The Potential Impact of Vehicle-to-Vehicle Communication on On-Street Parking Under Heterogeneous Conditions

The aim of this paper is to study the impacts of bottom-up information provision about on-street parking places on parking dynamics under heterogeneous conditions. Using an agent-based simulation model, performance is compared between a bottom-up vehicle-to-vehicle communication strategy and a strategy that combines parking sensors and vehicle-to-vehicle communication. In the latter approach onstreet parking places are all equipped with sensors capable of disseminating their status. The results show that search time is decreased for informed 'smart' cars, especially under spatially heterogeneous conditions, for the sensor-based strategy. Furthermore, for the case of the sensor-based strategy, the results point out that smart cars outperform regular cars in terms of walking distance under all circumstances. The positive impacts for the vehicle-to-vehicle strategy are limited to walking distance improvements only.

[1]  Karel Martens,et al.  The Potential Impact of Vehicle-to-Vehicle and Sensor-to-Vehicle Communication in Urban Parking , 2015, IEEE Intelligent Transportation Systems Magazine.

[2]  Michiel van Wezel,et al.  Cbprs: A City Based Parking and Routing System , 2008 .

[3]  Donald Shoup,et al.  THE TROUBLE WITH MINIMUM PARKING REQUIREMENTS , 1999 .

[4]  Ioannis Stavrakakis,et al.  Parking Assisting Applications: Effectiveness and Side-Issues in Managing Public Goods , 2013, 2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops.

[5]  Naphtali Rishe,et al.  Learning the relevance of parking information in VANETs , 2010, VANET '10.

[6]  Merkourios Karaliopoulos,et al.  Bounded rationality can increase parking search efficiency , 2014, MobiHoc '14.

[7]  Jie Lin,et al.  Spatio-temporal Information Ranking in VANET Applications , 2010, Int. J. Next Gener. Comput..

[8]  Felix Caicedo.  Real-time parking information management to reduce search time, vehicle displacement and emissions , 2010 .

[9]  Andry Rakotonirainy,et al.  Empirical IEEE 802.11p performance evaluation on test tracks , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[10]  I. Leontiadis,et al.  Opportunistic spatio-temporal dissemination system for vehicular networks , 2007, MobiOpp '07.

[11]  I. Benenson,et al.  From modeling parking search to establishing urban parking policy , 2008 .

[12]  Donald C. Shoup.  Parking on a Smart Campus: Lessons for Universities and Cities , 2005 .

[13]  P. Rietveld,et al.  Empirical evidence on cruising for parking , 2012 .

[14]  Itzhak Benenson,et al.  Exploring cruising using agent-based and analytical models of parking , 2013 .

[15]  Martin Mauve,et al.  Decentralized discovery of free parking places , 2006, VANET '06.

[16]  van der,et al.  Pamela, a parking analysis model for predicting effects in local areas , 2012 .

[17]  Itzhak Benenson,et al.  PARKAGENT: An agent-based model of parking in the city , 2008, Comput. Environ. Urban Syst..

[18]  D. J. Shah,et al.  Vehicular parking space discovery with agent approach , 2011, ICWET.

[19]  M. Gallo,et al.  Optimisation models for the urban parking pricing problem , 2006 .

[20]  Vivian Prinz,et al.  Cars communicating over publish/subscribe in a peer-to-peer vehicular network , 2009, IWCMC.

[21]  Hai-Jun Huang,et al.  Modelling heterogeneous drivers’ responses to route guidance and parking information systems in stochastic and time-dependent networks , 2012 .

[22]  Aloys Borgers,et al.  PAMELA: Parking Analysis Model for Predicting Effects in Local Areas , 2002 .

[23]  Sylvain Lecomte,et al.  A cooperative reservation protocol for parking spaces in vehicular ad hoc networks , 2009, Mobility Conference.