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dynamical characteristics (i.e.; the price) that may
change from one request to another, but they are fixed
during a single negotiation process.
In this work, a multi-agent smart parking mar-
ketplace is proposed relying on a distributed negoti-
ation mechanism. Negotiation allows parking man-
agers and drivers to evaluate parking spaces accord-
ing to their own private preferences, and to come to
a solution that can be acceptable by both parties. A
dynamic pricing scheme is used to incentivize drivers
to select parking spaces that lead to both a better car
park utilisation, and to limit traffic circulation in spe-
cific city areas. Differently from a centralised ne-
gotiation approach previously proposed, a distributed
solution is more realistic and more suitable to deal
with the complexity of modern transportation sys-
tems. The benefit of the negotiation was evaluated
in terms of a Social Welfare metrics measuring the
degree of satisfaction of all involved parties. Results
showed that, in the case of the number of parking re-
quests smaller than the number of available parking
spaces, the increasing number of Parking Managers
leads to a competition among them, and consequently
to a smaller average utility for the Parking Managers.
Of course, since there are more and better choices for
the Driver Agents, their average utility increases. On
the contrary, a greater number of requests allows each
Parking Manager to allocate more parking spaces, so
leading to an average Parking Managers and Driver
Agents constant utilities. In addition, by increasing
the number of Parking Managers and the number of
queries, the social welfare remain constant, so a de-
centralised negotiation approach does not have a neg-
ative impact on the overall level of satisfaction of the
involved negotiators. Finally, by increasing the num-
ber of available Parking Managers, and so the num-
ber of possible parking choices, the average number
of rounds necessary for successful negotiations de-
creases. So, while from one hand having more than
one PM causes an increased communication cost due
to an increased number of exchanged messages, it
is compensated by a decreased number of rounds to
reach an agreement.
REFERENCES
Adler, J. L. and Blue, V. J. (2002). A cooperative multi-
agent transportation management and route guidance
system. Transportation Research Part C: Emerging
Technologies, 10(5–6):433 – 454.
Barbuceanu, M. and Lo, W.-K. (2001). Multi-attribute util-
ity theoretic negotiation for electronic commerce. In
Agent-Mediated Electronic Commerce III, Current Is-
sues in Agent-Based Electronic Commerce Systems,
pages 15–30. Springer-Verlag.
Barile, F., Di Napoli, C., Di Nocera, D., and Rossi, S.
(2015). Evaluating the social benefit of a negotia-
tion–based parking allocation. In Advances in Practi-
cal Applications of Agents, Multi-Agent Systems, and
Sustainability: The PAAMS Collection, volume 9086
of LNCS, pages 15–26.
Bellifemine, F., Caire, G., Poggi, A., and Rimassa, G.
(2008). Jade: A software framework for developing
multi-agent applications. lessons learned. Inf. Softw.
Technol., 50(1-2):10–21.
Chou, S.-Y., Lin, S.-W., and Li, C.-C. (2008). Dynamic
parking negotiation and guidance using an agent-
based platform. Expert Systems with Applications,
35(3):805–817.
Di Martino, S. and Rossi, S. (2016). An architecture for a
mobility recommender system in smart cities. Proce-
dia Computer Science, 98:425 – 430.
Di Napoli, C., Di Nocera, D., and Rossi, S. (2014). Agent
negotiation for different needs in smart parking alloca-
tion. In Advances in Practical Applications of Hetero-
geneous Multi-Agent Systems. The PAAMS Collection,
volume 8473 of LNCS, pages 98–109.
Di Napoli, C., Pisa, P., and Rossi, S. (2013). Towards a dy-
namic negotiation mechanism for qos-aware service
markets. In Trends in Practical Applications of Agents
and Multiagent Systems, volume 221 of AISC, pages
9–16. Springer.
Endriss, U., Maudet, N., Sadri, F., and Toni, F. (2006). Ne-
gotiating socially optimal allocations of resources. J.
Artif. Intell. Res. (JAIR), 25:315–348.
Geng, Y. and Cassandras, C. (2013). New smart parking
system based on resource allocation and reservations.
IEEE Transactions on Intelligent Transportation Sys-
tems, 14(3):1129–1139.
Haklay, M. and Weber, P. (2008). Openstreetmap: User-
generated street maps. Pervasive Computing, IEEE,
7(4):12–18.
Mejri, N., Ayari, M., and Kamoun, F. (2013). An efficient
cooperative parking slot assignment solution. In The
Seventh International Conference on Mobile Ubiqui-
tous Computing, Systems, Services and Technologies,
pages 119–125. IARIA.
Nakamura, K., Hondo, I., Hataoka, N., and Horii, S. (2000).
Car information systems for its. Hitachi Review,
49(3):102–106.
Pan, B., Crotts, J. C., and Muller, B. (2007). Develop-
ing web-based tourist information tools using google
map. In Sigala, M., Mich, L., and Murphy, J., edi-
tors, Information and Communication Technologies in
Tourism 2007, pages 503–512. Springer Vienna.
Polycarpou, E., Lambrinos, L., and Protopapadakis, E.
(2013). Smart parking solutions for urban areas. In
IEEE 14th International Symposium on World of Wire-
less, Mobile and Multimedia Networks (WoWMoM),
pages 1–6. IEEE.
Ramezani, S. and Endriss, U. (2010). Nash social welfare
in multiagent resource allocation. In Agent-Mediated
Electronic Commerce. Designing Trading Strategies
and Mechanisms for Electronic Markets, volume 59
of LNBIP, pages 117–131. Springer.
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