=Paper=
{{Paper
|id=Vol-2404/paper10
|storemode=property
|title=A Preliminary Study for an Agent Blockchain-Based Framework Supporting Dynamic Car-Pooling
|pdfUrl=https://ceur-ws.org/Vol-2404/paper10.pdf
|volume=Vol-2404
|authors=Maria Nadia Postorino,Giuseppe M.L. Sarné
|dblpUrl=https://dblp.org/rec/conf/woa/PostorinoS19
}}
==A Preliminary Study for an Agent Blockchain-Based Framework Supporting Dynamic Car-Pooling==
https://ceur-ws.org/Vol-2404/paper10.pdf
Workshop "From Objects to Agents" (WOA 2019)
A Preliminary Study for an Agent Blockchain-Based
Framework Supporting Dynamic Car-Pooling
Maria Nadia Postorino∗ , Giuseppe M. L. Sarné‡
∗ Department DICAM, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
‡ Department DICEAM, University of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Cal., Italy, sarne@unirc.it
Abstract—In the last decades, private cars caused an increasing alternative to their ownership. The SE has gained relevance
growth of urban traffic flows all over the world with a consequent in recent years thanks to i) advancements in technological
increase of environmental pollution and road congestion. In areas (e.g., computer science, communication, electronics and
this context, Car-Pooling is an alternative car-based solution
for private mobility that optimizes the car loading factor with so on), which realize new ways to match demand and offer in
respect to the number of passengers, although it requires that real time [20] and ii) cultural changes in people’s habits [21]
all the participants share trip origin and destination at the same included an increasing environmental awareness.
time. To make the system more appealing, an on-demand service In this respect, a general consensus there is on CP as
adopting variable fares on the basis of trip length and number an efficient car-based solution for private mobility, which
of participants is proposed in this paper. Multi-agent, reputation
and blockchain technologies are used and a suitable dynamic may optimize the car use with respect to the number of
routing algorithm has been developed. Experiments on simulated passengers [22]. Unfortunately, the common CP model lacks
data prove the potentiality of this approach. flexibility because it is generally applied among privates and
Index Terms—Car-Pooling, Multi-Agent System, Reputation for trips made on a regular basis. In other words, it requires
System. that participants share the same time and the same origin and
destination for the outward and (usually) for the return jour-
I. I NTRODUCTION
neys. For such a reason, CP participants generally share also
Nowadays, most part of urban traffic flows all over the other characteristics like belonging to the same community,
world is due to private cars. In the last decades, they caused working in the same place, living in the same neighboring and
increasing costs for congestion, fuel consumption, travel time so on. Currently, some Web-platforms mediate among demand
and health among the others [1]–[6], in a context where the and offer to reach an audience as larger as possible at low
majority of the vehicles move only their drivers. costs [19]. Such platforms enlarged the basis of potential CP
To reduce the use of private cars, a wide range of strategies users and have reached a fair popularity (also thanks to addi-
has been developed by local, national and overnational (e.g., tional services like reputation systems, insurance, clear fares
the European Union) authorities. Most part of the actions and so on) [23], [24] but have a low flexibility with respect to
adopted inside urban areas rely on policies for (i) discouraging the trip origin and/or destination of the potential participants.
the use of private cars by introducing restrictions as well as Indeed, their main aim is to facilitate an agreement among
increasing their operational costs [7]–[13] and (ii) improving unknown participants for a CP journey.
and promoting transit [14]–[16]. However, people continue to To give more flexibility in time and space to the traditional
perceive private cars as more comfortable and flexible than CP, dynamic issues have been introduced recently [25], [26],
transit thanks to the absence of constraints on time and space which try to match in real-time the mobility demand with the
(e.g., fixed stops and timetables) that, conversely, characterize vehicles potentially able to satisfy it on-the-fly, if the new
transit [17]. destination is compatible with those of the other passengers
In the scenario above described, alternative forms of mobil- within prefixed time thresholds. Obviously, the other passen-
ity still based on the use of cars are car-sharing (CS) and car- gers accept a priori to adapt their route for a lower trip fare.
pooling (CP) systems. The CS is a Pay-As-You-Use modality In other words, when a ridesharing request is compatible with
mainly adopted for short trips in urban areas, while in the those of the other passengers, then the road path is modified to
CP two or more users that make frequently the same journey, pick and drop the new passenger, like a shared taxi. Obviously,
at compatible times, voluntarily agree to share a private car demand and offer must be processed in an automatic way as
and the traveling costs. The business models of these forms of quickly as possible to provide a real-time service.
mobility belong to the “Sharing Economy” (SE) [18], [19], This study wants to contribute to this issue by proposing
which promotes the temporary acquisition and shared usage a framework based on software agents, reputation system and
of goods, services or knowledge as a possible and effective blockchain [27] technologies. More in detail, each user is sup-
ported by a personal agent associated with his/her smartphone
Maria Nadia Postorino, previously at Department DICEAM, University
of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Cal., Italy, npos- (with suitable computation, storage, communication and GPS
torino@unirc.it capabilities). These agents allow to identify and localize their
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Workshop "From Objects to Agents" (WOA 2019)
users by exploiting the smartphone GPS. This information is active on F at a certain time (see below); ii) manages and
periodically sent to the Agency that, by means of a dynamic updates a reputation system based on the users’ feedback
routing algorithm, matches demand and offer for rides by about the travel mates; iii) collects all the P As requests
taking into account, positions, seat availability and temporal for rides. The Ag uses these information to search the
constraints in terms of both departure and arrival times of all best opportunities for rides that, suitably ordered, are sent
the participants. to the P As requiring them. Note that communication
For each ridesharing request coming from a personal agent, among P As should occur via Ag by using a suitable
the Agency will return a list of the existing ride opportunities tool. When a ride request/offer is accepted by the parts
to that agent. From this list, the personal agent will select then commitment and payments (included compensations
those rides considered as the most suitable for its user. Note due to the dynamic nature of this CP service) are carried
that over time user’s choices provide information about his/her out via a smart-contract running on the permissioned
preferences to its agent, which could allow a higher level blockchain platform.
of customization to be realized. After a ride is selected, Similarly, the tasks carried out by a P A are:
and confirmed by the driver, a smart-contract [28] runs over
• Affiliation. In F each P A needs to be affiliated with Ag.
a blockchain (like Ethereum [29]), to make the agreement
• Activation. Whenever a P A enters (i.e., it is active) on
public, irrevocable and for realizing safe payments in an
F then, periodically, it sends its GPS coordinates to Ag.
automatic way by using a cryptocurrency (e.g., Ether).
• Search. To search a ride, a P A sends to Ag time and
For each trip, it is also assumed a dynamic fare which de- position of both origin and destination point of its trip.
pends both on how much long is the trip and how many riders Ag will answer with a list of opportunities, then the
share that trip because the higher the number of participants P A will order this list, on the basis of the preferences
to a ride is, the lower the comfort is as well as the cost for of its user, and will submit it to him/her for his/her
passenger. It implies that in a dynamic CP, where the number choice (however, P A could perform this task also in an
of passengers can change along the trip if a new rider is added, autonomous way [31]).
the other participants receive a monetary compensation for • Commitment. After the choice of a ride, the P As of both
both the loss of comfort and the decreased cost for passenger. driver and new passenger make public and irrevocable
This procedure is automatically carried out each time that a their agreement by means of a smart-contract which runs
smart-contract for that ride runs on the blockchain. on a permissioned blockchain and also provides monetary
The paper is organized as follows. Section II presents payments and compensations with a cryptocurrency.
the proposed agent framework, while Section III describes • Feedback. At the end of a ride each P A sends a feedback
the main characteristics of the dynamic routing algorithm. f ∈ [0, 1] ∈ R (provided by its user) to Ag; the greater
The reputation system is described in Section IV, while the f is, the greater its appreciation about the ride is.
results of a campaign of simulations are show in Section V.
Section VI gives an overview on the related work and, finally, Finally, the third component of F is a permissioned
in Section VII some conclusions are presented. blockchain which allows to trust anonymous and unknown
actors and warranties data integrity and payments without
II. T HE M ULTI - AGENT F RAMEWORK exploiting other centralized third parties [32]. More in detail,
each time that two users (i.e., a driver and a new potential
This section describes a multi-agent framework (F ) able passenger) agree for a ride, then a smart-contract starts on
to support a dynamic CP. The framework consists of three the adopted blockchain platform by using a permissioned
components: approach. The smart-contract makes this choice irrevocable,
• the Agency (Ag), which is a trusted and safe centralized publicly and realizes all the contractual obligations, payment
component, unique in F , that coordinates the CP service and possible compensations to the included travel mates, in
and collaborates with the other agents. an automatic way. To this aim, the information about the P A
• A Personal Agent (P A), which supports the CP activities positions provided by Ag is also exploited. The costs due to
of a user and runs on his/her personal device (i.e., smart- the blockchain management are assumed to be included in the
phone) equipped with suitable computational, storing and CP fare. Note that this mechanism is not linked to a specific
communication capabilities as well as with a pair of blockchain protocol1 .
cryptographic keys [30] (for authentication and privacy
issues). A P A is free of entering/leaving F at any time. III. T HE DYNAMIC ROUTING A LGORITHM
• A permissioned blockchain.
In this Section the algorithm for the dynamic route research
Briefly, the tasks carried out by Ag are: running on the Agency is introduced. It is a variant of the
• Affiliation. Ag manages the affiliation of each P A with Alpha-Beta Pruning algorithm [33] driven in the in-depth
F and provides each P A with an identifier, an initial
1 For sake of simplicity, in this preliminary phase we refer to the well
reputation score, and a pair of cryptographic keys.
known Ethereum blockchain platform for the advantages deriving by both
• Support. Ag manages its identity and all the CP service the availability of documented API with the opportunity of adopting its
tasks. In detail, Ag: i) collects the position of all the P As cryptocurrency (i.e, Ether) and wallet service.
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Workshop "From Objects to Agents" (WOA 2019)
BF DFS M αP A∗ G DR Let N be a list of nodes
Time complexity bd bd bm bm bd bd d ÷ bm
Space complexity bd bd bm bm bd bd bm
heuristic boundary nodes X X X
T Exit by providing the complete
heuristic search X X X Is N empty? solution (if it has been found),
heuristic B&B X otherwise the partial one
optimal solution X X X X X
F
complete search X X X X X
partial solution X X X Split N in the subsets N1 and N2. N1 stores
the nodes that do not generate cycles --ordered
global path minimization X X according to the heuristic f1 (n) = d (n) + h ’(n) --
tree cuts X X X and N2 stores the nodes that generate cycles
TABLE I Extract the first node n from the list N1,
which becomes the new current state,
C OMPARISON WITH B RUTE F ORCE (BF); D EPTH F IRST S EARCH and remove it from the list itself
B RANCH &B OUND (DFS); M INIMUM (M); αP RUNING (αP); A+ ;
DYNAMIC ROUTING (DR) - ( B - BRANCHING FACTOR , M - MAX DEPTH ).
T Update the current best route
Is n a goal node?
if the new solution is better
F
search by suitable heuristics capable to improve the cut-off
technique and to evaluate boundary nodes (if the chosen depth End the search
on this branch
Is
horizon does not allow to reach the optimal solution). (pruning)
f2 (n) = g (n) + h ’(n)
> cost of the
T current
The main features of this algorithm (see Table I) are: best-line?
• if the solution is contained into the fixed search depth Propagate to the
parent node the
F
(horizon) then the optimal solution is provided, otherwise value of the border
node, given by the
heuristic
the more promising one is provided. At each node, the f2 (n) = g (n) + h ’(n) Is the max
depth of search
algorithm optimizes a function (e.g., cost, number of T reached?
passengers), positive and depending on one or more F
Propagate to the parent
parameters; node the best f2 (n)
value received from its
successors (from the
• if the search ends in advance (e.g., for time or resource leaves towards the root)
Is N empty?
limits) the temporary optimal solution is provided; F
• If cycles are allowed, the nodes generating them are T
examined last, otherwise discharged if the solution cannot
Fig. 1. The Dynamic Routing Algorithm in an iterative version.
admit cycles;
• when boundary conditions change, then the optimal route
is recomputed; containing cycles, but these paths are examined last because
• heuristic functions are adopted to estimate partial solu- the priority in expanding nodes is given to those nodes not
tions, optimize the Branch & Bound and drive the in- present in the current path. Moreover, if it is impossible to
depth search (the order of optimal expansion of nodes is find the optimal solution with respect the initial constraints
chosen based on the closeness of nodes to the optimal (e.g., search time), the best solution found at the set depth
solution); search is provided. Note that the fixed search depth does not
• any list of expanded nodes for the Depth-First-Search affect the algorithm performance if it is greater than the depth
(DFS) or for a temporary tree is stored. where the optimal solution is.
In Figure 1, the pseudo-code (in form of flow-chart) of the
IV. T HE R EPUTATION M ODEL
algorithm is depicted. For sake of clarity, it is represented in
an iterative form even if the algorithm is recursive. Note that The proposed reputation model considers the whole past
the variables are appropriately initialized, the functions and history of each CP actor on the basis of the feedback assigned
the adopted heuristic are known and the term “root” refers to to him/her by his/her counterparts [34]–[36]. Note that each
the node from which the search starts. user has an initial reputation, set to 0.5, to contrast whitewash-
When an intermediate node to be served is added, as in ing strategies [37] without penalizing too much the newcomers
the dynamic CP, the algorithm starts a new search to find a with a low initial reputation value [38].
new path if the request is compatible with existing constraints In particular, when a ride ends, each user entrusts his/her
(e.g., time and reputation), otherwise the request is rejected. appreciation in terms of feedback (f ∈ [0, 1] ⊂ R) to his/her
The algorithm solves a route problem linking more nodes P A that, in turn, sends f to Ag which manages the reputation
by considering (without loss of generality) the problem of system. When a feedback fx,y , released by the user ux about
searching (on the basis of the adopted heuristic) the best path the user uy , is received
h by the Ag, this latteri computes the
between two consecutive nodes as independent from all the parameter Qx,y = b
Px,y + rx,y · ax · fx,y /2, where: i)
other paths linking the other nodes belonging to the route P is the final cost of the ride; ii) rx,y is the number of past
(i.e., local solution). Even though in the CP problem the rides between ux and uy ; iii) ax is the accuracy of ux in
cycles should be avoided, the algorithm can also find solutions providing a feedback; iv) fx,y is the feedback given by ux
67
Workshop "From Objects to Agents" (WOA 2019)
about uy . To hinder malicious behaviors, Ag will update the 1.0
case A
current reputation score of uy (i.e., Ryold ) as Rynew = α · case B
% identified users
0.8
Ryold + (1 − α) · Qj,i only if Qj,i > 0 ∨ Ryold ≥ 0.5 is true,
0.6
conversely Ry will not be updated (e.g., Rynew = Ryold ). The
parameter α ∈ [0, 1] ⊂ R gives more or less relevance to Ryold 0.4
with respect to the new contribution Qx,y .
0.2
To avoid alternate behaviors, the parameter Pb takes into
account the cost P of the ride, where PM ax is the maxi- 0
0 5 10 15 20 25
mum cost threshold, such that Pb = M in(1, P/PM ax ). The epochs
parameter r is effective against collusive behaviors occurring
among two or more users that frequently exchange positive Fig. 2. Percentage of users correctly identified. Cases A and B.
feedback to increase maliciously their reputation scores. To
this aim, let Tx,y be a parameter depending on the time
occurring between two consecutive interactions between ux the users’ nature very quickly (e.g., 90% of users is correctly
and uy evaluated positively, the variable rx,y is computed as classified in less than 11 and 18 epochs for the cases A
and B, respectively). With respect to similar systems, this is
rx,y = 1 (e(1−Tx,y ) ) if fx,y ≥ 0.5 ∧ Tx,y > 1 , otherwise
rx,y = 1. To compute Tx,y , let tl and tp be the timestamps of also due to the fact that each user receives more feedback
the last two positive feedback and let ∆T be a time threshold. for the same ride, depending on the (random) number of
At the first positive feedback Tx,y = 1 while, for each further participants to the ride. Note that, all the “neutral” users’
positive feedback, i) if (|tl − tp | < ∆t then Tx,y = Tx,y + 1, behavior, receiving an initial reputation set to 0.5, are correctly
otherwise ii) Tx,y = M ax[1, Tx,y − ⌊|tl − tp |/∆t⌋]. recognized from the first to the last epoch. However, also by
The accuracy degree of ux in providing a correct feedback is setting a different value for the initial reputation, these users
taken into account by the parameter ax ∈ [0, 1] ∈ R where 1/0 are quickly recognized like the other.
stands for maximum/minimum accuracy. More specifically, ax The second experiment verified the performance of the
is computed as axnew = β · axold + (1 − β) · 1 − |fx,y − Ry | , dynamic routing algorithm with different graphs (even large
where the parameter beta ∈ [0, 1] ⊂ R weights aold with ones) generated randomly by adopting on the parameters com-
x
respect to the new contribution given by the difference between patible with the considered scenario. For each new admissible
fx,y and the reputation of the target user (i.e., Ry ). request the route is modified by rejecting the requests incom-
patible with both pre-existing time constraints and minimum
V. E XPERIMENTS reputation and by choosing among: i) minimizing the cost; ii)
maximizing the number of served users; ii) maximizing the
This section presents the results of two experiments testing
Users/Cost ratio. The computing time is always negligible,
the reputation system and the dynamic routing algorithm.
some milliseconds. From an operational point of view, note
To test the reputation system, a population of 1000 users
that when the new passengers are collected at a node, the
randomly distributed on 5 behaviors (e.g., very unpleasant, un-
requests on the previous node is reset to avoid the insertion
pleasant, neutral, pleasant, very pleasant), has been simulated
of loops between the two nodes.
for unvarying (case A) and alternate2 (case B) modalities.
In the simulations, the reputation system parameters α and VI. R ELATED W ORK
β have been set both to 0.2 and Pmax has been set to In recent years, Intelligent Transport Systems (ITSs) re-
5e. The number of ride mates and cost of the ride have ceived a great impulse from advancements occurred in com-
been randomly generated respectively in the ranges 2 ÷ 5
and [1.00e, 7.50e]. Moreover, in the case B, the cost of the
CP services has been set > 5e for the 25% of the rides.
The simulation has been arranged for epochs, so that the
reputation parameters tl and tp have been set to the respective
epochs and ∆t has been set to 3 epochs. Each feedback has
been randomly generated coherently with the user’s behavior.
All the users received an initial reputation score of 0.5 and
for each epoch only a population share of 20% has been
randomly selected. Obviously, the higher the percentage of
users correctly identified, the higher the accuracy of this
reputation system.
Figure 2 shows the results obtained for the two cases A and
B on the basis of the user’s behavior correctly recognized.
A common aspect is represented by the ability to recognize
2 With respect to the ride cost. Fig. 3. Random network 29 Nodes, 51 Links, path from 13 to 7, depth 6.
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Workshop "From Objects to Agents" (WOA 2019)
sible. Since the blochchain is replicated on more independent
hosts, it cannot be easily controlled, tampered or deleted [59].
Note that blockchain performance are significantly affected
by the adopted consensus protocol in terms of computational
complexity, robustness, latency, scalability and safety. Behind
the cryptocurrencies (like Bitcoin [27]) some blockchains can
realize smart-contracts [28], i.e. computerized transactions that
realize the terms of a contract. For instance. Ethereum [29]
was the first blockchain for smart-contracts but, nowadays,
other similar platforms exist (e.g., Ripple [60], Stellar [61]
and Tendermint [62]) and, like Ethereum, most of them has
their own digital coin (like Ether). This class of blockchains
appears the most suitable for developing the sharing economy
Fig. 4. Random Network 500 Nodes, 306 Links, path from 119 to 361, depth business.
10.
VII. C ONCLUSIONS
puter science, electronic and communication above all. In this CP can contribute to support public and private mobility by
context, an increasing attention is given to the adoption of reducing urban traffic and its environmental problems. To this
the intelligent software agent technologies [39]–[42] thanks aim, in this paper a dynamic form of CP potentially able to
to their learning and adaptive capabilities [43], the attitude to enlarge the number of CP users has been presented.
cooperate by sharing their These issues have been addressed by exploiting multi-
knowledge [44]–[47] and to deal agent systems, reputation systems and blockchain technologies
with large, uncertain and or dynamic systems in a centralized
or distributed way. and tested by realizing some experiments on simulated data.
The first experiment verified the capability of the proposed
Also trust and reputation systems are adopted in the ITS
algorithm to manage the dynamic routing, while the second
area, mainly because in larger and larger dynamics environ-
one, based on simulated data, verified the effectiveness of the
ments information about counterparts is often necessary [48],
proposed reputation system for two scenarios. The results of
[49] to evaluate the trustworthiness of potential counterparts.
these preliminary experiments encourage further developments
Note that to assure integrity of both trust/reputations and
of this form of dynamic CP.
identities, also cryptographic techniques are often exploited.
In the CP scenario, reputation systems are worthy to be ACKNOWLEDGMENT
implemented [50], [51] and several models exist. For instance,
in [52] a reputation system is used to refuse undesirable pas- This work has been developed at the Networks and Complex
sengers to avoid having unpleasant rides. Similarly, the authors Systems (NeCS) Laboratory - Department of Engineering
of [53] and [54] propose to implement reputation systems, Civil, Energy, Environment and Materials (DICEAM) - Uni-
respectively named Smart Rider Seeker and SmartShare, that versity Mediterranea of Reggio Calabria.
allow drivers and commuters to offer and request rides by also R EFERENCES
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