Modeling Cruising for Parking Itzhak Benenson bennya@post.tau.ac.il - PowerPoint PPT Presentation

Modeling Cruising for Parking Itzhak Benenson bennya@post.tau.ac.il http://www.tau.ac.il/~bennya/ http://geosimlab.tau.ac.il/ Geosimulation and Spatial Analysis Lab, Department of Geography and Human Environment, Tel Aviv University, Israel MTS Summer School 2015

Parking is a long lasting urban blight  Cruising for parking is the last leg of the car-based trip  Cruising is linked to many urban externalities: ◦ Congestion, air pollution and noise, loss of space, social inequity  Parking has mostly been left to engineers to solve supply and operational issues by building lots 2 MTS Summer School 2015

BASIC MODEL OF CRUISING FOR PARKING 3 MTS Summer School 2015

Basic model of cruising for parking: DEFINITIONS Parameters Arrivals a cars/min t  t + 1 Departure rate • Fraction d of the parking cars depart d /min cars that cruise longer than t min • Maximal search time t min depart Total parking places • a cars arrive R • vacant parking places are occupied by the cruising cars Initial conditions: All parking places are occupied 4 MTS Summer School 2015

Basic model of cruising for parking: DEFINITIONS • State vector of the system, M(t) = <M 0 (t), M 1 (t), … M t -1 (t)> , M m (t) is the numbers of cars cruising for m minutes • Total number of cruising cars N(t) = M 0 (t) + M 1 (t) + … + M t -1 (t) • O(t) – Number of occupied parking places • F(t) - Cars that failed to find a parking place • p(t) - probability to park p(t) = min{1, [number of free places]/[number of cruising cars]} p(t) = min{1, (R – (1 - d)*O(t))/N(t)} 5 MTS Summer School 2015

Basic model of cruising for parking: EQUATIONS OF SYSTEM DYNAMICS * 1. Two auxiliary equation to estimate parameters at the start of a next minute N(t) = M 0 (t) + M 1 (t) + … + M t -2 (t) + M t -1 (t) p(t) = min{1, (R – (1 – d)*O(t))/N(t)} 2. The dynamics of M(t) , O(t) and F(t) are described by the recurrence equations: M 0 (t + 1) = a M 1 (t + 1) = M 0 (t)*[1 – p(t)] … M t -1 (t + 1) = M t -2 (t)*[1 – p(t)] F(t + 1) = F(t) + M t -1 (t)*[1 – p(t)] O(t + 1) = min{(1 – d)*O(t) + N(t), R} * N. Levy, K. Martens, I. Benenson, 2013, Transportmetrica A, 9 (9), 773 – 797 6 MTS Summer School 2015

Basic model of cruising for parking: SOLUTION Arrivals: 4/min Dep. rate: 0.05 Capacity: 100 Arrivals: 6/min Dep. rate: 0.05 Capacity: 100 Arrivals: 10/min Dep. rate: 0.05 Capacity: 100 7 MTS Summer School 2015

Basic model of cruising for parking: EXTENSIONS Drivers ’ reaction to the lack parking places or Far from destination: Close to destination: to the high price of parking More attractive, Less attractive, Low parking prices High parking prices • Avoid areas with no parking: Number of arriving cars depends on the density of cruising cars or average cruising time • Avoid areas of expensive parking: Number of arriving cars or maximal cruising time depends on prices • Park further from the destination: Tradeoff between price of the parking place and distance to destination 8 MTS Summer School 2015

Basic model of cruising for parking: EXTENSION Max Arrivals = 10 k = 0.01 9 MTS Summer School 2015

Basic model of cruising for parking: EXTENSION Max Arrivals = 10 k = 0.2 10 MTS Summer School 2015

Basic model of cruising for parking: EXTENSION Max Arrivals = 10 k = 0.9 Three types of parking dynamics • Monotonous convergence • Non-monotonous convergence • Steady cycles 11 MTS Summer School 2015

Basic model of cruising for parking: CONCLUSIONS • Parking search is either easy or takes relatively long time • Drivers ’ reaction to cruising or regulator ’ s measures can decrease the number of cruising cars and parking failures. But, cruising will always be either almost zero or essential • If drivers ’ reaction to cruising is weak or intermediate then the system stabilizes, maybe non-monotonously. Strong feedback cause fluctuations. 12 MTS Summer School 2015

Parking reality: SPATIO-TEMPORAL HETEROGENEITY Local aspects (driver vs destination) • Parking supply is defined by many factors: type of parking (on-street/lot), price, distance to destination, parking control • Parking demand, parking supply, and drivers ’ behavior are all essentially heterogeneous, in space and in time Global aspects (urban parking policy) • In a long term, drivers consider parking as only one component of a trip • Parking supply is defined by the urban land-use policy Parking planning (future transportation) • What will be parking demand in the future? • What is parking demand of the autonomous cars? 13 MTS Summer School 2015

COMPONENTS OF PARKING REALITY Parking Parking Drivers ’ management demand parking and policy and supply behavior assessment WE STUDY THE CURRENT STATE WE AIM AT FORECASTING Parking Parking dynamics spatial in space pattern and time 14 MTS Summer School 2015

Parking Parking demand spatial and supply pattern 15 MTS Summer School 2015

PARKING DEMAND AND SUPPLY* GIS + Aerial photos + Population Census URBAN GIS, AERIAL PHOTOS DEMAND Night: Number of households multiplied car ownership rate Day: Office area/20 or proportional to Shops ’ turnover SUPPLY Curb: Length of streets /5 minus prohibited places Lots: Lot floor area /12 (5 m – length of a car, 12m 2 = 8m 2 car + 4m 2 pass *Levy, N., Benenson, I, 2015, Journal of Transport Geography, 46, 220 – 231 Parking Parking spatial demand 16 MTS Summer School 2015 and supply pattern

PARKING DEMAND AND SUPPLY* Parking turnover: Field surveys For a certain day of the week and hour of a day, parameters of the parking system are stable Residents Visitors Average occupancy Average occupancy STD STD (weekdays) (weekdays) 61.8% 0.94% 17.4% 1.77% * N. Levy, K. Martens, I. Benenson, 2013, Transportmetrica A, 9 (9), 773 – 797 Parking Parking spatial demand MTS Summer School 2015 17 and supply pattern

PARKING PATTERNS* Destination-parking place distance: Field surveys The distance between the parking place Municipality GIS, Remote Sensing data, and the destination population census and field surveys Estimated based on the data provide reliable estimates of parking spatial of owners ’ addresses patterns, demand, supply, and turnover at high spatio-temporal resolution *Levy, N., Benenson, I, 2015, Journal of Transport Geography, 46, 220 – 231 Parking Parking spatial demand 18 MTS Summer School 2015 and supply pattern

Drivers ’ parking behavior 19 MTS Summer School 2015

DRIVERS ’ PARKING SEARCH BEHAVIOR GPS data logging, GIS analysis, interviews with drivers Car speed during the trip 120 100 80 60 40 20 0 15:40:35 15:42:09 15:42:57 15:43:45 15:44:33 15:45:21 15:46:09 15:46:57 15:47:45 15:48:33 15:49:21 15:50:09 15:50:57 15:51:45 15:52:33 15:53:21 15:54:09 15:54:57 15:55:45 15:56:33 15:57:21 15:58:09 16:00:07 16:01:42 16:02:36 16:04:23 16:05:49 16:07:34 Car speed versus distance to parking 30000 Disstance from home 25000 Driver ’ s speed during 20000 parking search is 15000 12-16 km/h (3 - 4 m/s). 10000 5000 0 0 20 40 60 80 100 120 Speed (km/h) Drivers ’ parking 20 MTS Summer School 2015 behavior

Revealed/Stated preferences: Parking Lot Survey Private Muni • Survey of 100 random drivers immediately after they parked in private and municipal parking lots in the CBD area of Tel Aviv • Aim: understand the drivers ’ parking Muni Private behaviour after long term adaptation to conditions and prices. Drivers ’ parking 21 MTS Summer School 2015 behavior

Parking lot survey: RESULTS Two distinct groups – residents and visitors … but with similar behaviors Factor Category N Visitors Residents Sig.  2 =15.6 43 60.0% 20.5% Private Parking type choice 58 40.0% 79.5% p<0.001 Municipal 49 47.4% 50.0% At destination ~2/3 of drivers do not consider cruising a  2 =0.2 Closeness to 34 33.3% 34.1% Up to 5 min walk destination p>0.1 18 15.9% 15.9% More than 5 min walk viable parking choice  2 =0.04 35 37.2% 35.2% Yes Cruising 62 62.8% 64.8% No p>0.1 Parking General conclusion: Majority of drivers exhibit post-adaptive 95 193 121 t=-2.4; p=0.02 duration (min) behaviours. How do they adapt to parking conditions? 95 21.0 9.4 Price (ILS/hr) mean t=3.2; p=0.002 Willingness to 95 13.9 5.8 t=2.7; p=0.008 pay (ILS/hr) Drivers ’ parking 22 MTS Summer School 2015 behavior

ParkGame: THE IDEA and IMPLEMENTATION Aim: Understand cruising behaviour and the choice between curb and lot parking based on simulated cruising experience Drivers ’ parking 23 MTS Summer School 2015 behavior

ParkGame: DESIGN o User experience with real occupancy and turnover rates o Adjustable game duration, speeds (car/walk), prices, penalties. o Given GIS road network layers, adjustable to any city o Imitates driver ’ s limitations (possible view ahead = 5 cars) Drivers ’ parking 24 MTS Summer School 2015 behavior