Burçin Bozkaya, PhD. – Sabancı University Ronay Ak , PhD. Candidate – Istanbul Technical University

1. Burçin Bozkaya, PhD. – Sabancı University Ronay Ak, PhD. Candidate – Istanbul TechnicalUniversity Assessing Hazmat Transportation Risk for Istanbul Metropolitan Municipality

2. OUTLINE • Introduction • Existing Risk Models • Proposed Model • GIS Application • Conclusion

3. INTRODUCTION According to United States Department of Transportation (USDOT), hazardous material is defined as“a substance or material capable of posing an unreasonable risk to health, safety, or property when transported in commerce”.

4. INTRODUCTION • Goals of the study are: • Quantifying Risk in an Urban Area • Visualizing Risk • Offering alternatives to the decision maker within the context of a Decision Support System

5. INTRODUCTION • A quantitative analysis of the risks of transporting hazardous materials (hazmat) through the roads of Istanbul by using real population data. • A first study that attempts to measure hazmat risk on highways of metropolitan areas of this population density.

6. EXISTING RISK MODELS • Population Exposure Risk (ReVelle et al. 1991, Batta and Chiu 1988): r : impact radius of the danger circle alongroad segment l according to hazmat type m dl:population density around road segment l

7. EXISTING RISK MODELS • DoT Risk • (U.S. Department of Transportation, 1989): TARl: truck accident rate (accidents per vehicle-km) along route segment l Ll : length of route segment l

8. EXISTING RISK MODELS • Societal Risk (Erkut, E. and Verter, V., 1998): TARl : truck accident rate (accidents per vehicle-km) along route segment l Ll: length of route segment l P(R|A)l :probability of a hazmat release given an accident for route segment l dl: population density around road segment l (persons per sq-km) rm: impact radius of hazmat type m

9. EXISTING RISK MODELS • Incident Risk (Harwood, Viner and Russell, 1993): TARl : truck accident rate (accidents per vehicle-km) along route segment l Ll: length of route segment l P(R|A)l : probability of a hazmat release given anaccident forroute segment l

10. PROPOSED MODEL We contend that; • Risk is proportional to • the size of population exposed to risk • the duration of exposure • More suitable and realistic risk model for urban • settings like Istanbul.

11. PROPOSED MODEL • Time-Based Risk (Ak and Bozkaya, 2008): Vl: truck speed (e.g. km per hour) on link l Ll : length of route segment l clm: total population within a danger circle

12. Methodology • For each risk model : • Risk exposure impedance value for all road segments is computed • Using ArcGIS Network Analyst Extension: • A route that minimizes a given risk criterion is generated • Statistics for other risk criteria are “accumulated” along the route

13. Figure 1. Calculation of time-based risk: impact area around a road link

14. Figure 2. Calculation of time-based risk: accumulation of risk values

15. GIS APPLICATION • We haveselectively used the following datasets with our model and the existing risk models: • Aggregated population counts (aggregated by province, county, or district) • Building locations (for estimating detailed population counts) • Street-level road network with road lengths and travel time impedances • Impact radius by hazmat type • Estimated miles or kilometers for vehicles carrying hazmat • Truck accident rates by road type • Probability of release given a truck accident • Truck speeds by road type

16. GIS APPLICATION Table 1. Default Release Probability for Use in Hazmat Routing Analyses(Harwood, Vinerand Russell, 1993)

17. GIS APPLICATION Table 2. Truck Speed Values For Use In HazmatRouting Analyses by Road Type

18. GIS Application Figure 3. Data Preparation Screenshot (Ak and Bozkaya, 2008)

19. Figure 4. Risk Exposure Map of Asian side of Istanbul based on the Time-Based Risk Model

20. Figure 5. ArcGIS Desktop Network Analyst Extension analysis screen

21. Figure 6. Comparison of paths generated with different risk models

22. Table 3. Hazmat route comparison by risk model

23. GIS Application Figure 7. Proposed decision support framework analysis screen (Ak and Bozkaya, 2008)

24. Figure 8. Danger circles according to Time-Based Risk values

25. A SLIGHTLY REVISED VERSION OF THE TIME-BASED RISK MODEL By incorporating the congestion factor into first Time-Based Risk Model equation: HereCFldenotes the congestion factor applicable to link l. CFlcan be greater than, equal to, or less than 1.

26. Figure 9. Two different routes based on CFl factor (Congestion Scenario

27. Congestion Scenario for Hazmat Shipment Table 4. Impedance values according to CFl factor (Congestion Scenario)

28. CONCLUSION • GIS-based SDSS for generating and visualizing hazmat transportation routes with respect to various risk models. • SDSS allows changing road network parameters and assesing the resulting change in risk exposure. • The results indicate that our model can generate routes alternative to traditional ones, and attempt tosimultaneously consider two commonly used criteria: population exposedduring the shipment, and duration of the route.

29. FUTURE RESEARCH • Risk exposure to other people, vehicles or economic assets • Taking into consideration the dynamic and stochastic nature of trafficcongestion.

30. Thank You… Any Questions?