Electric vehicles can be perceived as a means to achieve carbon reduction, energy efficiency, and sustainable development of the transportation industry. Electric vehicle sales and deployment are increasing rapidly over time. However, electric vehicle deployment should be conducted in a planned manner, as electric vehicles have some limitations (e.g., limited driving range, refueling capacity, carrying capacity). The electric vehicle scheduling problem should be studied in detail to overcome such limitations, as it addresses them while optimizing the paths and timetables of electric vehicles. A number of studies have been dedicated towards electric vehicle scheduling. Yet, there is a lack of survey studies that cover a structural recapitulation of the electric vehicle scheduling efforts and provide a thorough overview of the existing tendencies, operations research aspects, problem-specific properties, and future research needs. For this reason, this study offers a structured survey of the existing research studies, which assessed electric vehicle scheduling. The collected studies are grouped into three categories for a detailed review, namely general electric vehicle scheduling, electric vehicle scheduling with power grid considerations, and electric vehicle scheduling with environmental considerations. A detailed description of the relevant studies along with a summary of findings and future research needs are provided for each of the study categories. In addition, a representative mathematical model is outlined for each study category in order to guide the future research. The outcomes of this research are expected to provide interesting and important insights to different groups of professionals in the field of electric vehicles.

During the COVID-19 pandemic, there were significant restrictions on the transportation of food products in Indonesia. The research objective of this study was to investigate the extent to which these restrictions impacted changes in marketing margins at the provincial level in Indonesia. The approach taken was through the examination of trade and freight margin statistical data before the pandemic (2019) and after the pandemic (2020) across a number of different commodity markets: rice, shallots, red chilli pepper, beef, chicken meat and eggs, sugar, and cooking oil. The evidence indicates that the pandemic brought a rapid rise in Indonesian domestic prices as a result of purchasing panic at its start. But after the imposition of transportation restrictions, there were wide variations: some durable food options experienced increased marketing margins, whereas non-durables tended to experience decreased marketing margins in some regions, as fresh products such as red chillies and shallots were discarded as a result of declining consumer purchasing power. The conclusion for policymakers is that any future restrictions should take into account this likely difference in response, in order to minimise economic disruption by calibrating support along the supply chain.

Drones are receiving popularity with time due to their advanced mobility. Although they were initially deployed for military purposes, they now have a wide array of applications in various public and private sectors. Further deployment of drones can promote the global economic recovery from the COVID-19 pandemic. Even though drones offer a number of advantages, they have limited flying time and weight carrying capacity. Effective drone schedules may assist with overcoming such limitations. Drone scheduling is associated with optimization of drone flight paths and may include other features, such as determination of arrival time at each node, utilization of drones, battery capacity considerations, and battery recharging considerations. A number of studies on drone scheduling have been published over the past years. However, there is a lack of a systematic literature survey that provides a holistic overview of the drone scheduling problem, existing tendencies, main research limitations, and future research needs. Therefore, this study conducts an extensive survey of the scientific literature that assessed drone scheduling. The collected studies are grouped into different categories, including general drone scheduling, drone scheduling for delivery of goods, drone scheduling for monitoring, and drone scheduling with recharge considerations. A detailed review of the collected studies is presented for each of the categories. Representative mathematical models are provided for each category of studies, accompanied by a summary of findings, existing gaps in the state-of-the-art, and future research needs. The outcomes of this research are expected to assist the relevant stakeholders with an effective drone schedule design.

Emergencies, such as pandemics (e.g., COVID-19), warrant urgent production and distribution of goods under disrupted supply chain conditions. An innovative logistics solution to meet the urgent demand during emer-gencies could be the factory-in-a-box manufacturing concept. The factory-in-a-box manufacturing concept de-ploys vehicles to transport containers that are used to install production modules (i.e., factories). The vehicles travel to customer locations and perform on-site production. Factory-in-a-box supply chain optimization is associated with a wide array of decisions. This study focuses on selection of vehicles for factory-in-a-box manufacturing and decisions regarding the optimal routes within the supply chain consisting of a depot, sup-pliers, manufacturers, and customers. Moreover, in order to contrast the options of factory-in-a-box manufacturing with those of conventional manufacturing, the location of the final production is determined for each customer (i.e., factory-in-a-box manufacturing with production at the customer location or conventional manufacturing with production at the manufacturer locations). A novel multi-objective optimization model is presented for the vehicle routing problem with a factory-in-a-box that aims to minimize the total cost associated with traversing the edges of the network and the total cost associated with visiting the nodes of the network. A customized multi-objective hybrid metaheuristic solution algorithm that directly considers problem-specific properties is designed as a solution approach. A case study is performed for a vaccination project involving factory-in-a-box manufacturing along with conventional manufacturing. The case study reveals that the devel-oped solution method outperforms the epsilon-constraint method, which is a classical exact optimization method for multi-objective optimization problems, and several well-known metaheuristics.

A significant number of accidents occur each year at level crossings globally. Substantial efforts are being made by different railway authorities and other stakeholders to prevent accidents by installing various countermeasures at level crossings (e.g., mountable curbs, two-quad gates, four-quad gates). However, due to budgetary constraints, it is not possible to deploy countermeasures at all level crossings. Besides, countermeasures have certain effectiveness factors and the associated installation cost. Usually, the cost of the countermeasure increases with its effectiveness. Therefore, it is essential to select the most effective countermeasures at the riskiest level crossings. The implementation of countermeasures at level crossings may lead to certain negative externalities as well. In particular, the deployment of countermeasures can result in a decreased traffic flow, causing traffic delays and adversely affecting the continuity of freight and passenger train movements. This study proposes a multi-objective mathematical model for resource allocation among level crossings, which aims not only to minimize the total hazard severity due to potential accidents but the associated traffic delays as well. Exact and heuristic solution approaches are designed to solve the developed multi-objective model. A set of computational experiments are conducted for the level crossings located in the State of Florida (United States). The results demonstrate superiority of the exact optimization method, as it obtained optimal Pareto Fronts within acceptable computational time. Moreover, a number of sensitivity analyses are conducted to showcase certain managerial insights that would be of interest to railway authorities and other stakeholders involved in level crossing safety improvements.

The Internet of Things (IoT) symbolizes numerous devices which are connected globally through the internet technology and are able to collect and share relevant data. The IoT has thus achieved a significant advancement in the field of sensors, networks, and communication technologies, such as long-term evolution (LTE) technology, fifth generation (5G) technology, wireless sensor networks (WSN), and others. Apart from technological advancements, the ability of IoT to run fully embedded (with or without an operating system), gather real-time data, estimate physical parameters, facilitate decision making based on the data gathered, use of various networks (e.g., local area networks (LAN), low-power wide-area network (LPWAN), cellular LPWAN) has provided enormous opportunities for its applications in the railway industry and other domains. The current study performs a comprehensive holistic survey of various IoT technologies that can be used in railway operations, management, maintenance, video surveillance, and safety at level crossings. This study also discusses current trends in the IoT, emerging IoT technologies, green IoT applications, and various research studies that have been conducted in the areas related to railway applications. Furthermore, various challenges that are associated with the IoT applications are discussed along with potential efforts that can be made to overcome these challenges. The outcomes of this work are expected to offer important insights regarding the applicability of IoT technologies for sustainable railway transportation, their future potential, operational benefits to relevant stakeholders and authorities, as well as critical future research needs that have to be addressed in the following years.

Factory in a box (FiB) is an emerging technology that meets the dynamic and diverse market demand by carrying a factory module on vehicles to perform on-site production near customers’ locations. It is suitable for meeting time-sensitive demands, such as the outbreak of disasters or epidemics/pandemics. Compared to traditional manufacturing, FiB poses a new challenge of frequently reconfiguring supply chain networks since the final production location changes as the vehicle carrying the factory travels. Supply chain network reconfiguration involves decisions regarding whether suppliers or manufacturers can be retained in the supply chain or replaced. Such a supply chain reconfiguration problem is coupled with manufacturing process planning, which assigns tasks to each manufacturer that impacts material flow in the supply chain network. Considering the supply chain reconfigurability, this article develops a new mathematical model based on nonlinear integer programming to optimize supply chain reconfiguration and assembly planning jointly. An evolutionary algorithm (EA) is developed and customized to the joint optimization of process planning and supplier/manufacturer selection. The performance of EA is verified with a nonlinear solver for a relaxed version of the problem. A case study on producing a medical product demonstrates the methodology in guiding supply chain reconfiguration and process planning as the final production site relocates in response to local demands. The methodology can be potentially generalized to supply chain and service process planning for a mobile hospital offering on-site medical services.

The level crossings in the United States experience a significant number of accidents every year. The accidents can be reduced with the application of various countermeasures (e.g., traffic signal preemption, flashing lights, barrier cubs, gates). However, the application of countermeasures for all the level crossings in the United States is not feasible due to monetary limitations. Moreover, each countermeasure has a unique level of effectiveness and installation cost (e.g., the most effective countermeasures are typically more expensive than the least effective ones). Hence, selection of potent and cost-effective countermeasures at the riskiest level crossings is imperative to improve safety. While improving safety at level crossings with the application of countermeasures, there is a significant risk of waning highway vehicle flows, increasing delays, and negatively affecting the continuity of passenger and freight flows. In such a scenario, multi-objective resource allocation models could be instrumental, since such models can analyze the tradeoffs between conflicting objectives (e.g., minimizing the number of accidents vs. minimizing the total delay). Hence, this chapter presents a framework for multi-objective resource allocation to minimize the number of accidents and to minimize the total delay at level crossings. Furthermore, various methods for quantifying the number of accidents as well as delays due to the application of countermeasures at level crossings are reviewed. Solution methods for multi-objective resource allocation models, including exact and approximate optimization approaches, are also discussed. Finally, future research avenues for multi-objective resource allocation among level crossings are outlined.

An efficient product distribution is critical for proper supply chain operations. Many supply chains handle perishable products that decay over time. Due to mismanagement of supply chain operations, a significant portion of perishable products is wasted, resulting in substantial monetary losses. Cross-docking terminals (CDTs) have been widely used in cold supply chains for the product distribution but have not received adequate attention in the scientific literature. To improve the efficiency of perishable product distribution, this study introduces for the first time a novel mixed-integer mathematical formulation for the truck scheduling optimization at a cold-chain CDT. The model explicitly captures the decay of perishable products throughout the service of arriving trucks and accounts for the presence of temperature-controlled storage areas that are specifically designated for perishable products. The objective minimizes the total cost incurred during the truck service. Considering the complexity of the proposed model, a customized Evolutionary Algorithm is developed to solve it. The computational performance of the developed algorithm is assessed throughout the numerical experiments based on a detailed comparative analysis against the other metaheuristics. The developed Evolutionary Algorithm is found to be the most promising metaheuristic, considering both solution quality and CPU time perspectives. Furthermore, the proposed algorithm demonstrates an acceptable stability of the solution quality at termination. A set of additional sensitivity analyses are performed in order to draw some significant managerial implications, which would be of potential interest to the supply chain stakeholders that are involved in the distribution of perishable products in cold supply chains.

Automation is expected to effectively address the growing demand for passenger and freight transportation, safety issues, human errors, and increasing congestion. The growth of autonomous vehicles using the state-of-the-art connected vehicle technologies has paved the way for the development of passenger and freight autonomous trains (ATs), also known as driverless trains. ATs are fully automated trains that are centrally controlled using advanced communication and internet technologies, such as high-speed internet (5G) technology, Internet of Things, dedicated short range communications, digital video detection cameras, and artificial intelligence-based methods. The current study focuses on a detailed up-to-date review of the existing trends, technologies, advancements, and challenges in the deployment of ATs with a full automation level in rail transportation. The basic AT features along with the key technologies that are instrumental for the AT deployment and operations are discussed in detail. Furthermore, a comprehensive evaluation of the state-of-the-art research efforts is performed as well with a specific emphasis on the issues associated with the AT deployment, user perception and outlook for ATs, innovative concepts and models that could be used for the AT design, and the AT operations at highway-rail grade crossings. Based on the conducted review, this study determines the main advantages and challenges from the AT deployment. The identified challenges have to be collaboratively addressed by the relevant stakeholders, including railroad companies, researchers, and government representatives, to facilitate the AT development and deployment considering the perspectives of future users and without affecting the safety level.

The maritime transportation flows and container demand have been increasing over time, although the COVID-19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model’s objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided.

Rail transport makes a significant contribution to the economy of many geographical locations around the globe. Along with numerous merits, rail transport has some demerits as well, such as traffic delays and safety issues at level crossings. Traffic delays at level crossings have various negative impacts, including economic losses, increasing number of violations because of delays, queue spillbacks at the adjacent intersections, and environmental impacts. This study reviews a number of level crossing delay estimation approaches found in the literature. Based on the state-of-the-art review and practical considerations, a new approach for estimation of the overall traffic delay per day at level crossings with the existing warning devices is proposed to improve the sustainability of rail transport. Moreover, in order to assist with the selection of countermeasures for level crossing upgrades, the proposed approach can be also used to estimate the overall traffic delay per day at level crossings after implementation of countermeasures. A case study is conducted for the public and private level crossings in the State of Florida, considering the countermeasures commonly used in the United States. It was revealed that traffic delays were higher at the public level crossings than at the private level crossings, both before and after implementation of the candidate countermeasures. The case study also indicated that low-cost countermeasures could be more advantageous in terms of safety effectiveness and traffic delays as compared to many high-cost conventional countermeasures. Furthermore, certain attributes of the level crossings in Florida with the highest traffic delays were investigated.

Highway-rail grade crossing (HRGC) accidents pose a serious risk of safety to highway users, including pedestrians trying to cross HRGCs. A significant increase in the number of HRGC accidents globally calls for greater research efforts, which are not limited to the analysis of accidents at HRGCs but also understanding user perception, driver behavior, potential conflicting areas at crossings, effectiveness of countermeasures and user perception towards them. HRGC safety is one of the priority areas in the State of Florida, since the state HRGCs experienced a total of 429 injuries and 146 fatalities between 2010 and 2019 with a significant increase in HRGC accidents over the last years. The present study aims to conduct a comprehensive analysis of the HRGCs that experienced accidents in Florida over the last years. The databases maintained by the Federal Rail Administration (FRA) are used to gather the relevant information for a total of 578 crossings that experienced at least one accident from 2010 to 2019. In contrast with many of the previous efforts, this study investigates a wide range of various factors, including physical and operational characteristics of crossings, vehicle and train characteristics, spatial characteristics, temporal and environmental characteristics, driver actions and related characteristics, and other relevant information. The outcomes of this research will help better understanding the major causes behind accidents at the HRGCs in the State of Florida in a holistic way by considering a variety of relevant factors, which will assist the appropriate stakeholders with implementation of safety improvement projects across the state.

The "factory-in-a-box" concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The "factory-in-a-box" planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of sub-assembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the "factory-in-a-box" supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the "factory-in-a-box" supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of "factory-in-a-box", demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well.

A large-scale emergency evacuation due to an approaching natural disaster requires local and state administrations to make important decisions regarding evacuation routes, emergency shelters, and evacuation time periods, among other things. Considering a conflicting nature of certain emergency evacuation planning decisions, this study introduces a multiobjective optimization model for emergency evacuation planning that aims to minimize a set of critical performance indicators, including the total evacuation time, mental demand, physical demand, temporal demand, effort, and frustration endured by the individuals evacuating from a given metropolitan area anticipating a natural disaster. The major driver characteristics, evacuation route characteristics, driving conditions, and traffic characteristics that affect the driving performance of individuals, including vulnerable population groups, are incorporated in the proposed mathematical model. In order to solve the developed mathematical model and analyze the trade-offs among the conflicting objectives, this study presents four multiobjective heuristic algorithms. The computational experiments were conducted using real-world data and showcase the efficiency of the proposed methodology. The developed multiobjective methodology is expected to improve the safety of evacuees at the natural disaster preparedness stage and ensure timely evacuation from areas expecting significant natural disaster impacts.

Disruption occurrences in liner shipping operations affect schedule reliability and may increase the total cost of delivering cargoes at ports. If a vessel experiences disruption occurrences either at ports of call or in sea, the liner shipping company is required to decide on the schedule recovery action to execute in order to recover the resulting delays. This study formulates a novel mathematical model for the vessel schedule recovery problem (VSRP) in liner shipping. The objective aims to minimize the total profit loss, suffered by the liner shipping company due to disruption occurrences at a given liner shipping route. A total of four recovery strategies are considered in the model, which include: (1) vessel sailing speed adjustment; (2) vessel handling rate adjustment; (3) port skipping without container diversion; and (4) port skipping with container diversion. The proposed mathematical formulation for the nonlinear VSRP model is solved to the global optimality using BARON. A set of computational experiments are further performed for the Middle East/Pakistan/India-West Mediterranean (WM3) route, which is served by the OOCL liner shipping company, for various scenarios of disruption occurrences in sea and at ports. The results from the performed analyses demonstrate potential benefits for liner shipping companies from using the proposed methodology for various realistic scenarios of disruptions.

Purpose - Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach - A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following. evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings - The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed MIA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu starch and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications - Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications - The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value - A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

Effective liner shipping is important for the global seaborne trade. The volume of cargoes transported by liner shipping has been increasing over the past decades. Liner shipping companies face three levels of decision problems, including strategic, tactical, and operational problems. The tactical-level decisions are commonly made every three to 6 months. These decisions include: (1) port service frequency determination; (2) fleet deployment; (3) sailing speed optimization; and (4) vessel schedule design. Most of the concurrent liner shipping studies have addressed the tactical-level decision problems separately. Even though a few studies have proposed joint planning models that capture multiple decision problems at the same time, none of the conducted studies has integrated all the four tactical-level decision problems. To address this gap in the state-of-the-art, this study presents a holistic optimization model that addresses all the tactical-level liner shipping decision problems, aiming to maximize the total profit obtained from liner shipping services. The key route service cost components, found in the liner shipping literature, are considered in this study, which include: (1) vessel operational cost; (2) vessel chartering cost; (3) port handling cost; (4) port late arrival cost; (5) fuel consumption cost; (6) container inventory costs in sea and at ports of call; and (7) emission costs in sea and at ports of call. An exact optimization approach is adopted for the developed mathematical model. The computational experiments, conducted for a set of Asia-North America liner shipping routes, showcase the efficiency of the proposed approach and offer some important managerial insights.

Highway–rail grade crossings (HRGCs) are one of the most dangerous segments of the transportation network. Every year numerous accidents are recorded at HRGCs between highway users and trains, between highway users and traffic control devices, and solely between highway users. These accidents cause fatalities, severe injuries, property damage, and release of hazardous materials. Researchers and state Departments of Transportation (DOTs) have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability. The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements. This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs. Furthermore, this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae. The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well. Based on the review results, the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs. However, certain states still prefer customized models due to some practical considerations. Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.

Accidents at highway-rail grade crossings can cause fatalities and injuries, as well as significant property damages. In order to prevent accidents, certain upgrades need to be made at highway-rail grade crossings. However, due to limited monetary resources, only the most hazardous highway-rail grade crossings should receive a priority for upgrading. Hence, accident/hazard prediction models are required to identify the most hazardous highway-rail grade crossings for safety improvement projects. This study selects and evaluates the accident and hazard prediction models found in the highway-rail grade crossing safety literature to rank the highway-rail grade crossings in the State of Florida. Three approaches are undertaken to evaluate the candidate accident and hazard prediction models, including the chi-square statistic, grouping of crossings based on the actual accident data, and Spearman rank correlation coefficient. The analysis was conducted for the 589 highway-rail grade crossings located in the State of Florida using the data available through the highway-rail grade crossing inventory database maintained by the Federal Railroad Administration. As a result of the performed analysis, a new hazard prediction model, named as the Florida Priority Index Formula, is recommended to rank/prioritize the highway-rail grade crossings in the State of Florida. The Florida Priority Index Formula provides a more accurate ranking of highway-rail grade crossings as compared to the alternative methods. The Florida Priority Index Formula assesses the potential hazard of a given highway-rail grade crossing based on the average daily traffic volume, average daily train volume, train speed, existing traffic control devices, accident history, and crossing upgrade records.