GRIDLOCK IN CITIES Nikolas Geroliminis Carlos Daganzo

Research Goal Model a city in macroscopic basis AND Develop system-wide Control strategies TO IMPROVE MOBILITY

Why macroscopically? Prediction-based models require time dependent O-D tables Highly congested networks exhibit chaotic behavior 1, 2 1, 2 Daganzo, 1998 and 2005 EXISTING MODELS ARE NOT REALISTIC AND APPROPRIATE TO DEAL WITH CROWDED CONDITIONS

URBAN GRIDLOCK: KEY ISSUES Move from PREDICTION to OBSERVATION ROBUST APPROACH PROPOSE→ MONITOR→MODIFY Information Technology

Fundamental Diagram (FD) for a link i 3 Regimes I : Undersaturated II : Efficient III : Oversaturated Growing queues from the downstream link block the arrivals Accumulation : n i (vehs) Travel Production : P i (veh-km/hr) Output : e i (vh/hr) P i, e i nini Qi(ni)Qi(ni) αQi(ni)=Gi(ni)αQi(ni)=Gi(ni)

FD Generalization to networks AGGREGATE BEHAVIOR = SCALED UP VERSION OF LINK BEHAVIOR

AGGREGATE DYNAMICS 1 Given : inflow O Exit function G(n) e = G(n) 1 Daganzo (2005)

Theory Validation (I) 0.3 km 1000ft A B C D BIGGER IS BETTER!

Traffic Regimes AB CD

Theory Validation (II) INPUT OUTPUT PREDICTION ACCUMULATION Production OBSERVABLE - Output UNOBSERVABLE

Theory Validation (III) Travel Production (VMT) OUTFLOWOUTFLOW

Theory Validation ( Ongoing ) Field Experiment Japan – Yokohama –400 taxis (GPS data) –Loop detector counts Partner: Masao Kuwahara (University of Tokyo, Japan) 1 km 0.62mil

R2R2 R1R1 R1R1 R2R2 inflow nini Ci(ni)Ci(ni) outflow nini Gi(ni)Gi(ni) Dynamics of a 2-reservoir system

Gridlock Simulation BEFORE CONTROL WITH CONTROL R2R2 R1R1 PARETO EFFICIENT

Applications (I) Transport Modeling for Nairobi Metropolitan Area –Improve the vehicle-carrying capacity –Improve the passenger-carrying capacity –Demand management strategies Joint project (Columbia University and UC Berkeley)

Applications (II) Xi’an (China) Collaborator: Yuwei Li (University of California, Berkeley)

Ongoing Work (I) Multi-reservoir systems –more or less homogeneous in traffic loads –reasonable static and dynamic system representation The effect of parking –Decrease in the outflow –Dynamic Behavior

Ongoing Work (II) Optimum Control Strategies How and Where to control? –Efficient –Equitable Pricing –Parking –Tolls Multimodal Systems R2R2 R1R1

QUESTIONS - SUGGESTIONS