A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas

Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operations. The nonlinear model is linearized and solved using CPLEX. A number of computational experiments are conducted for the liner shipping route, passing through ECAs. Important managerial insights reveal that the proposed methodology can assist liner shipping companies with efficient vessel schedule recovery, minimize the monetary losses due to disruptions in vessel schedules, and improve energy efficiency as well as environmental sustainability

Identifying Factors Affecting the Time Taken for Drivers to Complete Freeway Merging Maneuvers under Varying Weather, Traffic, and Geometric Conditions

Inadequate understanding of driver heterogeneity and characteristics creates challenges building models in microscopic traffic simulation tools that accurately represent merging behavior in real traffic. To further understand merging behavior, this study, unlike previous studies, considers age group and other driver characteristics under varying weather, traffic, and geometric conditions. A pilot study was conducted using a driving simulator to simulate merging scenarios on four-lane and six-lane freeway segments for Level of Service (LOS) A and B under clear and foggy weather conditions. A total of 100 individuals voluntarily participated in the study and their time taken to complete merging maneuvers (or merging time) was used as the performance measure. The collected data were analyzed using ANOVA and log-linear regression models, and results show that although there were statistically significant differences among age groups, number of lanes was the most significant predictor variable in the model because drivers required longer time merging to the four-lane freeway segment than to the six-lane freeway segment. Also, some driver characteristics and self-reported driving abilities were found to influence the merging time of drivers

Assessing perceived driving difficulties under emergency evacuation for vulnerable population groups

The devastating impacts of natural hazards, including loss of lives and properties, underline the importance of efficient hazard preparedness, especially in the areas with frequent hazard occurrence. Several studies indicated that driving during emergency evacuation is quite challenging due to dense traffic flow, inclement weather conditions, and unexpected maneuvers of other evacuees. However, limited research has been directed towards assessing the perceived driving difficulties of individuals, including vulnerable population, under emergency evacuation. This study deploys a driving simulator in order to emulate realistic emergency evacuation scenarios and to quantify the perceived driving difficulties of individuals under emergency evacuation. Based on the data, collected using a driving simulator, a number of statistical models are proposed to determine a set of performance indicators, including the mental demand, physical demand, temporal demand, performance, effort, and frustration, experienced by individuals as a result of emergency evacuation. The statistical models also capture a variety of different driver characteristics, traffic characteristics, driving conditions, and evacuation route characteristics. The analysis results suggest that the considered performance indicators are significantly influenced with a number of factors, including age, gender, education, race, presence of chronic diseases, and self-reported driving ability. The insights from the conducted research can be applied at the hazard preparedness stage to mitigate the perceived driving difficulties of individuals under emergency evacuation and ensure their safety

Development of statistical models for improving efficiency of emergency evacuation in areas with vulnerable population

Different parts of the world are characterized by frequent occurrences of natural hazards. As such, evacuation planning is an essential part of the natural hazard preparedness, especially in hazard-prone areas. Numerous research efforts have been directed towards improving the efficiency of the evacuation process. However, only a limited number of studies have specifically aimed to identify factors, influencing the driving ability of individuals under emergency evacuation and the occurrence of crashes along the evacuation routes. Furthermore, previous research efforts have focused on a relatively narrow range of factors (primarily driver and traffic flow characteristics). This study aims to fill the existing gap in the state-of-the-art by investigating the effects of a wide range of different factors (including driver characteristics, evacuation route characteristics, driving conditions, and traffic characteristics) on the major driving performance indicators under emergency evacuation. The considered driving performance indicators include travel time, lane deviation, crash occurrence, collision speed, average acceleration pedal pressure, and average braking pedal pressure. A set of statistical models is developed to identify the most significant factors that influence the major driving performance indicators. These models are tested using the data collected from the driving simulator and participants with various socio-demographic characteristics. The results indicate that age, gender, visual disorders, number of lanes, and space headway may substantially impact the driving ability of individuals throughout the emergency evacuation process. Findings from this research can be incorporated within the existing transportation planning models to facilitate the natural hazard preparedness, ensure safety of evacuees, including vulnerable populations, and reduce or even prevent the occurrence of crashes along the evacuation routes

A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida

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

Exact and heuristic solution algorithms for efficient emergency evacuation in areas with vulnerable populations

Proper emergency evacuation planning is a key to ensure safety and efficiency of transportation networks in the event of approaching natural hazards. A sound evacuation plan can save human lives and avoid congestion. In order to develop effective emergency evacuation plans, this study presents a mixed-integer programming model that assigns individuals, including vulnerable population groups, to emergency shelters through evacuation routes during the available time periods. The objective of the mathematical model is to minimize the total travel time of individuals leaving an evacuation zone. Unlike many emergency evacuation models presented in the literature, the proposed mathematical model directly accounts for the effects of socio-demographic characteristics of evacuees, evacuation route characteristics, driving conditions, and traffic characteristics on the travel time of evacuees. An exact optimization approach and a set of heuristic approaches are applied to yield solutions for the developed model. The numerical experiments are conducted for emergency evacuation of Broward County (Florida, United States). The results show that the exact optimization approach cannot tackle the large-size problem instances. On the other hand, the proposed heuristic algorithms are able to provide good-quality solutions within a reasonable computational time. Therefore, the developed mathematical model and heuristic algorithms can further assist the appropriate agencies with efficient and timely emergency evacuation planning

Multiobjective Optimization Model for Emergency Evacuation Planning in Geographical Locations with Vulnerable Population Groups

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