Modeling parking search behavior in the city center: A game-based approach

doi:10.1016/j.trc.2020.102800

Transportation Research Part C: Emerging Technologies

Volume 120, November 2020, 102800

https://doi.org/10.1016/j.trc.2020.102800 Get rights and content

Highlights

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We employ a serious game to reveal parking search behavior.
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AFT and MNL regression models are applied to analyze parking choices.
•
Driver behavior is suboptimal, myopic and risk-averse.
•
Drivers’ search path choice complies with a biased random walk model.
•
Formalized decision making rules can be used to characterize agents in a parking ABM.

Abstract

Drivers cruising for scarce on-street parking in city centers create negative externalities, including congestion and pollution. We apply a serious game – PARKGAME – to understand and model drivers' two intertwined instantaneous parking choices: when to quit cruising and where to cruise. Forty-nine participants took part in a lab experiment in which they had to arrive on time to a fictional appointment or face monetary penalties, and had to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Participants played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and a nearby parking lot of unlimited capacity. Players’ parking choices were analyzed with accelerated-failure time (AFT) and multinomial logistic regression models. Results show that drivers are mostly myopic and risk-averse, and quit their on-street parking search long before the optimal moment. Spatially, drivers are attracted by the lot-destination axis, and their turn choices at junctions comply with a second-order biased random walk. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.

Introduction

Automated vehicles will likely revolutionize urban transportation and turn the parking problem into a marginal issue (Millard-Ball, 2019). Until then, long cruising times for parking will remain an inherent and rather unpleasant attribute of car travel in metropolitan regions. While cruising is a known contributor to traffic congestion (Hampshire et al., 2016), it also has additional negative externalities including travel time wastage, and air and noise pollution (Inci et al., 2017).

Typically, cruising involves time-to-money tradeoffs in the vicinity of driver destinations, between certain but pricey parking at a paid and possibly distant lot and uncertain yet cheaper and potentially closer to destination on-street parking. It is the latter that motivates drivers to continue to search and cruise. Understanding driver behavior in response to parking availability and prices is thus critical for establishing sustainable parking policies that can reduce cruising effectively.

Parking policy making is based on an understanding of the collective reaction of drivers to changing parking prices or constraints in a heterogeneous urban space. Adequate representation of drivers’ parking search demands a high-resolution and explicit representation of the urban space – buildings, streets, parking lots. This can be achieved with Agent-Based models (ABM) (Benenson and Torrens, 2004) of parking search where each driver is represented by an artificial agent that is assigned a destination and certain behavior rules, possibly dependent on the driver’s socio-demographic characteristics e.g. age, gender, income. (Benenson et al., 2008, Levy et al., 2013, Karaliopoulos et al., 2017, Dieussaert et al., 2009). The knowledge of a driver’s search decisions in response to (1) parking prices and constraints, (2) instantaneous occupation rate, and (3) distance to destination and off-street parking facilities are key for establishing and turning such a model into an effective policy support tool. The existing knowledge is strongly biased towards the reaction of drivers to parking prices (Lehner and Peer, 2019), while the other two factors – the occupation rate and distance to destination remain obscure.

The goal of this paper is to bridge this gap and experimentally establish models of individual parking search behavior as a background for studying the collective consequences of parking policy measures with a spatially-explicit, empirically-based parking ABM. To this end, we study and analyze parking behavior based on gamified lab experiments with the PARKGAME Serious Game. We focus on two major components of cruising behavior: the temporal choice of when to quit cruising and park at a parking lot and the spatial choice of the cruising path.

The rest of the paper is organized as follows: Section 2 reviews the state-of-knowledge of parking search behavior; Section 3 presents the methodology of our study; Section 4 establishes a theoretical view of the expected behavior in PARKGAME; Section 5 presents the results of the gamified experiments. We discuss the results and possible future research direction in Section 6 that concludes the paper.

Section snippets

What is known about drivers' parking behavior?

There are many reasons why a driver heads to a destination and as a consequence searches there for parking (Dogru et al., 2017, Shiftan and Burd-Eden, 2001). To date, understanding of drivers’ parking behavior is mostly based on the analysis of revealed (RP) and stated preference (SP) data and, rarely, on field experiments. The initial wave of parking search behavior studies began with the seminal work of Polak and Axhausen (1990), who, based on interviews with drivers, formulated eight

PARKGAME serious game platform

The urban infrastructure in PARKGAME consists of street links the player can traverse and off-street parking facilities, represented by GIS layers in a standard shapefile format. On-street parking spots are constructed by the game software at 4 m distance from each other along the street links in line with the direction of traffic. This distance was based on field surveys (Benenson et al., 2008). On-street spots and parking lots are shown to the player as dots in green if vacant, or red if

Optimal and myopic cruising behavior

When players cruise past the meeting time, the late fine increases and the potential reward diminishes. Players are made aware of this in the briefing and can adjust their parking search tactics during the training phase. An important question arises whether, for a given scenario, there is an optimal moment to quit cruising and park at the lot. Another question is whether search tactics exist that, depending on the scenario, can guarantee the highest overall gain in the 8–16 repetitions of the

Results

Drivers’ choices are captured at junctions. Two choices are involved. The first choice (#1) is whether (i.e. how long) to continue cruising for an uncertain yet cheaper on-street parking spot or head to the certain, but more expensive lot. Players who choose to continue cruising (in #1) have to make a second choice (#2) and decide on whether to approach (i.e. drive closer to), remain at the same distance, or recede (i.e. drive farther away) from the destination and/or parking lot. In short,

Discussion

Our study exploits a serious game for establishing quantitative models of drivers’ parking search behavior. While cruising for parking under the pressure of time, drivers make decisions of two kinds: whether to continue the search for cheap on-street parking or park at the expensive off-street lot; and how to cruise – if to extend the search area by driving farther away from the destination or stay close to it. We gave players the opportunity to adapt and adjust their search tactics to the

Acknowledgements

This research was supported by the ISRAEL SCIENCE FOUNDATION (grant No. 1160/18) “Tessellation of urban parking prices”. We express our deepest gratitude to Dr. Roni Golan and Prof. Danny Ben-Shahar for a swift and concise instruction in discrete choice models, and to Dr. Nadav Levy for raising the idea of a game-based parking search model. We would also like to thank Carole Shoval for professional language editing. The comments and suggestions of three anonymous reviewers are also very much

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View full text

Modeling parking search behavior in the city center: A game-based approach

Highlights

Abstract

Introduction

Section snippets

What is known about drivers' parking behavior?

PARKGAME serious game platform

Optimal and myopic cruising behavior

Results

Discussion

Acknowledgements

Comput. Environ. Urban Syst.

Transp. Res. Part C: Emerg. Technol.

Transp. Res. Part B: Methodol.

Transp. Res. Procedia

Transp. Rev.

Comput. Environ. Urban Syst.

Transp. Res. Part F: Traffic Psychol. Behav.

Transp. Res. Part C: Emerg. Technol.

Transp. Res. Procedia

Transp. Policy

Econ. Transp.

Transp. Res. Part B: Methodol.

Transp. Res. Part C: Emerg. Technol.

Reg. Sci. Urban Econ.

Transp. Res. Part A: Policy Pract.

Transp. Res. Procedia

Transp. Policy

Transp. Policy

Transp. Res. Part A: Policy Pract.

A new look at the statistical model identification

Geosimulation: Automata-based modeling of urban phenomena

Modelling drivers’ car parking behaviour using data from a travel choice simulator: a stated preference experiment

Transp. Res. Part A: Policy Pract.