The CaSSandra Project: computing safe and

The CaSSandra Project: computing safe and efficient routes with GIS Luca Urciuoli1 and Jonas Tornberg2 1

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Lund Industrial Engineering and Management, Ole Romers Vag 1 Box 118, 22100 Lund, Sweden [email protected] Chalmers University of Technology, City and Mobility, SE-41296 Gothenburg, Sweden [email protected]

1 Introduction Large amounts of dangerous goods are kept constantly on the move in Europe because of their significant impact on economic growth and to support quality of life. Industrial manufacturing consumes and creates great quantities of dangerous goods or waste materials (i.e. pharmaceutical industries) that need to be stored, handled and transported. At the same time, petrol and gasoline stations need to be constantly replenished from refineries to guarantee fuel to citizens or to public and private transportation services. According to available statistics [2], road transportation accounts for the movement of the major part of dangerous goods within Europe (58% in 2002). The access to a well built and distributed road infrastructure gives higher flexibility and door to door capabilities [9]. Consequently, transport purchasers perceive this transportation mode as highly effective and economically advantageous. However, the same factors stated above oblige material flows to travel through highlypopulated areas or highly-trafficked road segments. As a consequence the exposure of civilians to such risks as explosions, fire, dispersion and contamination increases drastically [2]. This is especially evident for shipments replenishing petrol and gas stations located within urban areas. History shows that accidents which take place during the transportation of hazardous material can have the same magnitude as those occurring in industrial plants [15]. Possible consequences may include fatality of human beings (road users, or people working and living in the nearby area) and even ecological disaster if the cargo is dispersed in wa-

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Luca Urciuoli and Jonas Tornberg

ter catchment areas [10],[5],[8]. The ramifications on private stakeholders will include delayed shipment, undelivered shipment, wasted cargo and higher transportation costs (in case of bridge collapse) [10],[1]. Dangerous goods or hazardous materials (hazmat) are any solid, liquid or gas substances that can have harmful effects for living organisms, property or environment. Laws and regulations for the transportation of dangerous goods in Europe are first collected by the United Nations Economic Commission for Europe, UNECE [13] and then extended to to all transportation means (road, rail, sea and air). The transportation of dangerous goods over European roads is regulated by the Agreement concerning the International Carriage of Dangerous Goods by Roads (ADR) that is enforced in Sweden by the Swedish Rescue Services Agency (SRSA). Yearly publications are used by the SRSA to disseminate recommended and forbidden road segments: [12]: • Primary Roads. Suitable main road network for throughway traffic of dangerous goods. It is recommended to follow these road links as long as it is possible. • Secondary roads. Secondary roads are recommended for local transportation from and to the primary network. These shouldn’t be used for throughway traffic. • Restricted Roads. Carriers moving dangerous goods are forbidden to travel through road tunnels and segments in proximity to water catchment areas. Restricted roads are defined in the ADR regulations, while primary and secondary roads are defined independently by each Swedish municipality to approximatively avoid densely populated areas. Since municipalities cannot perform quantitative risk assessments based on the OD of transportation assignments, there could be further route alternatives that may minimize population exposure to accident risks. In addition, drivers seeking to optimize shipments’ efficiency, may 1) minimize travelling time by taking into account only the forbidden segments or 2) minimize their travelling time by taking into account forbidden and recommended segments. The goal seeking attitude of human-beings could incite many operators to choose the first alternative at the cost of a higher risk exposure. Previous research shows that is possible to model transportation routes by applying heuristic techniques to minimize societal risks while taking into account efficiency factors. Such practices have been widely used by researchers for many years and usually take into account the combination of such parameters as costs, safety in terms of vehicle collisions, potential exposure of population, road users affected, and travel

The Cassandra Project

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delays [10],[5],[3]. This investigation is part of a research project cofinanced by Volvo Trucks, Volvo Logistics, Ericsson Microwave systems and the Swedish Governmental Agency for Innovation Systems (VINNOVA). The aim of this study is to compare 1) fastest routes computed on the entire transportation network exclusive of restricted roads, with 2) fastest routes on DGR restricted and recommended routes and finally 3) with minimum night and day risk routes computed on the whole transportation network excluding the DGR restricted segments. The main objective is to demonstrate how the Swedish recommendations function to minimize societal exposure to accident risks. Additionally, the analysis of the routes will also show how efficiency factors in terms of travel time are affected when avoiding dense populated areas. The analysis is performed on a case study based on Volvo Logistics’ real transport operations of material flows containing 2-propanol to be delivered to the Prot of Gothenburg, in the region of Vastra Gotaland, Sweden. The results include the development of a Decision Support System, based on Geographic Information Systems (GIS), capable of calculating safer (in form of mitigated societal risks) and efficient routes. In addition, this paper discusses the importance of developing a cooperative platform for emergency preparedness and resiliency management including dynamic routing (based on real-time information) and evacuation planning to optimally direct rescue service operations.

2 Method The method followed in this investigation incorporates two main phases: the development of the risk model and the collection of data. Risk Model Development. The term risk is a measure of relative safety and is an essential factor to be considered for the analysis of hazard transportation. Traditionally, it can be defined as a combination of three factors: a scenario, the likelihood of the scenario, and its consequences [4],[6]. Lepofsky et al. [8] introduce accident frequency, release rates, wind probabilities and decision makers’ preferences to the model. Huang et al [5] develop a GIS model based on the AHP (Analytical Hierarchy Process) technique to compute and minimize route travelling costs according to a set of five criteria: exposure, socioeconomic impact, risks of hijacks, traffic conditions, and emergency response. Frank et al. [3] develop a Spatial Decision Support System based on the temporally constrained shortest path algorithm which takes into consideration the following attributes: accident probability, distance, expected consequences, total population at risk and travel time. Parentela et

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al. [10] develop a risk model based on the aggregated probability of an accident event, release event and consequences in form of injuries, travel delay, road users’ fatality and population affected. According to the literature reviewed under this study, hazardous transportation risk is basically computed as a combination of accident likelihood, release probability, consequence and risk preference. Therefore these factors are considered in the risk model exploited in this study (equation 1). Risk = α · ρ · (γ)ϕ

(1)

Where α is the accident likelihood, ρ the release probability, γ the consequences and ϕ the risk preferences of the decision maker. Data Collection.The accident rate statistics have been extracted from the STRADA (Swedish Traffic Accident Data Acquisition) database which is built on information provided by the Swedish Law Enforcement Agency and the Public Health Services. Yearly severity and frequency of traffic accidents is defined, identified and localised by means of geographical coordinates. The release probability is the likelihood of material release from the adopted unit load (i.e. special packages or tank trailers). According to available literature, release rates depend on diverse factors: type of commodity, roadway and trailer type. In this study it is assumed the shipment taking place on urban multi-lane roads in tank trailers with a material release frequency of 0.067, as estimated by Pet-Armacots [11]. In this investigation, accident consequences are measured in terms of exposed population at night and day time within an area of 300 meters around every travelled segment (buffer zone). The Day and Night population density (respectively DPI and NPI) are based on parcel point coordinates provided by Statistics Sweden (SCB). Buffer zone techniques are then exploited to determine the population density per square meter in the proximity of the segments. The damage to society in the form of human fatality has been estimated by Vagverket to about 1.5 million [14]. A commercial road/transport network database including physical and operational characteristics of the transportation system (TeleAtlas) was used and loaded in the GIS environment. Relevant attributes provided by the database are segment length, travel time and one- or two-ways roads. When estimating risks, it is relevant to take into considerations the preferences of the decision makers. Risks are subjective and relative to the observer and therefore they depend on individuals’ perceptions and attitudes (risk-averse, -neutral or -seeking) [6]. For instance, it


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is obvious that the public has a more risk-averse behaviour towards catastrophic incidents. In this study the risk preferences are assumed to be neutral and therefore a factor of ϕ = 1 will be taken for the risk computations. The material shipped by Volvo Logistics contains 2-propanol and is part of the production process of the vehicle industry. 2-propanol is a highly toxic and flammable substance. For this reason the ADR has classified it as a class III flammable liquids dangerous good. These are substances that are liquids or liquids containing solids in solution or suspension with a flash point at a temperature below 60.5 C [13].

3 Results This section presents the results obtained by running a route planning algorithm minimizing travel time and risks on different network configurations. Table 1 shows how travel time (TT), travel distance (TD), accident frequency (AF), night and day population exposure (NPI and DPI) and risks (RISK), according to equation 1, are affected. The same paths shown in the table are depicted in figure 1. The first path Table 1. Routing analysis impact measures Path Parameter to minimize TT (min) TD (km) NPI DPI AF 1 2 4 5

Travel Time SRSA Travel Time Night Risk Day Risk

26 33 59 68

39 47 62 62

0,20 0,08 0,02 0,05

1,02 0,17 0,05 0,03

RISK

0,256 32236,2 0,317 8307,1 0,04 378,2 0,04 403

is computed by minimizing travel time on the whole transportation network excluding the ADR restricted roads. In the table it is possible to notice the elevated risk level of this route. Thus, drivers considering only the forbidden segments place the general population in considerable danger. The second path is instead obtained by minizing the travel time on the forbidden and recommended roads by the SRSA. This path shows a relatively slight increase of the travel time (+27%) and length (21%) and a noticeable decrement of the risks (-74%), highlighting an optimal trade-off between efficiency and risk factors. The third and fourth routes are finally computed by minimizing night and day risks on the transportation network deprived of the ADR restricted roads. Comparing them with the second path, travel times (+78% path

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3 and +106% path 4) and distances (+32.7% path 3 and 32.6% path 4) increase considerably. However, the exposure to traffic accidents so as of night and day population decreases significantly, determining a fundamental reduction of the risks (-95.5% path 3 and -95.2% path 4).

Fig. 1. Routes computations results

4 Discussion The results of this investigation show that the SRSA recommended and forbidden routes provide an optimal balance of efficiency and risk factors. However if transport operators behave by considering only forbidden routes and minimizing their travel time, societal exposure increases significantly. Finally the optimization on day and night population exposure shows the possibility to decrease risks at the cost of efficiency factors (time and length). This study highlights the capability of geographic information techniques to easily handle complicated dangerous goods transportation problems by combining information about the transportation network, class of chemicals transported, population distribution, and traffic statistics into an integrated environment. Additionally, the importance of real-time routing and monitoring of dangerous goods is highlighted as a means to decrease societal risk exposure. Thus, future research has to be oriented towards the integration of the developed methodology into an extended decision support system in which vehicles can communicate and exchange information with a central server, where different actors can interact and make decisions about dangerous goods transportation routing, scheduling, monitoring and even emergency preparedness.


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