1. 1 Geography and road network vulnerability Erik Jenelius Div. of Transport and Location Analysis Royal Institute of Technology (KTH), Stockholm

2. 2 Aims of the presentation Study the vulnerability of different geographic regions in Sweden’s road network Assess the regional equity of the road network in terms of vulnerability Find properties of geography, network and traffic that explain regional differences, develop proxy variables

3. 3 Road network vulnerability Vulnerability is a susceptibility to incidents that can result in considerable reductions in road network serviceability (Berdica, 2002) Typical scenarios: Extreme weather, landslides, major accidents, malevolent attacks Vulnerability analysis should contain both probability/frequency and consequence In the following, we focus on consequence (conditional vulnerability)

4. 4 Consequence measure (1) Considered incident: A single road link is completely cut off/blocked/closed a certain period Some assumptions: 1.Changes only in route and departure time choices, not in trip generation, destination or mode choices 2.Users choose shortest route 3.Perfect information on the incident 4.Constant demand/hr Consequence measure: Increased travel time/delayed arrival for car users

5. 5 Consequence measure (2) Two possibilities during closure: 1.No alt. routes: Users wait until link reopened x od = demand/hr, t open = closure duration 2.Alt. routes: Users take new shortest route, or if better, wait Value of alt. routes increases with closure duration

6. 6 Regional exposure (conditional vulnerability) The average-case exposure of a region is the expected consequences for the region of a randomly located link closure Two variants: 1.User exposure: The average increase in travel time per user starting in the region 2.Total exposure: The total increase in travel time for all users starting in the region (socio-economic consequence)

7. 7 Regional inequity Large regional disparities in exposure indicate spatial inequity between users and regions A measure of equity: Gini coefficient G G = 0: perfect equity G = 1: perfect inequity

8. 8 Case study: Sweden Two closure durations: 30 minutes and 48 hours Average-case user and total exposure of every municipality (289) Network, O-D demand and equilibrium link travel times from SAMPERS No congestion effects - underestimation in dense areas 77,769 nodes, 174,046 links, 8,764 centroids

9. 9 The road networkPopulation density

10. 10 User exposure 30 mins: G = 0.3548 hrs: G = 0.64

11. 11 Total exposure 30 mins: G = 0.4348 hrs: G = 0.71

12. 12 Proxy variables (1) What affects the regional disparities in user and total exposure? Long closure: Location of cut links Short closure: 1.sparsity of the regional road network 2.average/total initial travel times of the users

13. 13 Proxy variables (2) Two measures of road network sparsity: 1.Geographic sparsity: 2.Network sparsity: where A r = surface area, L r = length of road, l r = average link length,  r = links-to-nodes ratio of region r

14. 14 User exposure (30 mins) adj R 2 = 0.87

15. 15 Total exposure (30 mins) adj R 2 = 0.89

16. 16 Conclusions Considerable regional disparities in exposure and importance, larger for longer closures Results are robust to change of partition Interesting topics for further research: How would congestion effects, more realistic closure probabilities etc affect the results? Compare with other countries Universality of proxy variables?

17. 17 Thank you