=Paper=
{{Paper
|id=Vol-1867/w3
|storemode=property
|title=A Study to Promote Car-Sharing by Adopting a Reputation System in a Multi-Agent Context
|pdfUrl=https://ceur-ws.org/Vol-1867/w3.pdf
|volume=Vol-1867
|authors=Emilio Picasso,Maria Nadia Postorino,Giuseppe M.L. Sarné
|dblpUrl=https://dblp.org/rec/conf/woa/PicassoPS17
}}
==A Study to Promote Car-Sharing by Adopting a Reputation System in a Multi-Agent Context==
https://ceur-ws.org/Vol-1867/w3.pdf
13
A Study to Promote Car-Sharing by Adopting a
Reputation System in a Multi-Agent Context
Emilio Picasso Maria Nadia Postorino and Giuseppe M. L. Sarné
Universidad de Buenos Aires, Las Heras 2214, DICEAM, University Mediterranea
Buenos Aires, 1127, Argentina 89122 Reggio Calabria, Italy
Email: epicasso@uca.edu.ar Email: {npostorino, sarne}@unirc.it
Abstract—In recent years the increasing rate of vehicular so on) and some operational costs (e.g., gas, oil, service and
traffic due to private mobility caused congestion and environ- so on). The car-sharing system (from hereafter only CS) is a
mental impacts in urban contexts all over the world. To face solution able to give the same advantages of a personal car
such problems an important contribution might be given by
transit systems. However, transit systems are characterized by without the aforementioned disadvantages - mainly costs.
a discontinuous spatial and time coverage so that other forms More in detail, CS follows a “Car-As-A-Service” paradigm
of mobility, like car-sharing, can be an effective complement [17], i.e. it is a membership based service mainly designed for
to it by providing the same flexibility and comfort of private both short time and distance trips, which is usually available on
cars. Several studies confirmed that car-sharing is almost as demand (or reservation) to all qualified drivers belonging to a
highly appreciated as private cars but having the advantage of
lower costs. For such reasons, in recent years the car-sharing community [18]. Three types of CSs are commonly identified,
market increased continuously as it has been resulting more namely: (i) Peer-to-Peer (P2P): it takes place among private
and more attractive for investors, although its market share users offering their personal cars for money (it was the first
remains limited. To encourage the car-sharing philosophy, from type of CS and is receiving a new impulse from information
one hand traditional car-sharing companies are trying to reduce technologies); (ii) Business to Consumer (B2C): it is made
operational costs to offer lower fares and, from the other hand,
several individual owners are starting to share their cars suitably available by business companies with the obvious goal to
supported by technology. To promote car-sharing activities, in obtain financial benefits; (iii) Not-For-Profit (NFP): in this case
this paper a multi-agent system able to monitor car-sharing users’ local communities or social organizations manage a CS service
driving habits is proposed. In particular, agents assist users in with the main aim to incentive a sustainable urban mobility.
improving their driving, as well as in building their individual The first CS initiatives were in Zurich (Switzerland) in 1948,
reputation measures over time. Such reputation scores can be
used to allow the access to car-sharing services and personalized Montpellier (France) in 1970 and Amsterdam (Netherlands) in
fares. Experiments on real and simulated data are encouraging 1971, but only after 1980 in Europe and in the USA the first of
and show the potentiality of this proposal. positive commercial results were achieved. Nowadays, many
Index Terms—Car-Sharing, Multi-Agent System, Reputation CSs (mainly B2C), similar for several aspects, are working all
System, Business Model. over the world almost exclusively in highly populated cities
with significant congestion and parking problems, although
I. I NTRODUCTION there are some examples of CS implementations in medium
The growing of urban traffic flows due to the increasing size cities. Recently, CSs are receiving a strong impulse by im-
number of private cars, which represent the most part of the provements in information and communication technologies,
vehicles moving in urban areas, has worsened the citizens which allow specialized companies to connect potential CS
quality of life in terms of local environmental effects and users and owners of private cars desiring to rent their personal
traffic congestion [1]–[5]. To face such problems, several vehicles when unused [19]–[21].
measures have been implemented by local authorities to reduce Given its increasing relevance, many studies have been
the use of private cars usually based on (i) restrictive rules addressed to explore the main issues characterizing CSs as
and/or suitable monetary policies (for instance, by adopting (i) users’ response and habits, for instance in terms of usage
road tolls, parking fees, limited traffic zones, interchange areas frequency and effects of information technology [22]–[24] (ii)
and so on) [6]–[12] and (ii) promotion of transit in urban environmental benefits, e.g. reduction of vehicle kilometers,
areas, which represents the most suitable alternative to private accidents, emissions and fuel consumptions, increase in av-
mobility [13]–[15]. In this scenario, the main problem is that erage speed [25], [26] and (iii) cost structure and system
private cars are generally more appealing than transit in terms organization, including pricing schemes [27], [28] or the
of comfort, privacy and flexibility [16]. Indeed, transit is combined use of car sharing and public transport [29], [30].
characterized by discontinuity both in time and in space (it is Even though in recent years CS has been growing in pop-
available at a given time and at fixed stops). On the other hand, ularity among consumers and, consequently, among investors,
the ownership of a personal car requires an initial investment its market share is limited with respect to other transport
to buy it, some mandatory costs (e.g., insurance, taxes and modalities so that its impact on the overall urban mobility
14
is essentially marginal. Therefore, to increase demand for considered CS scenario and Section III presents the proposed
and offer of CS services, they should be more attractive for multi-agent architecture, while Section IV describes in detail
users and investors. To this purpose, different policies can the activities carried out by the car-agents. The reputation
be implemented and combined to support changes in users’ system is described in Section V and the results of the
habits [31] as, for instance, discouraging the use of private simulated experiments are presented in Section VI. Finally,
cars in specific urban areas, increasing the number of cars in Section VII some conclusions are presented.
available for rent [32], enlarging the urban areas covered by
CS services [33], improving the financial appeal for all the CS II. T HE P ROPOSED S CENARIO
actors by making CS services more affordable for customers Commonly, the industrial costs (C) of a CS activity are
and, at the same time, by increasing economical benefits for represented as:
investors. C = CM + CO + CP
The implementation of suitable actions addressed to act on
the financial aspects of the CS market is then crucial. To where:
analyze the context, the cost of a CS service depends on sev- • CM is the Marketing cost derived by advertising, promo-
eral factors which can be grouped in Marketing, Organization tional events and whichever activity addressed to promote
and Production costs (see below), where the last group also the CS use;
depends on customers’ behaviors in using the CS services. • CO is the Organizational cost, which mainly involves
Even though the effects of the driving features are not costs for employees, buildings, parking areas and similar
completely characterized, a general consensus exists on the features;
fact that an “aggressive” driving (e.g. speeding up, hard • CP is the Production cost due to management and use
braking and so on) has more than one negative effect [34]. of the fleet.
In particular, it has direct impacts on costs for service, and The relevance of each component is strictly related to the
affects indirectly CS productivity because vehicles should be CS business modality, i.e. P2P, B2C or NFP.
stopped for maintenance. By promoting good driving habits, Generally, the entries having the greater impact on the
some CS costs could be reduced in order to (i) offer lower Production costs are those for buying or renting the fleet,
fares to users [34] by making CS more appealing with respect the vehicle insurance, taxes and finally the costs for the fleet
to private cars and/or (ii) increase the financial profits for CS maintenance and cleaning. Costs due to the adoption of infor-
activities by supporting existing companies in their initiatives mation and communication technologies are less significant.
as well as by attracting new investors and new actors in the As previously introduced, some of such costs are related to
CS business. the habits adopted in using the CS vehicles. Reckless or
To support this, a possible approach widely exploited in inappropriate driving habits can lead to accidents or abnormal
different contexts involving real and virtual communities is consumptions of both mechanical parts and consumables and
represented by the adoption of a reputation system, often this implies higher costs due to the service and lower profits
combined with agent technologies [35]. Indeed, intelligent for the stopped time of those vehicles. Furthermore, in the
software agents can both monitor CS consumers when they case of P2P-CS, inappropriate driving behaviors can also force
are using CS services and compute their individual reputation individual owners to avoid sharing their own cars.
score. These scores can be used for different aims as, for An important opportunity for promoting CS activities is to
instance, to select users admitted to CS services, which is encourage suitable individual driving modalities. Potentially
particularly useful to encourage more individual owners to saved money could be used to reduced maintenance costs
share their personal cars, or to determine personalized fares and also for awarding, with personalized lower fares, those
awarding the better customers. At the same time, agents consumers having appropriate behaviors. Therefore, as a hoped
monitoring CS users’ driving activities can support them in result, such behaviors will permit financial benefits for all the
improving their habits. CS actors.
In this scenario, the main difficulty is the monitoring of The question is “How is it possible to realize this?” Pro-
CS customers. However, progresses made in different fields gresses made in computer science, electronic, control systems,
as computer science, electronic, control systems, signal pro- signal processing and communications help us to answer the
cessing and communications make it not a very complex question above. Indeed, vehicles can be currently equipped
task. The adoption of intelligent software agent technology with at least 120 different types of sensors and their num-
can help in monitoring users [36], simulating, controlling and ber is increasing quickly (also including those equipments
managing transportation networks at different levels of detail allowing the new form of CS-P2P [19]–[21]). Data gathered
by providing intelligent decision-making frameworks [35] as by sensors plugged into cars can also be used to analyze
well as managing trust and reputation systems [37]. drivers’ behavior. For instance, some insurance companies
In this paper, we investigate on the opportunity to support make available sensor-based equipments to be placed into cars
CS customers by adopting a distributed reputation scheme and offer lower insurance fees because of the possibility to
within a multiagent system in order to promote CS activities. examine in a semi-automatic way the insured driving behavior
In the following, Section II provides an overview of the in case of accident. In this study, we associate each vehicle
15
with an intelligent software agent (hereafter simply agent) that autonomous driving. In such a context, some future scenarios
analyzes automatically data collected by the vehicle sensors, foresee that within few decades, mainly for safety reasons,
classifies each driver based on his/her behavior and assists only autonomous driving vehicles will be legal [41] and, as a
him/her in improving his/her driving. consequence, CS and other forms of mobility, for instance taxi
Such a classification will be used to compute the drivers’ services, will be different from those that we know nowadays.
reputation, which can be defined as: “what is generally However, the current equipment is already suitable for car-
said or believed about a person’s or thing’s character or agent activities to be carried out2 .
standing” [38]–[40]. In other words, within a community the To realize the agent tasks described in Section III, a given
reputation has the meaning of a collective indirect measure CA analyzes the sensor data collected in real time and exploits
of trustworthiness deriving by referrals or ratings provided them to: (i) address the driving habits of the CS users on the
by the other community members on the basis of their past basis of its data analysis; (ii) compute an overall score for
interactions. evaluating the driving style of the current CS session.
The computed reputation scores will be used for both As for the first task, we note that some dashboards already
allowing/avoiding the access to the CS service and determining provide information to the driver mainly to optimize the
personalized CS fares, e.g. a greater or a lower discount on gasoline usage. In our proposal, we suppose that the CA is
the baseline price able to give driver information useful to optimize the use of
the vehicle under a more general point of view (e.g., gasoline,
III. T HE C AR -S HARING M ULTI - AGENT A RCHITECTURE brakes and so on) or, in other words, to optimize his/her
In this Section, we provide a short overview about the driving habits. The second task of the CA is addressed to
proposed multi-agent platform, which fits all the types of CS compute a feedback, identified as F , exploited by the Agency
(i.e., P2P, B2C and NFP). The components of this platform to update the value of the driver’s reputation measure as
are (i) a community of agents, named car-agents (CA), each specified in Section V.
one associated with a car, and (ii) their Agency. To compute the value of F 3 , let CAi be the car-agent
More in detail, for each driving session carried out on the associated with the vehicle i and let uj be a user exploiting
associated car, each car-agent provides to: that CS service. Moreover, let Fi,j S
be the feedback computed
• analyze some data coming from sensors plugged on board by the agent CAi for the user uj with respect to the service
in order to classify the driving habits of the current CS S into the domain [0, 1] ⊂ R, where 0 means the minimum
user; appreciation for the ui ’s driving habits and 1 denotes the
• support the CS user in improving his/her driving style; maximum one. Based on the information sSi,1 , · · · , sSi,n given
• compute a score (i.e., feedback) on the basis of its by the n sensors on the vehicle i, the car-agent CAi calculates
monitoring activity, which will be sent to its Agency. the feedback Fi,j S
. Different strategies and algorithms can
In a complementary way, the Agency provides to: be used for computing such a feedback and, therefore, we
describe it as the result of a function F( ) depending on the
• collect the feedbacks computed locally by the car-agents
parameters sSi,1 , · · · , sSi,n in the form:
to update the reputation score1 of each user exploiting
the CS service the Agency is managing;
S
• apply specific policies on the basis of the computed Fi,j = F(sSi,1 , · · · , sSi,n ) (1)
drivers’ reputation measures (e.g., the Agency allows or
denies the access to the CS service, determines person- Note that Eq. 1 is the kernel of the system and its correct
alized fares for the CS service and so on); definition could represent a very complex problem. For a
• make available some common services to all the CAs
low number of parameters also a simple if-then-else approach
associated with it (e.g., the agent white pages). could be applied; differently, for a high number of parameters
other computational techniques, among which fuzzy-logic or
IV. T HE C AR -AGENT ACTIVITY artificial neural networks, appear more suitable candidates for
This section describes the car-agent activities introduced in implementing F .
the previous section.
Each car-agent accesses all the data coming from the V. T HE R EPUTATION S YSTEM
sensors plugged on board of its associated vehicle that are In order to compute the drivers’ reputation scores, we
useful for its goals. Note that almost all the new cars are designed a specific reputation system, realized by the Agency,
provided with a significant number of sensors and processors which satisfies the following three properties, summarized
to analyze sensors data. The number of on board sensors in [42]:
(i.e., information sources) is expected to grow significantly,
similarly to those on vehicles that currently are equipped to test 2 Similar services are already make available by some fleet management
softwares in a centralized way.
1 Note that some corrective actions on the reputation scores could be adopted 3 Note that the function F ( ) could not be the same for all the car-agents
in presence of events that cannot be gathered in an automatic way as, for managed by Ag so that the score evaluations can not be uniform along all
instance, car body damages, interior cleaning and so on (see Section V). the multiagent system.
16
• each involved entity is time persistent, so that for each expensive CS services. For NFP-CS it is worthless
interaction an expectation of future interactions always and, therefore, in this case the value of V is set to
exists; 1.
• reputation ratings about current interactions are captured – ξj is a system parameter ranging in [0, 1] ⊂ R
and spread within the involved community; intended to give the reputation system a uniform
• reputation ratings about past interactions are used to guide metric by taking into account those characteristics
decisional processes about current interactions. of the CS services not intrinsically considered by
More in detail, let CAi be the car-agent associated with the the parameter Ci,jS
and mainly due to the adoption
vehicle i belonging to a CS company managed by the Agency of different policies by the CS companies.
and let uj be the user of the CS service S on i. When the CS • Pj is a penalization coefficient for uj ranging in [0, 1] ⊂
S
service ends, the car-agent associated with the shared vehicle R used in the case of behaviors/effects not automatically
sends to its Agency the feedback Fi,j S
for uj , computed based detectable/verifiable by the car-agents4 . For default PjS is
on the information gathered by the vehicle sensors during S. set to 1, i.e. absence of penalization for uj .
The Agency will exploit Fi,jS
to update the reputation score of On the basis of the reputation score the Agency can adopt
uj . different fare policies and even deny the possibility of using a
More formally, let Rj ∈ [0, 1] ⊂ R be the reputation of CS service to a customer having a very low reputation score.
the user uj . After the car-agent CAi has sent to the Agency
the feedback Fi,j
S
for the service S consumed by uj , then the VI. E XPERIMENTS
reputation of uj is updated as follows: In this section we present the results of two experiments ad-
dressed to verify the effectiveness of the approach previously
Rjnew = (α · σi,j
S
+ (1 − α) · Rjold ) · PjS (2) discussed.
The first experiment is addressed to verify if the system
where: can compute a reasonable feedback. To this purpose, the
• α is a system parameter ranging in [0, 1] ⊂ R and ruling OpenXC repository [43] was exploited. It consists of a
the behavior of the reputation system. More in detail, it number of anonymous tuple, i.e. the data do not permit
weights the relevance of the parameter σi,j S
(see below), their association with drivers, referred to different scenarios
which takes into account the feedback Fi,j S
, in updating and driving habits. More in detail, each tupla is made as
the reputation of uj (i.e., Rj ). In other words, the higher {”name”:”string”, ”value”:integer, ”timestamp”:time}, where
its value, the lower the sensitivity of the reputation at the first pair identifies the type of information, the second
quick changes and vice versa. pair gives its value and the last pair returns a progressive
• σi,j is the contribution to the reputation due to S, which
S
time. As an example, see the tupla above:
also takes into account the feedback Fi,j S
. More formally,
σi,j is computed as:
S
{ ”name”:”accelerator pedal position”,
”value”:2,
S
σi,j S
= Fi,j S
· Vi,j · ξi (3) ”timestamp”:1361454211.483000 }
where: To solve the problem given by the anonymity of the
– Fi,j
S
is the feedback computed and sent by CAi to OpenXC data, we generated 100 driver’ profiles belonging to
the Agency. five driver styles (respectively named very soft, soft, neutral,
– Vi,j
S
is a parameter belonging to [0, 1] ⊂ R and aggressive and very aggressive). Then for each simulated
referred to the monetary cost C(S) of the service driver, 10 driving tracks have been built by suitably assembling
S (Eq. 4) computed as: the OpenXC data for a global number of 1000 driving tracks.
More in detail, each driving category has 20 simulated
1 S
if Ci,j = CM ax drivers. Each category is characterized by a different driving
habit in terms of aggressive driving data (e.g., hard accel-
S
Vi,j = S (4) eration, hard braking and so on) included into the tracks
Ci,j
otherwise data (see Table I) and randomly assigned to each simulated
CM ax
driver. Therefore, for a specific simulated driver the associated
where CM ax (S) is a system threshold representing driving tracks consist of suitable sequences5 of homogeneous
the maximum cost for a CS service after which Vi,j S
(e.g., “aggressive” or “not aggressive”) OpenXC tuple match-
is considered satured. Therefore, the lower the cost, ing the assigned profile. Finally, given the adopted method
the lower the effect on the value of the feedback
4 Note that the evaluation of damages as accidents, damages, interior
given by the service S. This is a countermeasure
introduced to reduce the weight of positive reputation cleaning, penalties and so on, currently have to be necessarily processed by
humans.
for marginal CS services and then avoid misleading 5 Each sequence consists of 8 tuple and each driving track is different for
behavior addressed to consume such reputation for number of tuple, i.e. time length.
17
Driving Category aggressive/non aggressive ratio drivers’ nature and the involved scenario. When the simulation
very sof t 1:0 (i.e., only not aggressive actions)
sof t from 4:1 to 2:1 starts, all the drivers receive an initial reputation score of 0.5
neutral 1:1 and for each epoch only 20% of the overall number of drivers
aggresive from 1:4 to 1:2 is randomly selected. Clearly, the higher is the percentage of
very aggresive 0:1 (i.e., only aggressive actions)
drivers correctly identified, the higher is the accuracy of the
TABLE I
T HE ADOPTED AGGRESSIVE / NON AGGRESSIVE DRIVING ACTIONS RATIO proposed reputation system.
Both scenarios provided satisfactory results (depicted in
Figure 1). In particular, line A shows that 90% of the drivers
nature is correctly recognized after less than 80 epochs.
to assemble the driving tracks, a timestamps harmonization Note that initially all the normal drivers are recognized and,
procedure needed. However, this procedure did not affect the although it is due to the assigned initial reputation score of 0.5,
experimental results in any way. this result does not change along all the simulation. Some tests
The computation of the feedback F for a driving track (i.e., carried out by adopting a different initial reputation score (e.g.,
a driver) is computed on the basis of the ratio between the 0.75) led to a similar result. The proposed reputation system
driving time (in seconds) and the overall number of aggressive works well also in presence of drivers’ oscillatory behavior
actions in that time. In order to identify an aggressive action and 90% of the aggressive drivers have been recognized (line
an Artificial Neural Network (ANN) [44] has been adopted, B) after less than 140 epochs also under these particular
a tool able to deal with problems denoted by uncertainty and conditions.
frequently used in trasportation research [45], [46] The ANN
has been set up after preliminary tests that identified the opti-
mal pattern structure and the ANN architecture, topology and
learning strategy. More in detail, for the ANN training set we
used the data coming from the 30% of the generated driving
tracks arranged in patterns. Each pattern contains as input 9
data deriving by three tuple 6 and a unique real value ranging
in [0, 1] as output data, where 0/1 means minimum/maximum
aggressiveness degree. In particular, each tupla consists of an
integer number coding the attribute ”name”, the associated
value and the time interval occurring with the previous tupla 7 .
Fig. 1. Percentage of drivers correctly identified. Scenarios A and B.
The ANN model and learning algorithm we identified as
the most profitable solutions, are a three-layer ANN trained
by a back-propagation (BP) algorithm [44], having 9, 120 VII. C ONCLUSIONS
and 1 nodes for the input, hidden and output layers and
CS can play an important role to support public and private
hyperbolic and sigmoid activation functions for the neurones
mobility and contribute to reduce traffic and environmental
of the hidden and output layers. The above described trained
problems affecting urban contexts. To this purpose, in this
ANN recognized aggressive driver actions on the remaining
paper we investigated about the possibility of improving
driving tracks with an accuracy of over 79%, which can be
convenience and profits for CS users and CS suppliers, re-
considered a satisfactory preliminary result given the nature
spectively.
of the exploited dataset.
To address these issues we propose the adoption of a
The second experiment is addressed to test the effectiveness
reputation system implemented by intelligent software agents
of the proposed reputation system. To this purpose, 1000
and tested by performing some experiments based on real and
simulated drivers have randomly associated with the driver
simulated data. The aim is to identify good driving behaviors
typologies presented in Table I. Two different scenarios were
to reduce CS fees, and vice versa, thus making the system
considered: the first scenario (named A) assumes a uniform
more attractive for both CS users and CS suppliers. The first
drivers’ behavior along all the simulation, while the second
experiment exploited real vehicular sensor data to identify the
one (named B) is addressed to test the robustness of the repu-
driving users’ habits; the second one, based on simulated data,
tation system as regards to oscillatory behaviors by assuming
verified the effectiveness of the proposed reputation system for
that aggressive drivers try to build a positive reputation on
two scenarios. The results of these preliminary experiments
cheap CS services for consuming it on expensive CS services
encourage future researches for further developments of this
(25% of the CS services was assumed to be expensive). In the
proposal.
simulations, the reputation system parameters α, ξ and P were
respectively set to 0.15, 1 and 1, while the feedback F and ACKNOWLEDGMENT
the parameter V were randomly generated coherently with the
This work has been developed within by the Networks and
6 The target tupla and those referred to the previous and following actions. Complex Systems (NeCS) Laboratory - Department DICEAM
7 Note as the first tupla of each driving track is not considered. - University Mediterranea of Reggio Calabria.
18
R EFERENCES [26] J. Firnkorn and M. Müller, “What will be the environmental effects
of new free-floating car-sharing systems? the case of car2go in ulm,”
Ecological Economics, vol. 70, no. 8, pp. 1519–1528, 2011.
[1] C. A. M. Toledo, “Congestion indicators and congestion impacts: a
[27] F. Ciari, M. Balac, and M. Balmer, “Modelling the effect of different
study on the relevance of area-wide indicators,” Procedia-Social and
pricing schemes on free-floating carsharing travel demand: a test case
Behavioral Sciences, vol. 16, pp. 781–791, 2011.
for zurich, switzerland,” Transportation, vol. 42, no. 3, pp. 413–433,
[2] L. Chen and H. Yang, “Managing congestion and emissions in road 2015.
networks with tolls and rebates,” Transportation Research Part B: [28] M. Duncan, “The cost saving potential of carsharing in a us context,”
Methodological, vol. 46, no. 8, pp. 933–948, 2012. Transportation, vol. 38, no. 2, pp. 363–382, 2011.
[3] E. Cascetta and M. N. Postorino, “Fixed point approaches to the [29] S. A. Shaheen and A. P. Cohen, “Carsharing and personal vehicle
estimation of o/d matrices using traffic counts on congested networks,” services: worldwide market developments and emerging trends,” Inter-
Transportation science, vol. 35, no. 2, pp. 134–147, 2001. national Journal of Sustainable Transportation, vol. 7, no. 1, pp. 5–34,
[4] A. Spickermann, V. Grienitz, and A. Heiko, “Heading towards a 2013.
multimodal city of the future?: Multi-stakeholder scenarios for urban [30] J. Schuppan, S. Kettner, A. Delatte, and O. Schwedes, “Urban mul-
mobility,” Technological Forecasting and Social Change, vol. 89, pp. timodal travel behaviour: Towards mobility without a private car,”
201–221, 2014. Transportation Research Procedia, vol. 4, pp. 553–556, 2014.
[5] T. Fontes, S. Pereira, P. Fernandes, J. Bandeira, and M. Coelho, “How [31] J. Kopp, R. Gerike, and K. W. Axhausen, “Do sharing people behave
to combine different microsimulation tools to assess the environmental differently? an empirical evaluation of the distinctive mobility patterns
impacts of road traffic? lessons and directions,” Transportation Research of free-floating car-sharing members,” Transportation, vol. 42, no. 3, pp.
Part D: Transport and Environment, vol. 34, pp. 293–306, 2015. 449–469, 2015.
[6] M. Postorino, G. Musolino, and P. Velonà, “Evaluation of o/d trip ma- [32] M. Nourinejad and M. J. Roorda, “Carsharing operations policies: a
trices by traffic counts in transit systems,” in Schedule-Based Dynamic comparison between one-way and two-way systems,” Transportation,
Transit Modeling: theory and applications. Springer, 2004, pp. 197– vol. 42, no. 3, pp. 497–518, 2015.
216. [33] K. Uesugi, N. Mukai, and T. Watanabe, “Optimization of vehicle
[7] S. Ison and T. Rye, “Implementing road user charging: the lessons learnt assignment for car sharing system,” in International Conference on
from hong kong, cambridge and central london,” Transport Reviews, Knowledge-Based and Intelligent Information and Engineering Systems.
vol. 25, no. 4, pp. 451–465, 2005. Springer, 2007, pp. 1105–1111.
[8] N. Paulley, R. Balcombe, R. Mackett, H. Titheridge, J. Preston, M. Ward- [34] Z. F. Quek and E. Ng, “Driver identification by driving style,” Technical
man, J. Shires, and P. White, “The demand for public transport: The report, technical report in CS 229 Project, Stanford university, Tech.
effects of fares, quality of service, income and car ownership,” Transport Rep., 2013.
Policy, vol. 13, no. 4, pp. 295–306, 2006. [35] M. N. Postorino and G. M. L. Sarné, “Agents meet traffic simulation,
[9] S. Liu, K. P. Triantis, and S. Sarangi, “A framework for evaluating control and management: A review of selected recent contributions,”
the dynamic impacts of a congestion pricing policy for a transportation in Proceedings of the 17th Workshop “from Objects to Agents”, WOA
socioeconomic system,” Transportation Research Part A: Policy and 2016, ser. CEUR Workshop Proceedings, vol. 1664. CEUR-WS.org,
Practice, vol. 44, no. 8, pp. 596–608, 2010. 2016.
[10] M. N. Postorino, “A comparative analysis of different specifications of [36] D. Rosaci and G. M. L. Sarné, “Multi-agent technology and ontologies
modal choice models in an urban area,” European journal of operational to support personalization in b2c e-commerce,” Electronic Commerce
research, vol. 71, no. 2, pp. 288–302, 1993. Research and Applications, vol. 13, no. 1, pp. 13–23, 2014.
[11] A. de Palma and R. Lindsey, “Traffic congestion pricing methodologies [37] J. Granatyr, V. Botelho, O. R. Lessing, E. E. Scalabrin, J.-P. Barthès,
and technologies,” Transportation Research Part C: Emerging Technolo- and F. Enembreck, “Trust and reputation models for multiagent systems,”
gies, vol. 19, no. 6, pp. 1377–1399, 2011. ACM Computing Surveys (CSUR), vol. 48, no. 2, p. 27, 2015.
[12] M. Gibson and M. Carnovale, “The effects of road pricing on driver [38] A. Jøsang, R. Ismail, and C. Boyd, “A survey of trust and reputation
behavior and air pollution,” Journal of Urban Economics, vol. 89, pp. systems for online service provision,” Decision support systems, vol. 43,
62–73, 2015. no. 2, pp. 618–644, 2007.
[13] J. D. Harford, “Congestion, pollution, and benefit-to-cost ratios of us [39] D. Rosaci, G. M. L. Sarné, and S. Garruzzo, “TRR: An integrated
public transit systems,” Transportation Research Part D: Transport and reliability-reputation model for agent societies,” in Proceedings of the
Environment, vol. 11, no. 1, pp. 45–58, 2006. 12th Workshop from “Objects to Agents”, WOA 2014, ser. CEUR
Workshop Proceedings, vol. 741. CEUR-WS.org, 2011.
[14] M. Postorino and V. Fedele, “The analytic hierarchy process to evaluate
[40] M. N. Postorino and G. M. L. Sarné, “An agent-based sensor grid
the quality of service in transit systems,” WIT Transactions on The Built
to monitor urban traffic,” in Proceedings of the 15th Workshop from
Environment, vol. 89, 2006.
“Objects to Agents”, WOA 2014, ser. CEUR Workshop Proceedings,
[15] C. Winston and V. Maheshri, “On the social desirability of urban rail vol. 1260. CEUR-WS.org, 2014.
transit systems,” Journal of urban economics, vol. 62, no. 2, pp. 362– [41] D. Newman, “D. Newman, ”Autonomous
382, 2007. Cars: The Future Of Mobility”, available at
[16] B. Caulfield, “An examination of the factors that impact upon multiple http://www.forbes.com/sites/danielnewman/2016/09/27/autonomous-
vehicle ownership: The case of dublin, ireland,” Transport Policy, cars-the-future-of-mobility/#6d7ca743514a,” 2017.
vol. 19, no. 1, pp. 132–138, 2012. [42] P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman, “Reputation
[17] J. Agyeman, D. McLaren, and A. Schaefer-Borrego, “Sharing cities,” systems,” Communications of the ACM, vol. 43, no. 12, pp. 45–48, 2000.
Friends of the Earth Briefing, pp. 1–32, 2013. [43] Openxcplatform. (2017) http://openxcplatform.com/resources/traces.html.
[18] C. Sinclair, “Codes of ethics and standards of practice.” 1993, available [44] S. Haykin, Neural Networks - A Comprensive Foundation. New York:
at www.carsharing.org. Macmillan College Publishing Company, 2000.
[19] Car2Share, “https://www.car2share.com,” 2017. [45] M. N. Postorino and G. M. L. Sarné, “A neural network hybrid
[20] Drife, “http://dryfe.it,” 2017. recommender system,” in Proceedings of the 2011 conference on neural
[21] Getaround, “https://www.getaround.com/san-francisco,” 2017. Nets WIRN’10, 2011, pp. 180–187.
[22] P. Baltusis, “On board vehicle diagnostics,” SAE, Tech. Rep., 2004. [46] M. N. Postorino and M. Versaci, “A neuro-fuzzy approach to simulate
[23] K. M. N. Habib, C. Morency, M. T. Islam, and V. Grasset, “Modelling the user mode choice behaviour in a travel decision framework,”
users behaviour of a carsharing program: Application of a joint hazard International Journal of Modelling and Simulation, vol. 28, no. 1, pp.
and zero inflated dynamic ordered probability model,” Transportation 64–71, 2008.
research part A: policy and practice, vol. 46, no. 2, pp. 241–254, 2012.
[24] Telematics, “http://www..com/board-diagnostics-future-vehicle-
analysis,” 2017.
[25] N. Fellows and D. Pitfield, “An economic and operational evaluation
of urban car-sharing,” Transportation Research Part D: Transport and
Environment, vol. 5, no. 1, pp. 1–10, 2000.