Evangelos I. Kaisar

Robust berth scheduling at marine container terminals via hierarchical optimization

In this paper, we present a mathematical model and a solution approach for the discrete berth scheduling problem, where vessel arrival and handling times are not known with certainty. The proposed model provides a robust berth schedule by minimizing the average and the range of the total service times required for serving all vessels at a marine container terminal. Particularly, a bi-objective optimization problem is formulated such that each of the two objective functions contains another optimization problem in its definition. A heuristic algorithm is proposed to solve the resulting robust berth scheduling problem. Simulation is utilized to evaluate the proposed berth scheduling policy as well as to compare it to three vessel service policies usually adopted in practice for scheduling under uncertainty.

Critical Intersection Signal Optimization During Urban Evacuation Utilizing Dynamic Programming

Despite advancements in the field of traffic planning and operations, many major cities still rely on pretimed signal settings. With only pretimed signal control strategies, the secondary effects of a terror attack are magnified by slow evacuation times resulting in further loss of life. However, the cost of implementing new infrastructure based solely on the chance of a no-notice evacuation is not something that city planners are willing to do. The purpose of the research is to define a cost-effective methodology to develop pretimed signal control strategies to assist evacuations in urban areas. To that end, a dynamic programming methodology was developed to assist critical intersections in urban corridors. To test this methodology, a microscopic traffic-simulation environment was created for a case study of a 10 intersection evacuation corridor in Washington, DC. Using the proposed methodology to optimizing signal splits of a critical intersection within an evacuation corridor, evacuation clearance time was reduced by approximately 1 h. Furthermore, this formulation can be used to develop pretimed signal control settings for evacuation scenarios. Results showed that peak-hour signal timings are not sufficient in the case of an emergency, and signal timing plans must be tailored for emergency evacuation.

Application of Transit Signal Priority for No-Notice Urban Evacuation

During an urban evacuation, is it advisable for regional planners to allow transit buses signal priority in cases where police-assisted traffic controls are not an option? With only a finite number of available units, buses will be required to make multiple trips in and out of evacuation zones. Therefore, it is within reason that some regional municipalities would want to allow transit priority to hasten trips made by buses. However, studies in the past have shown that, during times of high roadway demand, transit priority causes major delays for vehicular traffic. The goal of this paper is to examine and quantify the benefit of transit signal priority will have on transit vehicles and any hindrance this priority has on nontransit evacuees. By applying state-of-the-art tools in evacuation modeling and microscopic traffic simulation, the aspects of a multimodel evacuation and transit signal priority impact study are merged within a single study of Washington, DC. On the basis of simulation results, transit signal priority has the potential to save lives by reducing bus travel time by 26%. This signifies that three prioritized buses can handle the workload of four nonprioritized buses. Furthermore, this priority has no adverse effect on the evacuation clearance time of nontransit evacuees. On the basis of this study, transit priority in a multimodal evacuation should be used.

Testing Accuracy and Reliability of MAC Readers to Measure Arterial Travel Times

Media Access Control (MAC) Readers are a relatively accurate and inexpensive way to collect travel time data. This study utilizes a research-driven MAC Reader developed at Florida Atlantic University. Thus, it investigates the impact of factors that may affect the readings such as location of Bluetooth devices and MAC Readers, approaching speed of vehicles with Bluetooth devices, and antennae type. The results indicate that MAC Readers resemble travel times collected by other methods, Bluetooth devices placed in the vehicle’s dashboard are detected more easily, vehicles traveling at lower speeds are detected more reliably, and Omni-directional antenna are detected more successfully.

Guidance for identifying corridor conditions that warrant deploying transit signal priority and queue jump

Nowadays, our streets and highways are growing in traffic congestion as large number of cars are entering the transportation system due to rise in population. The high volume of vehicles and numerous signalized intersection the traffic congestion is causing huge problems to schedule reliability. As transportation demand is increasing various road networks are facing increasing congestion. To mitigate the high-density congestion transit signal priority (TSP) and queue jumper are significant solution. Transit signal priority is providing solutions according to many variables, and it is pursuing several valuable objectives such as: reduced transit travel times, better schedule adherence, better transit efficiency, and increased road network efficiency by car mobility. Our objective is to compare and evaluate existing guidelines or form new guidelines for TSP and Queue jump. In addition, in this research, we study various findings from a series of gap filling research efforts of various guidelines strategies which are not in common use in the United States; a simulation platform will be used to study transit signal priority and their guidelines. The preliminary results show improvement in travel times in each directional of travel and various scenario that implemented some of the transit preferential treatments, when compared to base scenario.

Implementing and Interpreting a Data Envelopment Analysis Model to Assess Safety and Security of Intermodal Facilities

Following September 11, 2001, numerous security policies were created which have caused a number of unique challenges in planning for the transportation networks. In particular, there is a need to enhance security by improving collaboration between various transportation modes. Improved intermodal connectivity has, therefore, been identified as one of the main challenges to achieve a safer, more secure, and productive transportation network. In this study, a mathematical model using Data Envelopment Analysis (DEA) is developed and investigated to assess the safety and security of intermodal transportation facilities. The DEA model can assess the efficiency level of safety and security of intermodal facilities and identify potential solutions for improvement. The DEA methodology presented is general in its framework and can be applied to any network of intermodal transportation systems. Availability of credible data, complimented with DEA methodology, will help in management decisions making safety and security decisions for intermodal transportation facilities.

A Hybrid Meso-Microscopic Model for Catastrophic Event Management

Disaster planning has become one of the foremost in transportation management. This paper presents a case study of the urban area and its capability to conduct an efficient response to a catastrophic event. To ensure proper response, there are a number of critical issues that must be addressed prior to the bioterrorist attack using smallpox. The identity of the disease in this case study is important due to the initial dormant period regarding signs and symptoms after contact. The dormant period makes monitoring normal daily traffic operations critical in creating a proper response. Regarding the network analysis, simulation will be discussed on two levels. The first level is a mesoscopic simulation, in which flows and trip distributions will be monitored to deliver an appropriate response plan. The second level is developed through microsimulation. This allows analysis and optimization of movements throughout the network to receive treatment, vaccinations and/or quarantine.