1. An Agent-Based Cellular Automaton Cruising-For-Parking Simulation A. Horni, L. Montini, R. A. Waraich, K. W. Axhausen IVT ETH Zürich July 2012

2. Thompson and Richardson (1998), A Parking Search Model "Parking plays an important role in urban transport systems." "Motorists have been observed spending a significant percentage of their total trip time searching for a car park ( [Huber, 1962] and [Axhausen and Polak, 1991]). […]” Shiftan, Burd-Eden (2001), Modeling Response to Parking Policy "Parking policy is one of the most powerful means urban planners and policy makers can use to manage travel demand and traffic in city centers.“ Arnott and Inci (2005), An Integrated Model of Downtown Parking and Traffic Congestion “[…] In fact, traffic experts simply do not know what proportion of cars on downtown city streets are cruising for parking.” 2 Scale of Parking Search

3. Munich and Regensburg Montini et al. (2012), Searching for Parking in GPS Data (S11) 3 Scale of Parking Search

4. RP and SP surveys (Axhausen and Polak, 1991, Weis et al., 2011) laboratory experiments (Bonsall et al., 1998) car-following (Wright and Orram, 1976) riding with a searcher (Laurier, 2005) GPS surveys (Montini et al. 2012) simulations (PARKAGENT, PARKIT,...) 4 Parking Search Modeling

5. GPS Processing (Montini et al.) 5 Destination Choice (Horni et al.) ca-based cruising for parking simulation MATSim Parking Choice and Search (Waraich et al.) parameter extraction for calibration MATSim interaction effects application Context

6. t0t0 t1t1 t0t0 t1t1 transition process equilibrium (iterative) models needs to be efficient but not behaviorally sound characteristics or uniqueness needs to be defined (not under-determined) rule-based (sequential) models needs to be behaviorally sound needs to be clearily defined does not matter as long as within boundary conditions search process q0q0 q1q1 t0t0 t1t1 t0t0 t1t1 simulated period s1s1 s0s0 Simulation Concept - A Rule-Based Model

7. 7 Overall goal: Implementation task Goal here: Generate aggregate models for parking search key measures (here t search ) … similar to estimated functions such as … «parking fundamental diagram» … for hybrid application Goal Axhausen et al. (1994) PGI Frankfurt a. M.

8. Simulation Main Components: Framework - Implementation SIMULATION input Supply (network & parking infrastructure) Demand (population trips) output «Parking Fundamental Diagram» Output Generation Analyzer SpatialElementDrawer ScenarioPlotter Parking Decision Modeling AcceptanceRadius/Linear/Quadtratic ParkingDecision/Linear/Quadratic RandomRouteChoice WeightedRandomRouteChoice Initialization InfrastructureCreator XMLReader PopulationCreator Infrastructure ParkingLot NNode NLink LCell SpatialElement contains attached to derived creates Drive simulation componets CA simulate() update() plot() end SQueue CAServer queueHandling supports Population Agent Route contains provide parking decisions Global Controller setup() simulate() SConfig update populates

9. Simulation Main Components: Cellular Automaton 9 update process on randomly chosen links, nodes and parking lots as in famous Nagel and Schreckenberg (1992) CA future: parking search speeds

10. CAServer class for update process: not naively iterating over all agents and infrastructure elements (e.g., cells) but only over occupied ones -> queues of agents, links nodes and parking lots resolution queue models – CA – car following models jam density used for cell size as in Wu and Brilon (1997) future: maybe pool cells in free flow conditions 10 Simulation Main Components: Cellular Automaton

11. parking type choice exogenously, derived from supply (for ZH scenario only) search tactic search starting point (latent, GPS study …) weighted random walk destination approaching efficiency agent’s memory of parking lots with free spaces 11 Simulation Main Components: Parking Search Modeling

12. parking lot choice Acceptance radius 12 Simulation Main Components: Parking Search Modeling

13. 3 small-scale scenarios for development and calibration 13 Zurich Inner-city scenario derived from real-world data (MATSim demand), navigation network ready, but not yet calibrated & speed issues! Results and Scenarios

14. 14 100 agents 2 origins, 1 destination 30 min simulated Results: Chessboard Scenario

15. Future Calibration: GPS Data Montini et al. (2012), Searching for parking in GPS data (IATBR, S11) approx. 32’000 person days from Zurich and Geneva, raw data (x, y, z, timestamp) 15

16. Future: Application in MATSim - Hybrid Approach equilibrium model parking simulation mobility simulation rule-based model

17. Future: Application in MATSim - Hybrid Approach 17 t search = sample from aggregate functions t search = simulate with CA really necessary apart from parking studies? simulation costs?

18. 18 Future: Destination Choice Interaction Effects + +  no agglomeration terms and  iid

19. Discussion 19 estimated aggregate functions reproduced combination of SOA techniques software structure very similar to MATSim -> easy migration high simulation costs www.ivt.ethz.ch