Journal of Advanced Transportation

Volume 2018, Article ID 2029586, 14 pages

https://doi.org/10.1155/2018/2029586

## Capacity-Constrained Contraflow Adaption for Lane Reconfiguration in Evacuation Planning

^{1}School of Computer and Information, Hefei University of Technology, Hefei 230009, China^{2}Institute of Technology Innovation, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230088, China

Correspondence should be addressed to Wenbo Li; nc.ca.mii@ilbw

Received 22 January 2018; Revised 30 March 2018; Accepted 15 April 2018; Published 27 June 2018

Academic Editor: Ludovic Leclercq

Copyright © 2018 Wu Ni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a heuristic contraflow-based reconfiguration evacuation algorithm, which is named Capacity-Constrained Contraflow Adaption (CC-Adap). First, it effectively calculates optimal candidate routes for evacuation. Second, an evaluation method is proposed for estimating these candidate routes. Third, CC-Adap utilizes a contraflow-based method to reconfigure the evacuation routes to improve capacity constraints. Fourth, traffic conditions are updated in real time. Fifth, CC-Adap reuses historical evacuation routes to reduce the computational cost and accelerate the evacuation process. Experimental results show that CC-Adap generates high-performing evacuation strategies and can be used to tackle large-scale evacuation planning.

#### 1. Introduction

In recent years, both natural and man-made disasters have posed serious threats to humans, such as Hurricane Andrew [1], Hurricane Katrina [2], the “9/11” terrorist attack event [3], and 2011 Tohoku Earthquake Tsunami [4]. Effective evacuation plans are necessary for saving lives and minimizing casualties. The most important objective of an effective evacuation plan is to transfer evacuees to safe areas as quickly as possible. However, dynamic factors make this objective complicated to achieve. For example, the original intention in designing a transportation network is not to address the sharp increase of traffic flow when an emergency occurs [5]. The number of evacuees might far exceed the road capacity, thereby making evacuation route planning a computational challenge.

A well-known routing algorithm, namely, Capacity-Constrained Route Planner (CCRP) [1], was proposed for tackling evacuation route planning. CCRP models the capacity constraints of roads and intersections and selects the shortest paths for evacuation. Later, two heuristics, namely, Intelligent Load Reduction (ILR) and Incremental Data Structure (IDS), were introduced to improve the algorithm’s performance [6]. Although routing algorithms can effectively solve mid-size traffic evacuation problems [6], they might not be effective for large-scale evacuation scenarios with massive number of evacuees, which might lead to heavy traffic congestion, owing to capacity constraints. Contraflow-based methods are considered effective techniques for alleviating traffic congestion [2]. Contraflow-based methods reconfigure the edges in the ideal direction and reallocate edge capacity to reduce the evacuation time. For example, Kim et al. presented a heuristic approach that combines a Greedy method and a contraflow-based method for implementing transportation network reconfiguration and, to some extent, minimizing the total evacuation time [2]. However, finding an optimal contraflow plan for reconfiguring network is computationally challenging because we must enumerate all possible combinations of edges in the exhaustive search space [2]. Moreover, it takes a considerable amount of time to evaluate these contraflow candidates by calculating the evacuation time.

In this paper, we propose a Capacity-Constrained Contraflow Adaption (CC-Adap) algorithm for effective evacuation. CC-Adap consists of five steps. First, we utilize an evacuation algorithm to generate optimal candidate routes for evacuees, which finds the routes with maximal flow rate among all the available routes from source vertices to sink vertices. Second, we propose an evaluation method for heuristically evaluating the candidate routes’ capabilities based on the traffic conditions and determining the appropriate routes for evacuation. Then, we implement a contraflow-based method for optimizing the routes’ performances and reducing traffic congestion. Next, traffic conditions are updated in real time to make the route planning more practical. Finally, CC-Adap reuses the historical evacuation routes before calculating the available routes at each new time step. Experimental results indicate that CC-Adap boosts performance in evacuation route planning.

The remainder of this paper is organized as follows: Section 2 introduces some related works. Section 3 defines the transportation network and contraflow problem. Section 4 details the CC-Adap evacuation algorithm. Experimental results and analysis are presented in Section 5. Section 6 presents the conclusions of this paper.

#### 2. Related Work

To the best of our knowledge, the existing methods can be divided into descriptive and prescriptive approaches [5]. Descriptive approaches aim at mimicking real emergency evacuations based on the simulation of the traffic situations and drivers’ behaviours, while prescriptive methods mainly focus on providing a high-performing evacuation plan with minimum evacuation time.

##### 2.1. Descriptive Methods

The aim of descriptive methods is to simulate traffic evacuation situations as vividly as possible. Many traffic evacuation simulation tools have been proven to be capable of solving complex traffic evacuation problems, including MITSIMLab [7], VISSIM [8], MATSim [9], DynusT and DYNASMART [10], and AIMSUN [11]. In addition, some simulation methods utilize multimodel integration methods to simulate evacuees’ behaviours during evacuation and combine various existing evacuation methods for more realistic traffic simulation. For example, Pan et al. implemented a simulation framework for classifying evacuees’ behaviours into locomotion, steering, and social for evacuation analysis [12]. Wu and Huang combined a control volume model and a flow merging hypothesis to simulate evacuees’ behaviours in high-rise building evacuation situations [13]. Noh et al. considered different behaviours of heterogeneous evacuees in building evacuation [14]. They presented a partially dedicated evacuation strategy that divides evacuees into heterogeneous groups, assigns corresponding routes based on a flow model and a simulation optimization approach, and, to some extent, minimizes the average evacuation time [14]. Beloglazov et al. studied the behaviours of people in wildfire evacuation and integrated traffic simulator, wildfire simulator, and behaviour simulator to define the risk metric and provided detailed plan of how evacuation unfold [15]. Yuan et al. constructed a multilevel agent decision model for simulating driver’s behaviours and determining actions for each agent [16]. These descriptive methods can achieve satisfactory performance in traffic evacuation for local communities. However, most exhibit poor generalization performance owing to high computational complexity.

##### 2.2. Prescriptive Methods

Prescriptive methods typically provide suggested schedules for evacuation planners, aiming at reducing traffic congestion and minimizing total evacuation time. Zeng and Wang made a small modification to CCRP to improve the performance, which gives priority to longer evacuation routes for evacuating evacuees [17]. Kang et al. calculated the Dijkstra shortest evacuation paths and chose the best one for evacuees based on current traffic conditions [18]. Shahabi and Wilson proposed a Capacity-Aware Shortest Path Evacuation Routing (CASPER) framework for emergency traffic evacuation, which utilizes an advanced traffic model and an intelligent routing algorithm to calculate the optimal evacuation plan for evacuees [5]. Khan et al. used the Intelligent Transportation System (ITS) to compute the maximum traffic flow and the routes with least traffic congestion towards safe places for evacuees based on real-time traffic conditions [19]. Pourrahmani et al. considered the uncertain evacuee demand at the pick-up source points by utilizing fuzzy credibility theory to present a genetic algorithm (GA) for handling the stated evacuation problem [20]. Chen et al. implemented a distribution Load-balancing Emergency Guiding System (LEGS) to assign the fastest paths to transfer evacuees to the exits according to the capacity constraints and concurrent movement of evacuees [21]. Ikeda and Inoue proposed a Multi-Objective Genetic Algorithm (MOGA) to evaluate evacuation routes by considering route distance and evacuation time to select optimal evacuation routes [22]. Heydar et al. divided the transportation network into pedestrian network and vehicular network, where pedestrians were assigned the shortest routes to the safe areas in pedestrian network and vehicles transferred evacuees to designated shelters in vehicular network [23]. These prescriptive methods have demonstrated the feasibility of calculating evacuation strategies for traffic evacuation. However, the performances of these prescriptive methods are usually limited owing to the capacity constraints of road segments and intersections.

Previous works [24–27] have considered the benefits of contraflow-based methods on evacuation planning. In addition, in the literatures, the optimality of evacuation planning by integrating contraflow-based methods has been validated. For example, Xie et al. designed a bilevel framework for optimizing network evacuation performance, in which the lower level solves the traffic assignment problem and the upper level integrates a contraflow method with crossing elimination strategies to optimize the traffic assignment [28]. Wang et al. implemented a multiobjective optimization model to determine the evacuation priorities and setup time for doing the contraflow operations [3]. Even et al. implemented a conflict-based path-generation algorithm to simultaneously guide the evacuation and select the to-be-reversed roads [29]. Kim et al. presented a Bottleneck Relief (BR) approach that iteratively flips min-cut edges based on the theorem in [30] for solving massive traffic evacuation problems [2].

Above-mentioned contraflow-based methods are selected to generate contraflow strategy that can tackle capacity constraints effectively [2, 3, 24–29]. However, there are two difficulties in calculating the appropriate contraflow strategy for the complex traffic system. The first difficulty is that it is a time-consuming task to determine the appropriate to-be-reversed edges in the complex traffic network [2, 24, 25]. The second difficulty is that it is difficult to determine the ideal direction of these edges after being reversed [2, 3, 28, 29]. Thus, it is vital for the planners to find an appropriate contraflow strategy that could overcome these two difficulties.

So, this paper presents a prescriptive algorithm to generate appropriate contraflow strategy and optimal evacuation plan. For the first difficulty, the presented algorithm utilizes Greedy method and iterative optimization technique to select the to-be-reversed edges on the evacuation routes. For the second difficulty, the ideal direction of these to-be-reversed edges is determined by the traffic flow on the evacuation routes.

#### 3. Modeling and Problem Formulation

Various methods, such as simulation [13–15], classification network [23], and mathematical modeling [1, 2], are used to formulate the evacuation situations. In this paper, the mathematical graph is used to describe the traffic evacuation situations [1, 2, 5, 6, 17].

##### 3.1. Network Modeling

Suppose we are given a multisource and multisink transportation network , where and , respectively, represent the sets of vertices and edges. Here, is the source vertex set, is the transition vertex set, and is the sink vertex set. The definitions of the vertices and edges are given as follows:(i)Each vertex has a maximal vertex capacity and each source vertex has an extra initial occupancy , which represents the number of evacuees. For each sink vertex , the value of can be set according to the actual demand. In this paper, of each sink vertex is set to* infinity*.(ii)Each edge has a maximal edge capacity , a travel time , and an initial direction. is the number of evacuees (e.g., residents or vehicles) that can pass per unit period. The edge direction is initialized according to the real road conditions. is the time cost when evacuees pass edge . Various factors should be considered when formulating edge travel time, such as road length, traffic flow, and drivers’ behaviours. Many works have studied the relationship between these factors and edge travel time [13–15, 17]. This paper focuses on the impact of road length and traffic flow on the edge travel time. Considering the emergency situations, the traffic flow on the edges should be limited by the maximal edge capacity. Thus, the evacuees can use maximal speed passing one given edge when the traffic flow does not exceed the maximal edge capacity. In other words, more evacuees can be evacuated to safe area using this strategy in the minimum time. According to this, the edge travel time in the transportation network model is determined by the length and the maximal edge capacity of one given road.

There are different traffic elements in transportation network including buildings, crossroads, playgrounds, shelters, and parks. Buildings, crossroads, and playgrounds are the most common traffic elements in transportation network. Figure 1 illustrates the modeling results of the buildings, the crossroad, and the playground. Firstly, the buildings are modeled as source vertex. Secondly, the crossroad is modeled as transition vertex. Thirdly, the playground is modeled as sink vertex. The edges of the vertices are modeled according to the traffic configuration. As for the other traffic elements, they are modeled similar to these three common traffic elements shown in Figure 1.