یک چارچوب ارزیابی خطر برای حمل و نقل مواد خطرناک در بزرگراه ها توسط شبکه های پتری رنگی A Risk Assessment Framework for Hazmat Transportation in Highways by Colored Petri Nets

I. INTRODUCTION THE management of hazardous material (hazmat) transportation on road has attracted a growing attention by researchers in recent years. Indeed, hazmat transportation implies potentially high risks depending upon the nature of the hazmat carried and the physiochemical events associated with these materials, the localization and density of the affected subjects, the characteristics and state of roads, the density of the traffic, and the environmental conditions. Mitigation of transport risk requires the implementation of a variety of policy tools such as specializing in hazmat incidents emergency response teams [29]. Moreover, a number of transport safety measures aim at reducing the possible undesirable consequences of incidents involving accidental release of dangerous goods during transportation [4], [26], [30]. In this context, different decision makers and actors are involved in decision problems that can be seen under planning or control viewpoints [21]. Planning problems refer to long-term decisions (route design, resource allocation), in which there is no need of information in real time. On the other hand, control problems are related to the short term and/or real time decisions (routing of vehicles, emergency operations, restoration procedures after an accident) that need real-time information and dynamic models. Research in the area of planning problems focuses on two main issues [29]: 1) assessing the risk induced on the population by hazmat transportation traveling on the road network [10], [11] and 2) identifying the route that minimizes transport risk [4], [7]. A popular measure is the number of people living within a threshold distance from the routes used by hazmat trucks. A model suggested by Batta and Chiu [3] emphasizes exposure to hazmats rather than the occurrence of an accident. Alternatively, incident probability is suggested as a risk measure by several authors [1], [12], [25]. For the second research issue, Kara and Verter [15] propose a bi-level programming model for the problem of designing a road network for hazmat transportation. Moreover, Erkut and Gzara [10] consider a bi-objective (cost and risk-minimization) version of the network design problem. Verter and Kara [29] provide a path-based formulation for the hazmat transport network design problem. Varying the routing options included in the model for each shipment can generate alternative solutions to the network design problem. In addition, Verter and Gendreau [28] focus on the tactical planning problem of a railroad company that regularly transports a predetermined amount of mixed freight (i.e., nonhazardous and hazardous cargo) across a railroad network. Few contributions are relevant to the operational management problem for hazmat transportation. In particular, Minciardi and Robba [21] proposed a generic decision architecture for the management of vehicle fleets that transport hazmat. The solution takes into account the presence of different decision makers with the objective of the reduction of economic costs and the containment of risk for vehicles and infrastructures. Moreover, Centrone et al. [8] present a dynamic freeway model in a colored Petri net (CPN) framework that allows analyzing in real-time the risk of hazmat transportation in different routes. This paper presents a decision support system (DSS) to be used by the highway planners for monitoring and controlling vehicles transporting hazmat and solving two operational problems: 1) assessing the risk induced on the population by hazmat vehicles traveling on highways and 2) selecting the optimal restoring procedures and routes of the heavy vehicles (HVs) after an accident. A decision-making process to choose the best option from a feasible set is present in just about every conceivable human task [23]. As there are various definitions for what a DSS is, there are various classifications for the different types of DSSs. According to Power [24], DSSs can be divided into five main categories. 1) Communication-Based: Emphasis is given to the communication and collaboration of a group of decision makers. 2) Data-Based: The DSS makes extensive use of databases, processes large amounts of data, uses queries and advanced methods, such as on line analytical processing. 3) Document-Based: The DSS is mainly used in knowledge management and retrieval systems. 4) Knowledge-Based: The DSS makes use of artificial intelligence and rules for automated decision making, and are also called expert systems. 5) Model-Based: The DSS gives major emphasis on mathematical models, simulations and optimization techniques that are used to optimize performances and objectives. The proposed DSS is designed in a model-based framework and consists of three components: the data component (DC), the model component (MC) and the user Interface Component (UIC). The DC stores all information needed for the DSS to operate; the UIC allows the effective interaction of the user with the real system. Moreover, the MC contains all the models, algorithms, rules, and knowledge that are needed to provide decision support to users. In the presented DSS, the MC consists of three modules: the risk assessment module (RAM), the simulation module (SM) and the decision module (DM). In particular, we specify the two main modules of the MC: the RAM and the SM. The RAM assesses in real time the social risk of transporting hazmat on the highways, by using the data coming from information and communication technology (ICT) tools. The social risk is defined as the risk of people that can be exposed to the consequences of an accident with hazmat release. Moreover, the SM is devoted to analyze the risk of hazmat transportation and to forecast the highway stretches where the risk can be high. Indeed, the simulation studies have proved to be useful domains for making decisions and assessing DSS implementations [5], [22]. In the presented DSS, the SM is based on a modular colored timed Petri net (CTPN) model of the highway network and includes the model of the accident and the restoration procedure after the accident. Hence, the DSS can be employed to suggest to hazmat transporters the safest route and to choose the best accident restoration policy with respect to the number of involved people and the evaluated social risk. In order to show the effectiveness and the applicability of the DSS, we show a prototype implementation devoted to evaluate the risk related to the transit of hazmat in a stretch of the A4 highway in the North-east of Italy. Assuming that an accident occurs in a particular link of the highway, we evaluate and compare the risk in the links by applying different strategies to restore the normal traffic flow. The remainder of this paper is organized as follows. Section II describes the DSS architecture. Moreover, Section III describes the proposed risk assessment and DMs. Section IV presents the SM described in a CTPN framework and Section V shows a DSS prototype applied to a case study. Finally, Section VI reports the conclusion.