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)

Outline Theoretical framework Approach being pursued Progress with simulation Future directions

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

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

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

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

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

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

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

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

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

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

Normal behaviour – Traffic counts

Response to closure – Traffic counts

Normal behaviour – Journey times

Response to closure – Journey times

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

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

Normal behaviour – Traffic counts

Response to closure – Traffic counts

Normal behaviour – Journey times

Response to closure – Journey times

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

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

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

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

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

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

Thank you Any Questions? Ed Manley