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Nir Fulman , PhD student

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I received a BA summa cum laude in geography from Tel Aviv University in 2015 and have chosen a direct PhD studies at the Porter School of Environment and Geoscience. My PhD study focuses on studies of drivers parking behavior and modeling urban parking. My wider academic interests include spatial analysis and mathematical modeling of complex system dynamics with applications to urban systems. I teach a basic course of GIS at the Geography department and several courses on QGIS/PostGIS/Postgres within the framework of the Open Source GIS project

Participation in funded research projects:

  • From Proprietary to Open Source Tools for Management and Analysis of Governmental Spatial Big Data, Survey of Israel, 2017 – 2019

  • Adaptive Dynamic Parking Pricing: From theory to Practical Implementation, Israeli Prime Minister Office, Innovation in Transportation Fund, 2016-2018 

  • Tessellation of urban parking prices, Israeli Science Foundation, 2018 - 2021

Revealing parking search behavior with a serious game


Understanding drivers’ behavioral response to parking prices and the search time is critical for establishing effective parking policies that reduce cruising. We exploit serious PARKGAME to gain insights into behavioral rules that guide parking behavior.


The player navigates a virtual road network using keyboard keys, trying to get in time to a meeting at a destination. At the beginning of the game, the player is given a budget for parking and for paying delay fines if late to the meeting. The parking cost is lower than this budget allowing to save money if arriving on time. Two parking options are available: (1) A pricey parking lot which is located at varying distances from the destination, and where parking is always available and (2) Cheap curbside parking on highly occupied streets but with very uncertain search time and distance between parking spot and destination.











Game screenshot: A player has just passed the destination and drives toward the parking lot.
Details important for decision-making are presented on the UI


49 participants participated in sessions performed in a Manhattan-like city grid of 10X10 blocks. Players start the game at a distance of 1-minute drive from the destination. Drivers’ behavior was studied in three scenarios: In scenario A, the lot was at a distance of 45 m from the destination, and the time between a start of the game and the meeting was 3 min. Scenarios B and C were devised for studying the influence of the parking lot's location on cruising behavior. In scenario B, the lot was located 135m (135-sec walk) from the destination, and the time between the start of the game and the meeting was also 3 min. That is, in scenario B a driver has less non-fined time for the on-street search. In scenario C the game duration was longer, 4:30 min. This left to the driver the same non-fined search time as in scenario A. After an initial learning period, each player played each scenario 8 times.


The experiments revealed that cruising players make two main types of decisions: The first – temporal – is whether to continue the search for the uncertain yet cheap on-street parking or to cancel the search and drive to the expensive lot. Risk-averse players parked at the lot, accepting low gains without ever being late, whereas other players took the risk of a longer search for on-street parking.


The second type of decision regards the spatial pattern of search – to cruise closer to the destination, or search further away and walk longer back to the destination. In this respect, the basic incentive of all drivers was to remain closer to the destination and they rarely drifted more than 300 m away from it. A preliminary model of the driver’s parking search was specified as a Markov process, namely, a random walk biased towards the destination. When close to the destination, the probability to get closer decreases, whereas further away it increases, and vice versa. When fully established, the model of parking behavior will be incorporated into a dynamic agent-based model of parking in the city, whose goal is to minimize urban cruising.


My software for free download
An algorithm for establishing heterogeneous parking prices

My favorite software

- QGIS - Free and open-source (FOSS) desktop GIS

- Eclipse - Integrated development environment (IDE)
- PyDev - Eclipse plugin for programming in Python

- PostgreSQL - FOSS object-relational database

- PostGIS - Spatial database extender for PostgreSQL 


  1. N. Fulman, I. Benenson, 2018, Agent-based modeling for transportation planning: A method for estimating parking search time based on demand and supply. In: Proceedings of the 10th International Workshop on Agents in Traffic and Transportation (ATT 2018), CEUR Workshop Proceedings, 2129, 31-39

  2. N. Fulman, I. Benenson (accepted for publication, 2018), Establishing heterogeneous parking prices for uniform parking availability for autonomous and human-driven vehicles, IEEE Intelligent Transportation Systems Magazine

  3. N. Fulman, I. Benenson, 2017, Simulating parking for establishing parking prices, Procedia Computer Science 109C, 911–916

Lectures at Conferences and Meetings

  1. N. Fulman, 2016, An algorithm and software for establishing heterogeneous parking prices, Data and Cities as Complex Adaptive Systems Summer School, Manchester, UK

  2. N. Fulman, I. Benenson, 2016, Pricing parking using a static and spatially-explicit model, Annual Conference of the Israeli Regional Science Association, Acre, Israel

  3. N. Fulman, I. Benenson, 2017, Simulating parking for establishing parking prices, 8th International Conference on Ambient Systems, Networks and Technologies (ANT 2017), Madeira, Portugal

  4. N. Fulman, I. Benenson, 2017, Parking search time: An ultimate explanation, Seminar on Information and Incentives as Means for Promoting Transportation System Objectives, Haifa, Israel

  5. N. Fulman, I. Benenson, 2017, A spatially explicit model for establishing adaptive parking prices, Geocomputation, Leeds, UK

  6. N. Fulman, I. Benenson, 2017, Why do we search for parking for so long? The European Colloquium on Theoretical and Quantitative Geography, York, UK

  7. N. Fulman, Y. Shaham, 2017, In search of behavioral heuristics – From agent-based to game-based urban modeling: Drivers’ parking searching behavior, Smart Cities Conference: Potentials, Prospects and Discontents, Tel-Aviv, Israel

  8. N. Fulman, I. Benenson, 2017, Why does parking search take such a long time? 58th Israeli Geographical Association Conference, Beer-Sheba, Israel

  9. N. Fulman, I. Benenson, 2018, Simulations and serious games for urban parking planning, Geography department research seminar, Tel Aviv, Israel

  10. N. Fulman, I. Benenson, 2018, ABM as a tool for transportation planning: Mapping parking search time, 10th International Workshop on Agents In Traffic and Transportation (ATT 2018) held in conjunction with ECAI/IJCAI, AAMAS and ICML conferences (FAIM 2018), Stockholm, Sweden

Poster Presentations

  1. N. Fulman, 2016, An algorithm and software for establishing heterogeneous parking prices. International Workshop on the Economics of Parking, Barcelona, Spain.

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