Integrated Special Event Traffic Management Strategies in Urban Transportation Network

How to effectively optimize and control spreading traffic in urban network during the special event has emerged as one of the critical issues faced by many transportation professionals in the past several decades due to the surging demand and the often limited network capacity. The contribution of this dissertation is to develop a set of integrated mathematical programming models for unconventional traffic management of special events in urban transportation network. Traffic management strategies such as lane reorganization and reversal, turning restriction, lane-based signal timing, ramp closure, and uninterrupted flow intersection will be coordinated and concurrently optimized for best overall system performance. Considering the complexity of the proposed formulations and the concerns of computing efficiency, this study has also developed efficient solution heuristics that can yield sufficiently reliable solutions for real-world application. Case studies and extensive numerical analyses results validate the effectiveness and applicability of the proposed models

An Integrated Optimal Control System for Emergency Evacuation

How to effectively control evacuation traffic has emerged as one of the critical research issues in transportation community, due to the unusually high demand surge and the often limited network capacity. This dissertation has developed an integrated traffic control system for evacuation operations that may require concurrent implementation of different control options, including traffic routing, contraflow operation, staged evacuation, and intersection signal control. The system applies a hierarchical control framework to achieve a trade-off between modeling accuracy and operational efficiency for large-scale network applications. The network-level optimization formulations function to assign traffic to different evacuation corridors, select lane reversal configurations for contraflow operations, and identify the evacuation sequence of different demand zones for staged evacuation. With special constraints to approximate flow interactions at intersections, the formulations have introduced two network enhancement approaches with the aim to capture the real-world operational complexities associated with contraflow operations and staged evacuation. The corridor-level optimization formulations, taking the network-level decisions as input, function to identify the critical control points and generate the optimal signal timings along the major evacuation corridors. The formulations feature the critical intersection concept to reduce the interference of side-street traffic on arterial evacuation flows. This study has also developed an efficient solution method using the Genetic Algorithm based heuristics along with an embedded macroscopic simulator. This dissertation has also proposed a revised cell transmission model that aims to capture the complex temporal and spatial interactions of evacuation traffic flows for both levels of optimization formulations. This model can significantly reduce the size of the optimization problem, and yet preserve the ability in effectively modeling network traffic dynamics. Numerical studies were conducted for each individual control component as well as for the entire integrated control system. The results reveal that the staged evacuation and contraflow strategies generated from the proposed formulations can substantially improve the evacuation efficiency and effectively reduce network congestions. Signal control strategies with the critical intersection concept also outperform the state-of-the-practice evacuation signal plans

Analytical model for staging emergency evacuations

Disaster response in areas of high population density is centered on the efficient evacuation of people and possibly goods. Developing evacuation plans suitable for different levels of urgency based on the intensity of threat is a challenging task. In case of densely populated cities (e.g., New York, Los Angeles), the level of threat is enhanced by the congestion of their transportation systems, and the decision to evacuate a region simultaneously or by dividing it into multiple stages (or zones) affects the required evacuation time and associated delays. The evolution of the traffic conditions on the evacuation route can vary significantly based on the type of evacuation strategy employed (i.e., simultaneous or staged). In this dissertation, mathematical models are developed for estimating evacuation time and delay. Evacuation time is the time for evacuating all vehicles from a designated region, while delay includes queuing and moving delays incurred by evacuees. The base model handles a uniform demand distribution over the evacuation route and deterministic evacuees\u27 behavior. The relationship between delay and evacuation time is investigated, and the impact of a staged versus a simultaneous evacuation is analyzed. A numerical method is adopted to determine the optimal number of staging zones. A sensitivity analysis is conducted of parameters (e.g., demand density, access flow rate, and evacuation route length) affecting evacuation time and delay. To account for the heterogeneous demand distribution over the evacuation region and evacuees\u27 behavioral responses to an evacuation order (e.g., fast, medium, and slow), a more realistic model is developed by enhancing the base model. Based on a numerical searching process, the enhanced model determines the optimal time windows and lengths of individual staged zones dependent on the demand distribution, behavioral response, and evolution of traffic conditions on the evacuation route. The applicability of the model is demonstrated with a numerical example. Results indicate that evacuation time and delay can be significantly reduced if a staged evacuation can be appropriately implemented. Finally, the impact of compliance is investigated. Compliance is defined as the conformity of a staged zone to its demand loading pattern. It is found that the level of compliance and deviation from scheduled access time influence the effectiveness of staging. Further, a method to revise the optimal staging scheme to accommodate the noncompliant demand is illustrated. The models developed in this research can serve as useful tools to provide suitable guidelines for emergency management authorities in making critical decisions during the evacuation process

Discrete Time Dynamic Traffic Assignment Models with Lane Reversals for Evacuation Planning

In an event of a natural or man-made disaster, an evacuation is likely to be called for to move residents away from potentially hazardous areas. Road congestion and traffic stalling is a common occurrence as residents evacuate towns and cities for safe refuges. Lane reversal, or contra-flow, is a remedy to increase outbound flow capacities from disaster areas which in turn will reduce evacuation time of evacuees during emergency situations. This thesis presents a discrete-time traffic assignment system with lane reversals which incorporates multiple sources and multiple destinations to predict optimal traffic flow at various times throughout the entire planning horizon. With the realization of lane reversals, naturally the threat of potential head-on collisions emerges. To avoid the occurrence of such situations, a collision prevention constraint is introduced to limit directional flow on lanes based on departure time.;This model belongs to the class of dynamic traffic assignment (DTA) problems. Initially the model was formulated as a discrete-time system optimum dynamic traffic assignment (DTA-SO) problem, which is a mixed integer nonlinear programming problem. Through various proven theorems, a linearized upper bound was derived that is able to approximate the original problem with very high precision. The result is an upper bound mixed integer linear programming problem (DTA-UB). The discrete-time DTA model is suitable for evacuation planning because the model is able to take care of dynamic demands, and temporal ow assignment. Also, simultaneous route and departure is assumed and an appropriate travel time function is used to approximate the minimum and maximum travel time on an arc.;This thesis discusses the different attributes that relates to Dynamic Traffic Assignment. DTA model properties and formulation methodology are also expounded upon. A model analysis that breaks down each output into individual entities is provided to further understand the computational results of small networks. A no reversal DTA-UB model (NRDTA-UB) is formulated and its computational results are compared to DTA-UB. Through the extensive computational results, DTA-UB is proven to obtain much better results than NRDTA-UB despite having longer solving time. This is a step toward realizing the supremacy of having lane reversals in a real-life evacuation scenario

Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical Programming: Modeling and Optimization

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation time, and departure time. Thus, the contribution of this dissertation is in the two areas of fairness and evacuation. The first part of the contribution of this dissertation is in the field of fairness. The objective in MMF is to allocate resources fairly among multiple demands given limited resources while utilizing the resources for higher efficiency. Fairness and efficiency are contradicting objectives, so they are translated into a bi-objective mathematical programming model and solved using the ϵ-constraint method, introduced by Vira and Haimes (1983). Although the solution is an approximation to the MMF, the model produces quality solutions, when ϵ is properly selected, in less computational time compared to the progressive-filling algorithm (PFA). In addition, a new algorithm is developed in this research called the θ progressive-filling algorithm that finds the MMF in resource allocation for general problems and works on problems with the nonconvex structure problems. The second part of the contribution is in evacuation modeling. The common dynamic evacuation models lack a piece of essential information for achieving fairness, which is the time each evacuee or group of evacuees spend in the network. Most evacuation models compute the total time for all evacuees to move from the endangered zone to the safe destination. Lack of information about the users of the transportation network is the motivation to develop a new optimization model that reports more information about the users of the network. The model finds the travel time, evacuation time, departure time, and the route selected for each group of evacuees. Given that the travel time function is a non-linear convex function of the traffic volume, the function is linearized through a piecewise linear approximation. The developed model is a mixed-integer linear programming (MILP) model with high complexity. Hence, the model is not capable of solving large scale problems. The complexity of the model was reduced by introducing a linear programming (LP) version of the full model. The complexity is significantly reduced while maintaining the exact output. In addition, the new θ-progressive-filling algorithm was implemented on the evacuation model to find a fair and efficient evacuation plan. The algorithm is also used to identify the optimal routes in the transportation network. Moreover, the robustness of the evacuation model was tested against demand uncertainty to observe the model behavior when the demand is uncertain. Finally, the robustness of the model is tested when the traffic flow is uncontrolled. In this case, the model's only decision is to distribute the evacuees on routes and has no control over the departure time

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event