A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented

Stochastic dynamic traffic assignment model under emergent incidents

Urban emergent incidents affect transportation operation and result in the rapid spread of traffic congestion in network, so it’s necessary to analyze the dynamic changes of traffic flow distribution under emergent incidents. Therefore, model and algorithm for the dynamic traffic assignment problem under emergent incidents have been highly concerned by government and scholars. This paper proposes a stochastic dynamic traffic assignment (SDTA) model based user optimum considering the loss of node capacity and change of network structure under traffic and environment emergencies. The Nested Logit model is used to describe the departure time and path choice. Then, the variational inequality formulation is proposed and discrete dynamic network loading algorithm is designed and validated by a numerical example. The results show that the model and algorithm can be used to express the development trend of actual dynamic network under emergency

DEVELOPMENT OF A MIXED-FLOW OPTIMIZATION SYSTEM FOR EMERGENCY EVACUATION IN URBAN NETWORKS

In most metropolitan areas, an emergency evacuation may demand a potentially large number of evacuees to use transit systems or to walk over some distance to access their passenger cars. In the process of approaching designated pick-up points for evacuation, the massive number of pedestrians often incurs tremendous burden to vehicles in the roadway network. Hence, one critical issue in a multi-modal evacuation planning is the effective coordination of the vehicle and pedestrian flows by considering their complex interactions. The purpose of this research is to develop an integrated system that is capable of generating the optimal evacuation plan and reflecting the real-world network traffic conditions caused by the conflicts of these two types of flows. The first part of this research is an integer programming model designed to optimize the control plans for massive mixed pedestrian-vehicle flows within the evacuation zone. The proposed model, integrating the pedestrian and vehicle networks, can effectively account for their potential conflicts during the evacuation. The model can generate the optimal routing strategies to guide evacuees moving toward either their pick-up locations or parking areas and can also produce a responsive plan to accommodate the massive pedestrian movements. The second part of this research is a mixed-flow simulation tool that can capture the conflicts between pedestrians, between vehicles, and between pedestrians and vehicles in an evacuation network. The core logic of this simulation model is the Mixed-Cellular Automata (MCA) concept, which, with some embedded components, offers a realistic mechanism to reflect the competing and conflicting interactions between vehicle and pedestrian flows. This study is expected to yield the following contributions * Design of an effective framework for planning a multi-modal evacuation within metropolitan areas; * Development of an integrated mixed-flow optimization model that can overcome various modeling and computing difficulties in capturing the mixed-flow dynamics in urban network evacuation; * Construction and calibration of a new mixed-flow simulation model, based on the Cellular Automaton concept, to reflect various conflicting patterns between vehicle and pedestrian flows in an evacuation network

Leveraging e-transportation in real-time traffic evacuation management

As part of intelligent transportation systems, Internet-connected sensors and cameras have become ubiquitous along roads and highways, enabling many novel e-transportation applications. In this research, we leverage this emerging technology to improve the surface transportation aspect of homeland security, by enhancing its support for evacuation in case of terrorist attacks or other unpredictable disasters. In particular, we extend our existing work on developing a Smart Traffic Evacuation Management System (STEMS), by proposing more efficient evacuation algorithms that dynamically generate evacuation plans for both single and multiple incidents scenarios, based on real-time traffic information obtained from sensor data available through the Web. (c) 2006 Elsevier B.V. All rights reserved

Leveraging E-Transportation In Real-Time Traffic Evacuation Management

As part of intelligent transportation systems, Internet-connected sensors and cameras have become ubiquitous along roads and highways, enabling many novel e-transportation applications. In this research, we leverage this emerging technology to improve the surface transportation aspect of homeland security, by enhancing its support for evacuation in case of terrorist attacks or other unpredictable disasters. In particular, we extend our existing work on developing a Smart Traffic Evacuation Management System (STEMS), by proposing more efficient evacuation algorithms that dynamically generate evacuation plans for both single and multiple incidents scenarios, based on real-time traffic information obtained from sensor data available through the Web. © 2006 Elsevier B.V. All rights reserved