crash reductions associated their use. The survey had an 88% response rate, with findings showing 72% of reporting agencies currently use contrast pavement markings in some capacity. The survey further identified the use of three design configurations, common pavement type usage, and installation cost. Crash reductions were quantified through a statistical approach with findings suggesting a crash reduction between 8% and 12% for three crash severity levels.

The Federal Motor Carrier Safety Administration (FMCSA) reported that commercial motor vehicles (CMVs), including large trucks and buses, comprised about 14% of total fatal crashes in the United States in 2018. Compared to other commercial vehicle types (e.g., taxis or commercial vans), CMVs require more driving skills and experience. Additionally, CMV drivers are more involved in long work hours, in-time delivery restrictions, and relatively inflexible working conditions. Despite the difficulties associated with long-haul CMV driving, very few studies have been documented to address driving challenges for long-haul CMV drivers. This study conducted a survey-based analysis to identify the concerns and difficulties encountered by long-haul CMV drivers in Kentucky. Questions encompassing different trucking aspects, such as drivers’ socio-demographic characteristics, CMV-related factors, and work-specific features, were prepared. The survey was then posted online on the Kentucky Transportation Cabinet (KYTC) website and on KYTC’s social media accounts, such as Facebook and Twitter. A total of 210 responses were gathered and used in the analysis. Comprehensive analysis was performed using descriptive statistics and graphical displays (such as pie/bar charts) and chi-square test of independence (to test the significant correlation between various factors vs. driver drowsiness/sleepiness and out-of-vehicle distraction). The results of the chi-square test showed that payment type, work flexibility status, presence of truck adaptive cruise control system, truck ownership status, nighttime driving, break taking, eating or drinking while driving, in-time delivery percentage, and listening to music/radio/podcasts when driving were significantly associated with the likelihood of driver drowsiness/sleepiness. Furthermore, respondents stated that lack of rest areas and parking spots, poor roadway maintenance, and lack of awareness/training for non-CMV drivers were the three main issues affecting long-haul trucking in Kentucky.

Transportation safety researchers utilize crash prediction models (CPMs) to examine the safety performance of roadway facilities. Using statistical modeling, the CPMs associate traffic and roadway design elements to crash frequency. Many studies in recent years have applied relatively advanced techniques to model crash data. Regardless of this, the traditional negative binomial (NB) model remains highly popular, probably because of the ease of estimation and the ability to accommodate overdispersion. Though the NB model performs well for data with moderate to low overdispersion, its performance is compromised in the case of highly over-dispersed data. Analysts need alternative approaches to model such data sets. The Poisson-inverse Gaussian (PIG) regression modeling framework has shown the potential to model highly dispersed data more effectively due to the flexible nature of the inverse Gaussian distribution. This study applied the PIG regression framework to model a six-year crash data for urban road segments, taking the traffic volume and roadway geometric design attributes as predictor variables. We compared the PIG models with the traditional NB models for goodness-of-fit and predictive performance. Moreover, we computed the prediction intervals (PIs) for the predicted responses at new sites to examine the level of uncertainty beyond the point estimates for the two modeling frameworks. The PIG and NB models revealed a significant association between the predictor variables (i.e., traffic volume and roadway design attributes) and crash frequency. In terms of performance (i.e., goodness-of-fit, predictive performance, and prediction intervals), the PIG models performed either better or equally well for the models developed in this study. In conclusion, the PIG models could be adopted as a potential alternative to the NB models, given the convenience in their estimation and the ability to accommodate overdispersion.

Many workers’ deaths happened in work zones in the US between 2003 and 2019, and rear-end is the most common type of crashes in the work zones. To reduce the deaths and injuries from rear-end crashes in the work zones, autonomous truck-mounted attenuator (ATMA) was developed to remove drivers from the follower truck attached with the truck-mounted attenuator (TMA). A few states in the US have started the implementation of the emerging ATMA technology in the past five years. However, there is a lack of studies summarizing implementation status of ATMA with its benefits and challenges, which could shed lights on ATMA system for the states who just started or plan to study or implement ATMA. Therefore, this study conducted semi-structured interviews with experts from four state departments of transportation who are the pioneers in ATMA implementations. The findings of this study show that safety is the main reason why states started to use ATMA system and the challenges mainly result from technology, human behavior, and regulation perspectives. Specifically, the loss of GPS signal, difficulty in motoring follower truck, and regulation of prohibiting autonomous vehicles’ operations are the main challenges.

The number of occupational deaths of the transportation industry is one of the highest at Indiana, compared with other industries. Most occupational deaths and safety issues are caused by ergonomic hazards because some types of transportation maintenance activities exceed workers’ capabilities and limitations. Safety prevention technologies are often neglected or poorly implemented in the current transportation industry. Therefore, this study assessed the prevalence of injuries and activities causing these ergonomic injuries by conducting a survey with maintenance workers working for Indiana Department of Transportation. Then the demographic analysis and analysis of means were used to evaluate injury types and activities causing these ergonomic injuries. The key finding is that back and shoulder injuries are the most common ergonomic injuries caused by lifting bags of materials, lifting frames, pulling deer, and shoveling gravels. The finding can shed lights on what future prevention technologies should focus.

Dashboard cameras and sensors were installed in 233 taxi vans in Honolulu, Hawaii. They produced many hours of naturalistic driving data (NDD) between fall 2019 and spring 2020 in the form of 20 s recorded events. The study achieved its objectives to (1) collect data from naturalistic driving events where driving maneuvers caused acceleration or deceleration in any direction of 0.5g or higher, (2) develop a database suitable for statistical analysis, (3) derive basic statistics for all variables, and (4) investigate correlations between variables. The database included a total of 402 harsh events, of which were 398 near-crashes and 4 were crashes. Several variables such as road, environmental, driver, and vehicle characteristics were coded for each event. The installation of an NDD system by the taxi company proved to be a successful tool for coaching drivers, and for providing statistically significant insights into traffic safety factors relating to near-miss events, such as wider expressways come with a higher risk for near rear-end events; urban roads without parking lower the risk of near rear-end events; light traffic density significantly reduces the risk of rear-end events on freeways; and, mobile phone usage has a positive and significant coefficient that increases the risk of highway rear-end events.

The high friction surface treatments (HFST) is an effective countermeasure to reduce crashes on horizontal curves. However, the long-term economic benefit of HFST from crash reduction has not yet been fully understood. In this study, the PaveVision3D and grip tester were applied to monitor 18 HFST sites in ten states to perform benefit-cost analysis and life cycle cost analysis. On average, the HFST achieved a benefit-cost ratio of 14.6, indicating the HFST system is a cost-effective treatment to reduce crashes and improve traffic safety. Also, the HFST achieved higher crash reduction on tight curves with a small radius. Further, the deterministic and probabilistic life cycle cost analysis incorporating the crash cost as user cost was performed to compare HFST against chip seal and diamond grinding via RealCost 2.5. Even with a higher unit cost, the HFST shows the lowest equivalent uniform annual cost (EUAC) than the other two preservation techniques due to its superior effectiveness in crash reduction when the site has more than three crashes happening every year. Therefore, the HFST is a better preservation treatment to restore skid resistance and reduce crash probabilities on risky pavement sections with good long-term economic benefits.

Annually, millions of people worldwide suffer injuries or die from car crashes. Automated speed enforcement (ASE) cameras and red-light cameras are appropriate countermeasure techniques used to mitigate the severity of crashes and decrease the number of crashes every year. ASE and red-light cameras are used in many countries and continents, including Asia, Europe, and North America. This paper will study and examine the effects of implementing ASE and red-light cameras on crash reductions. Different countries impose different penalties, and the level of enforcement varies depending on the government control and legislation. This paper will study countries like the United States, Canada, the United Kingdom, and Saudi Arabia to collect data. However, ASE is known to reduce crashes; there are still several obstacles to overcome. Some issues include the high cost of implementation and the public view of ASE. Some members of society believe that it is a method for governments to generate profit and consider it an invasion of privacy. This paper aims to measure how effective automated speed enforcement and red-light cameras are in reducing fatal injury crashes. Moreover, this paper will examine whether there is a correlation between imposing higher penalties and decreasing total crashes.

The main focus of this research is to develop a weather-responsive human-in-the-loop VSL algorithm based on Wyoming Department of Transportation (WYDOT) historical data as well as the Second Strategic Highway Research Program’s (SHRP 2) naturalistic driving study (NDS) driver behavior data to improve traffic safety and operation of roadways under adverse weather conditions. This study developed three simulation scenarios for microsimulation modeling in clear, rainy, and snowy weather conditions. The safety and operational effectiveness of the proposed variable speed limit (VSL) algorithm were evaluated using microsimulation in VISSIM, which revealed that the proposed algorithm was superior in terms of time-to-collision, total travel time, average delay, and average speed. The proposed VSL could be integrated into the connected vehicle (CV) environment aiming at regulating driving speed and preserving a safe flow of traffic by incorporating the trajectory level vehicle data as input and providing VSL data to drivers via on-board-units in real-time.

Single-vehicle crashes contribute to more fatalities compared to other crash types in the United States. Based on the age group, risk factors associated with single-vehicle fatal crashes were compared between older drivers and other drivers who have been involved in crashes at intersections in the Midwest region. In this study, a fatal crash data set was gathered for five years from the Fatality Analysis Reporting System (FARS) database. Two separate logistic regression models were developed to predict single-vehicle fatal crashes at intersections by older and other drivers. Risk factors affecting toward increased single-vehicle fatal crashes at intersections were identified for each age group, and comparisons were made with the intention of identifying suitable countermeasures. The effect of these factors was more strongly influential toward increased older driver fatal single-vehicle crashes at intersections compared to other drivers. The predictors that increase fatal single-vehicle crashes at intersections for both driver age groups were controlled intersections, two-way undivided highways, reduced speed limits, urban roadway locations, driving during nighttime, and rollover or hitting animals. Additionally, driving under influence of alcohol or drugs were determinants of elevated fatal single-vehicle crashes at intersections for other drivers but not for older drivers. The study findings are helpful in investigating the potential ways in improving the safety of not only older drivers, but also all road users.

A 2.9-mi corridor of US 29 (N. Tryon St.) comprising of Lynx Blue Line light rail transit (LRT) in the city of Charlotte, North Carolina, was considered for the analysis. A base simulation model with existing traffic conditions and LRT for the year 2018 was modeled in PTV Vissim microsimulation platform. Hypothetical scenarios with different penetration of non-motorist traffic (pedestrians and bicycles), different frequency of LRT, and change in the vehicular traffic volume were modeled to assess the effect of various modes on traffic safety performance using surrogate safety assessment model (SSAM). The evaluation is based on the conflicts between the road users during the simulation runs from the trajectory files in PTV Vissim. Notable effects on safety performance were observed across different scenarios, emphasizing the need to plan and build infrastructure by evaluating complex mobility patterns and interactions between all the road users. The methodological framework used in this research is cross-disciplinary, transferable, and can be applied to other regions.

The push to legalizing marijuana in some states has resulted in elevated crash rates. Drugs are known to be leading factor in fatal crashes with nearly 50% of drivers in fatal crashes testing positive for some type of mind-altering drug. A decrease in response rate due to drug use can elevate the risk of crashes and increase the severity of the event. This study seeks to identify the decreased reaction time and elevated crash risk associated with marijuana use and driving. The study approach utilizes a state of the art driving simulator where drivers’ reaction time to imminent crash events are evaluated. Participants for this study were selected using the participant pool procedure where members of the university community were offered the chance to drive for a gift card. Participants completed a survey on marijuana use placing them into two groups: marijuana users and non-users.

In this research, a driving simulator experiment was created to test young driver responsiveness to near-collision scenarios with and without a variety of collision warnings. The young driver subjects were separated into the classification of high school or college students. During the approximately 5-min-long experiment on a four-lane arterial roadway, subjects encountered six near-collision scenarios (three with vehicles in their lane and three with vehicles lane-changing into their lane). With each near-collision scenario, no warning or one of five collision warnings is triggered, with the order randomly selected for each subject. The five types of warnings tested include auditory warning, auditory warning with non-informative symbolic visual warning, auditory warning with informative symbolic visual warning, auditory warning with non-informative alphanumeric visual warning, and auditory warning with informative alphanumeric visual warning. Results from this exploratory analysis show that different groups of drivers respond better to some warning systems than other warning systems.

The need for roadway safety countermeasures ascended with the growth of the automobile industry and the increase in car ownership during the mid-20th century. Fastened seatbelts, along with many other factors, countermeasures, and accessories, help save lives and prevent injuries to road users. The decade of 1960s saw the beginning of the safety movement, which was followed by the 1970s. There was little support or interest among the population regarding safety belt legislation, so by the 1990s, acceptance of automotive safety equipment had become a social norm. The state of South Carolina modified its safety belt law to be a primary law on December 11, 2005. This study applies a before and after study to track the effect of the primary law legislation on daytime and nighttime belt use. Seven analysis locations were selected, and data were collected during daytime and nighttime. The population was broken down into categories based on gender and vehicle type, and belt use for each was tracked. Statistical analysis showed, in most cases, that a primary law was linked to a significant positive effect on drivers, especially in a long-term study (years 2016 and 2005). However, a comparison of immediately before-and-after the law’s passage (years 2006 and 2005) showed no significant effect on drivers’ behavior. Yet, belt use increased over time so that over 90% of drivers in the case study (and generally in the United States) now fasten their seatbelts while driving.

Traffic safety is one of the biggest challenges of connected and autonomous vehicles (CAVs). The safety of CAVs relies largely on the awareness of surroundings provided by the surveillance systems and motion detection in the ego vehicle. Many factors, such as weather, interferences, and sensibilities, contribute to the effectiveness of real world detection. We propose a system that mitigates CAV safety by sending collision warnings to the vehicles around. The system analyzes the trajectories of the vehicles embedded in the basic safety massages (BSMs) generated in the CAVs. Our system overcomes the obstacles in previous research studies when the time to perform a chain of tasks to avoid a crash after the vehicles come into the effective vehicle-to-vehicle (V2V) range may not be adequate. This paper focuses on the conflict detections using the real time BSMs with the following steps: (1) redefining the conflict, a key concept of surrogate safety analysis, originated from traffic simulation and improved for our system; (2) developing the mathematical algorithms to identify the conflicts; and (3) implementing the algorithms in the in-vehicle subsystem. The algorithm is tested on the SHARPII crash data, which contain data similar to BSMs. This paper is part of our ongoing project of Automatic Safety Diagnosis in Connected Vehicle Environment.

The main objective of this study is to investigate the impact of adverse weather on traffic stream behavior by calibrating key traffic flow parameters on Interstate 80 (I-80) in Wyoming. In order to achieve the study objectives, data from four field sensors and nearby road weather information system (RWIS) were collected from the Wyoming Department of Transportation (WYDOT) and utilized to calibrate key traffic stream parameters. Calibrated the Highway Capacity Manual (HCM) models in different road surface conditions were fitted using the field sensor data. Most of the traffic flow parameters were found to be well-fitted. It was also observed that the percentage of heavy trucks on I-80, Wyoming, is extremely high, especially in adverse weather compared to clear weather conditions. The calibration procedure conducted in this study provided valuable insights regarding the key macroscopic traffic parameters considering adverse weather conditions.

A 2018 study of performance measures for the Utah Department of Transportation’s (UDOT) Incident Management Team (IMT) program concluded that the program was cost effective and benefited Utah motorists. During the 2018 legislative session, UDOT received funding to expand its IMT program. To determine the benefits of expanding the IMT program, a comparison of performance measures for 2018 and 2020 incident data was conducted. In addition, data regarding the affected volume, the excess travel time, and the excess user cost associated with incident congestion were gathered. The effects of the COVID-19 pandemic affected traffic volumes during this study, and statistical analyses were utilized to account for volume differences between the two years. Results indicated that the expansion of the IMT program has allowed UDOT to respond more consistently to incidents and respond to a larger quantity of incidents over a larger coverage area and in extended operating hours.

Many existing cloverleaf interchanges were built over 60 years ago based on old design guidelines. Many of those cloverleaf interchanges have operational problems due to the inconsistency with current traffic demands. Cloverleaf interchanges create weaving areas between vehicles entering and leaving the freeway. This could cause an unsafe condition, especially in system interchanges with high speeds on freeways and low speeds on loop ramps. This research evaluates the traffic operation and safety of a new interchange design called left hook, which might show potential in mitigating cloverleaf interchange problems. The left hook interchange removes all the four weaving areas of a cloverleaf interchange. The evaluation was done using VISSIM and the Surrogate Safety Assessment Model (SSAM) in this study. Overall, 72 simulation scenarios (with over 360 runs) were tested to include various conditions in terms of traffic volumes, turning traffic ratios, traffic distributions, and truck ratios. Based on the results, the left hook interchange performed better with fewer queues, stoppages, and rear-end conflicts. However, to reach a more solid conclusion on the new design, more studies should be conducted.

Location prioritization is a crucial phase of transportation planning and requires a rational approach to achieve acceptable results. This research applies an integrated multi-criteria decision-making framework in location prioritization for road safety improvement. The objective of the study was to prioritize two locations from each of four national highways, four districts, and four urban neighborhoods in Bangladesh. The framework adopted a disutility-based approach integrated with the best-worst method (BWM) to determine the most hazardous locations among the alternatives from a road safety standpoint. The alternatives were evaluated based on criteria and sub-criteria, such as traffic parameters and demographic factors. The relative weights of criteria and sub-criteria were estimated from pair-wise comparison conducted by a team of experts, decision-makers, and stakeholders using a modified Delphi approach. The criteria concerning safety hazards were considered as disutility and appraised as a negative value, whereas the facility-related criteria were considered as utility and appraised as a positive value. Altogether, the total sum of disutility and utility value for a particular location revealed the existing safety scenario. Results show that the developed models were able to effectively prioritize among the alternatives maintaining a very high consistency ratio. The findings presented in this paper can work as a framework for location prioritization globally.