Simulation of Non-Recurrent Road Congestion CASA Seminar Ed Manley EngD Candidate

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. Blockage Theoretical FrameworkNon-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 Before Incident After Incident Traffic Volume Road Size Congested A Road Heavy B Road Medium Residential Light

4. Theoretical FrameworkComplexity • 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 MICROSCOPIC Movement Choices emergence

5. Theoretical FrameworkExisting 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. ApproachAgent-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. SimulationPreliminary 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. SimulationDriver Wayfinding Congestion Charge Zone Entry Origin Knowledge of Network Shortest Path ‘Taxi Driver’ ‘Commuter’ ‘Tourist’ Advanced Traffic Information Random Destination Each driver is represented individually in this way

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

10. SimulationData 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. SimulationDemonstration • Demonstration of software in current form • Presentation of two road closure scenarios and data relating to change in network

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

27. Future DirectionsSimulation 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 youAny Questions?Ed Manleyedward.manley.09@ucl.ac.uk