1. Simulation of Non-Recurrent Road Congestion CASA Seminar Ed Manley EngD Candidate University College London Supervisors Dr Tao Cheng (UCL) Prof Alan Penn (UCL) Mr Andy Emmonds (TfL)

2. Outline Theoretical framework Approach being pursued Progress with simulation Future directions

3. Theoretical Framework Non-Recurrent Congestion Outcome of reactions to an incident or event Responses of individuals is difficult to predict Resulting impact can cascade across network in unanticipated directions Blockage Traffic Volume Congested Heavy Medium Light Road Size A Road B Road Residential Before IncidentAfter Incident

4. Theoretical Framework Complexity Congestion emerges through interactions Interactions arise through individual choices Normally within bounds of network capacity Incidents cause an increase in path clashes MACROSCOPIC Congested Network emergence MICROSCOPIC Movement Choices

5. Theoretical Framework Existing Modelling Approaches Designed to simulate ‘normal’ conditions Behaviour from macroscopic perspective Locomotion aspects microscopic Assumptions of equilibrium and homogeneity in decisions –Perfect network knowledge –Rational, shortest path choice –Unrealistic in non-‘normal’ environments

6. Approach Agent-based Simulation Every individual is autonomous, acting only on their own aims, ideas, knowledge and goals Accumulation of behaviours and reactions amounts to the global picture Microscopic effects leading to macroscopic phenomena

7. Simulation Preliminary Developments Java application, using Repast framework Incidents on the London road network Spatially continuous, temporally discrete Currently quite simple, but highly flexible Not yet an accurate simulation, but a platform on which to build greater complexity

8. Simulation Driver Wayfinding Knowledge of Network ‘Taxi Driver’ ‘Commuter’ ‘Tourist’ Congestion Charge Zone Entry Advanced Traffic Information Shortest Path Origin Random Destination Each driver is represented individually in this way

9. Simulation Driver Rules OriginsDestinations Road Network ArcGIS files Route Execute Plan Check for Road Closures ahead Check for Vehicles ahead On Road Change Route Reach Destination At Junction Change SpeedNegotiate Agents Created If ATIS, route around road closures Number specified in Input files Yes Desired speed

10. Simulation Data Extraction Data exported by simulation –Traffic counts (vehicles passing along roads) –Journey times along road section –During or at end of simulation CSV format, compatible with many software Can be customised into any format

11. Simulation Demonstration Demonstration of software in current form Presentation of two road closure scenarios and data relating to change in network

12. Simulation Demonstration Scenario 1: Closure of Blackwall Tunnel – 900 vehicles 6 Origin points – North of Blackwall Tunnel along Northern Approach 1 Destination point – Sun in the Sands roundabout Slide 1: Normal behaviour – Traffic counts Slide 2: Response to closure – Traffic counts Slide 3: Normal behaviour – Journey times Slide 4: Response to closure – Journey times

13. Normal behaviour – Traffic counts

14. Response to closure – Traffic counts

15. Normal behaviour – Journey times

16. Response to closure – Journey times

17. Simulation Demonstration Scenario 1: Mean Driver Journey Times Pre-Closure: Taxi = 11 minutes 11 seconds Commuter = 11 minutes 21 seconds Tourist = 11 minutes 10 seconds Post-Closure: Taxi = 20 minutes 26 seconds Commuter = 25 minutes 39 seconds Tourist = 25 minutes 40 seconds

18. Simulation Demonstration Scenario 2: Closure of Edgware Road – 1100 vehicles 11 Origin points – North-West London (Hampstead, Brent Cross, Camden Town, Queens Park) 1 Destination point – Elephant and Castle, South London Slide 1: Normal behaviour – Traffic counts Slide 2: Response to closure – Traffic counts Slide 3: Normal behaviour – Journey times Slide 4: Response to closure – Journey times

19. Normal behaviour – Traffic counts

20. Response to closure – Traffic counts

21. Normal behaviour – Journey times

22. Response to closure – Journey times

23. Simulation Demonstration Scenario 2: Mean Driver Journey Times Pre-Closure: Taxi = 12 minutes 46 seconds Commuter =13 minutes 58 seconds Tourist = 14 minutes 24 seconds Post-Closure: Taxi = 12 minutes 52 seconds Commuter = 18 minutes 13 seconds Tourist = 18 minutes 43 seconds

24. Simulation Sizes and Speeds Current Maximum tested: 5500 agents Speed (900 agents, travelling 6 miles) ≈ 1 minute Planned speed improvements: –64-bit processing –Parallel processing –GPU processing

25. Future Directions Simulation Development Routing Heuristics –Implementation and testing of existing heuristics models –Use of Space Syntax angularity measures –GPS or mobile phone traces to be used to measure reactions to incidents Cognitive Mapping –Need greater understanding of variation in knowledge –Apply probabilistic model of knowledge to population

26. Future Simulation Driver Modelling Knowledge of Network Cognitive Maps Location-based Landmark guided Routing Mechanism Modelling Foundation Space Syntax Coarse-to-fine Heuristic methods Multiple Factors On-route guidance Congestion aversion Time of day Weather Trace data Trip Purpose Origin-Destination Geodemographic For thousands of individuals

27. Future Directions Simulation Development Realistic network infrastructure and physical movement models Integration with other software Computational improvements (GPU, 64-bit) Intervention modelling and testing Validation using existing datasets

28. Conclusions Irregular incidents difficult to model with aggregated simulation methods Relatively simple simulation developed –Individual behaviour → Macroscopic phenomena –Platform on which to build further complexity Future focus on route-decision process around unexpected incidents

29. Thank you Any Questions? Ed Manley edward.manley.09@ucl.ac.uk