=Paper=
{{Paper
|id=None
|storemode=property
|title=Recommending Eco-Friendly Route Plans
|pdfUrl=https://ceur-ws.org/Vol-891/LIFESTYLE2012_paper1.pdf
|volume=Vol-891
}}
==Recommending Eco-Friendly Route Plans==
https://ceur-ws.org/Vol-891/LIFESTYLE2012_paper1.pdf
Recommending Eco-Friendly Route Plans
Efthimios Bothos Dimitris Apostolou Gregoris Mentzas
National Technical University University of Piraeus National Technical University
of Athens Piraeus, Greece of Athens
Athens, Greece dapost@unipi.gr Athens, Greece
mpthim@mail.ntua.gr gmentzas@mail.ntua.gr
ABSTRACT issue of high importance pertains the development of meth-
As personal transportation is one of the greatest contrib- ods and tools able to support and guide citizens towards
utors of CO2 emissions, means able to assist travelers in pro-environmental behaviors with respect to their traveling
reducing their ecological impact are urgently needed. In habits and decisions.
this work we focus on travel recommenders that encourage Previous research has demonstrated that information re-
green transportation habits among travelers who have a pre- garding transport-related attributes such as travel time, travel
existing interest in taking action to lessen their impact on costs and carbon emissions can lead to changes in citizens’
the environment. We aim to provide urban travelers with travel behavior [3]. Nevertheless, although individuals base
a personalized travel recommender that will nudge them to their choices on the attributes of the choice set (content),
plan routes while considering the environmentally friendli- the presentation of information (context) has also a strong
est travel modes. We present a novel, ecologically-aware effect on travelers’ behavior [4]. The presentation of choices,
approach for travel recommender systems and propose a sys- also known as “choice architecture” [17], refers to the design
tem architecture that incorporates dimensions of recommen- and incorporation of small features or nudges in the choice
dation information elements and profile matching methods. making process, which can assist individuals to overcome
cognitive biases by highlighting the better choices for them,
without restricting their freedom of choice. Tools available
Categories and Subject Descriptors to choice architects can be divided into two categories: those
H.3.3 [Information Search and Retrieval]: Information used in structuring the choice task and those used in describ-
filtering, Selection process; H.3.4 [Systems and Software]: ing the choice options [9]. Recommender systems can act as
User profiles and alert services tools for structuring the choice task and address the prob-
lem of what to present to travelers. Furthermore the use
of information technologies incorporating feedback and per-
General Terms sonalization can be central to make lifestyle or behavioral
Design, Human Factors, Algorithms changes [5] and, in our case, can nudge environmentally-
responsible behavior.
Keywords In this work in progress we focus on recommender sys-
tems that encourage lifestyle changes towards green trans-
Travel Recommenders, Choice Architecture, Nudging, Per- portation habits among travelers who have a pre-existing
suasive Technologies, Lifestyle Change interest in taking action to lessen their impact on the en-
vironment. We aim to provide urban travelers with a per-
1. INTRODUCTION sonalized travel recommender that will nudge them to plan
Environmental issues are becoming increasingly pressing multi-modal routes while considering the environmentally
in our times and means to reduce the ecological impact of friendliest travel modes. We present a novel, ecologically-
citizens’ activities are needed urgently. A major source of aware approach for travel recommender systems and pro-
environmental pollution from citizens’ activities is carbon pose a system architecture that incorporates dimensions of
emissions due to traffic and mobility. It is estimated that recommendation information elements and profile matching
urban transport in the European Union accounts for 15% of methods.
all greenhouse gas emissions [12]. As work and leisure life Our approach is detailed in Section 2. We synthesize con-
become progressively geographically distributed, a research cepts from multi-criteria decision making (MCDM) recom-
mender systems and recommendations diversification to in-
fuse the ecological dimension on travel recommenders. Namely,
we focus on MCDM to infer user preferences and we balance
the utility of routes with their carbon footprint in order to
generate travel recommendations with ecological character-
Paper presented at the Workshop on Recommendation Technologies for istics. In Section 3 we analyze the conceptual architecture
Lifestyle Change 2012, in conjunction with the 6th ACM conference on of a system that implements the proposed approach. An il-
Recommender Systems. Copyright c 2012 for the individual papers by the
papers’ authors. This volume is published and copyrighted by its editors.
lustrative scenario depicts the various user interactions with
the proposed system in Section 4. We conclude with related
Lifestyle @RecSys’12, September 13, 2012, Dublin, Ireland
work and future directions.
2. APPROACH
Contrary to the vast majority of previous research on rec-
ommender systems that has focused on improving the accu-
racy of recommendations, i.e. better modeling user pref-
erences to present individually preferred items, we focus
on recommender systems as a tool for nudging users to-
wards eco-friendly traveling decisions. Specifically, the rec-
ommender generates a list of suggested routes which reside
within the limits of users’ preferences and presents choices
with low carbon emissions. With our approach we address
the problem of a “filter bubble” [15] in its ecological dimen-
sion: users of existing navigation services may be trapped
in a self-reinforcing cycle of emission-intensive travel modes Figure 1: Travel profiles as combinations of alter-
while never being pushed to discover alternatives. native travel modes and corresponding qualitative
The problem an ecologically aware travel recommender CO2 emissions.
system is asked to solve can be formulated as follows: Given
a user u, find a subset S ⊆ AvailableRoutes(u) such that
|S| = P resentedRoutes and the choice of S provides a good
balance between the user perceived route utility and CO2 The alternative routes emerge from ‘travel profiles’ [18]
emissions. The research agenda of the above problem in- which in our case are defined as the combination of one or
cludes two main issues: First what is meant by user per- more of the major transportation modes (personal vehicle,
ceived route utility and how this is calculated and second public transportation, walking or bicycle). In total there
what is the meaning of the term ‘balance’. Both issues can 3
3
P
be answered in a number of ways. Our approach is based on are k
= 7 travel profiles to choose from. Based on the
k=1
utility-based recommenders and involves a three-step pro- travel mode characteristics and associated emission models
cess: users provide their preferences which are then trans- of each travel profile we can infer that the use of more walk-
formed to a user perceived route utility value. In the final ing or bicycle leads to less CO2 emissions (see Figure 1),
step, the utility and the CO2 emissions of a route are pro- thus our aim is to nudge users into using travel profiles that
vided as input to a recommendation algorithm that selects include walking or bicycle.
|S| results to be presented to the user. The alternative routes are annotated with a utility value
based on the submitted user preferences. To this direction
2.1 User Preferences Multi-Criteria Decision Making (MCDM), a set of widely
Following [18] we adopt a utility based approach to elicit studied methods in the Operations Research domain for de-
user preferences. In more details users provide their pref- cision making, can be employed. With MCDM a decision
erences over a set of criteria when planing a route. The problem can be seen as the selection of the best alternative
revealed preferences are used to infer a user perceived util- from a decision matrix M × N with N alternatives and M
ity per route. criteria. More specifically we select Multi-Attribute Utility
First users are asked to assign themselves in one of six Theory (MAUT) methods [7] which determine the utility of
groups of drivers as identified by [2] - Hard driver, Compla- alternatives from user preferences on selected criteria. These
cent car addict, Malcontented motorist, Aspiring environ- methods are based on the concept that bad performing al-
mentalist, Car-less crusader, Reluctant rider (for a thorough ternatives on one criterion can be compensated by good per-
description of these categories please see [2]). This informa- forming criteria. In our case an alternative is a route with
tion is asked only once and affects the level of nudging the criteria Cj . Each criterion has a weight Wj and the ele-
user may be inclined to accept (i.e. an Aspiring environmen- ments ai,j in the decision matrix denote the utility U (ci,j )
talist will be presented with more routes that involve public of criterion ci,j . Indicative MCDM models that can be used
transportation and walking than a Hard driver). include Weighted Sum and Weighted Product models.
Although most navigation applications provide the quick- In Weighted Sum Models a weighted mean over all criteria
est routes as suggestions, in real life situations users are dimension for all alternatives is calculated. The result is
n
concerned with other aspects when deciding on a specific P
a utility score per alternative: Ui = aij wj . Weighted
trip in a city. For example, the price of the ticket or the fare j=1
(e.g. for a taxi) of the transport mean might influence the Product Models multiply instead of summing up the criteria,
user’s decisions [18]. Moreover travelers interested in reduc- and power instead of multiplying the weights in order to
ing their carbon footprint may be willing to walk a bit more n
w
aijj .
Q
calculate the utility scores: Ui =
or accept a longer trip. Based on the above arguments, in j=1
a second step users are asked to provide their preferences
on a set of criteria which are then used to calculate a per
route utility value. Indicative criteria are: preferred delay
2.3 Recommendation Strategies
for arrival, preferred walking or bicycling time and preferred Given a set of candidate routes AvailableRoutes(u) and a
travel cost. given threshold K of final desired number of recommenda-
tions, the optimal scenario of recommendation is finding a
2.2 Routes and Utility Calculation set of routes, that has the highest perceived utility and the
preferences as well as information related to the current con-
text. In more details we identify the following information
elements:
• User preferences provided by the user through a multi-
criteria input interface together with the routing query
before the trip planning.
• User profile configured by the user through an input
interface on the first use of the system.
• Current context of the user, e.g. trip purpose (busi-
ness, leisure, tourism), weather and traffic information.
3.2 Routing engine
The routing engine takes as input a set of routing options
and generates a set of itineraries. It is controlled by the Rec-
Figure 2: Proposed Architecture. ommendation service that manages the options on behalf of
the user and adjusts the values based on the user’s profile.
Routing options to be supported include route characteris-
lowest CO2 emissions. However such an optimal top−K an-
tics such as travel modes. The results should include infor-
swer set in general does not exist: lowering CO2 emissions
mation regarding emission levels, calculated with emission
typically does not correlate with the highest utility routes
models and the estimated arrival time at the destination.
being selected. As a result, we have to achieve a balance be-
tween CO2 emissions and route utility. In order to generate 3.3 Recommendation Service
lists of suggested eco-friendly routes, recommendation diver-
This component comprises of four distinct functions re-
sification algorithms can be employed following [23]. The
sponsible for personalizing and contextualizing the alter-
two problems share similarities: diversification solutions at-
native routes to be presented to the user. The first two,
tempt to identify relevant yet diversified items whereas we
query personalization and contextualization, transform the
want to suggest relevant yet eco-friendly routes.
user routing query and context signals into the appropriate
Two optimal algorithms are the MaxUtil which maximizes
routing engine API parameters. Query personalization is de-
the utility of the K routes presented and the MinCO2 that
pendent on the available transportation means the user has
minimizes the CO2 emissions of the K routes. Additional
at her disposal i.e. car/motorcycle and bicycle and considers
heuristic algorithms are the Swap and Greedy similarly to
any disabilities the user may have. Two rules are defined for
[20] and [22]. With algorithm Swap we begin with the K
these cases:
highest utility routes, and swap the route with the high-
est emissions with the next highest utility route among the • If the user owns a vehicle then routing results involving
remaining routes. A route is swapped only if the overall car/ motorcycle should be considered, similarly if the
CO2 emissions of the displayed set is decreased. To prevent user owns a bicycle, routing results involving a bicycle
a sudden drop of the overall utility of the resulting set, a should be considered.
pre-defined upper-bound U B denoting how much drop in
utility is tolerated has to be used. With the use of U B, • If the user has disabilities then bicycle and public means
swapping stops when the utility of the resulting routes be- of transportation that do not provide amenities for per-
comes lower than U B. Furthermore the value of U B de- sons with disabilities should be avoided.
pends on the drivers group the user has assigned herself (see
Section 2.1). With algorithm Greedy recommendation lists Query contextualization considers a number of static rules
are formed by combining routes from different travel profiles. to further filter the initial set of results:
The list with the lowest emissions and acceptable utility is • Weather data: if the day is rainy, then bike and walk-
selected. Lists with acceptable utility are those whose differ- ing time should be kept to a minimum.
ence with the highest utility list resides within certain limits:
HU − Ui ≤ AD where HU is the Highest Utility, Ui is the • Traffic data: if there is indication of high traffic den-
utility of list i and AD is the Acceptable Difference which sity, car time should be kept to a minimum.
depends on the drivers group the user has assigned herself.
• Trip purpose affects the possible delays with respect
3. ARCHITECTURE to the time of arrival. Expected delays should be min-
imized for business trips, can be moderately tolerable
In this section we describe a system architecture that for leisure trips, and tolerable for tourism trips.
shows how our approach can be instantiated and extended to
incorporate personal and contextual information. The pro- Based on the aforementioned rules, the user query is aug-
posed architecture comprises of the following components: mented and a request is sent to the routing engine for alter-
Recommendation information elements, Recommendation ser- native itineraries.
vice and Routing engine (see Figure 2). Following query personalization and contextualization, the
routing engine is triggered to generate a set of n results per
3.1 Recommendation information elements travel profile given the set of personalization and contex-
These elements incorporate the individual user profile and tualization parameters. Once the results are available, two
Figure 3: User input: Preferences on the criteria, Figure 4: Recommendation lists as combinations of
and relative importance of criteria. travel profiles. Each travel profile is a combination
of one or more travel modes (CAR, PT - Public
Transport, W/B - Walking or Bicycle).
more functions are triggered. The utility calculation func-
tion maps the recommendation information elements and
the characteristics of the route to a perceived utility value In this scenario we use the Ordered Weighted Averag-
per user and route following MCDM methods as described ing (OWA) MCDM method [19]. With OWA the normal-
in Section 2.2. This step allows the projection of the user’s ized criteria values aij (numerical values of the poor, fair,
decision strategy on the results. The final step refers to the good selections) are multiplied with the corresponding im-
generation of recommendations following Section 2.3. portance weights wj (importance percentages). Next, rather
than being aggregated, weighted criteria values bij = aij wj
for each alternative i are re-ordered by descending value so
4. ILLUSTRATIVE SCENARIO that bi1 > ... > bin . An OWA operator is applied to the
In the following we describe an illustrative use case sce- ordered criteria values that can potentially emphasize the
nario of our approach. John is about to go out and meet his better or the poorer values. At this preliminary phase of
friends at a movie theater and uses his eco-friendly travel this work we opt for the neutral operator [19] which assigns
recommender to plan the route. equal weights to each criterion and the final utility scores
are calculated as the weighted sum of the criterion values.
4.1 Query Personalization and Contextualiza-
tion 4.4 Recommendations
The recommendation service interacts with the routing Using algorithm Greedy, as explained in Section 2.3, we
engine and retrieves a number of routes to present to John. generate lists of recommended routes by combining results
According to the user profile, John owns a car, has no dis- from travel profiles (see Figure 4). The total utility and CO2
abilities and has described himself as a ‘complacent car ad- emissions of each list are calculated as the sum of the utilities
dict’. According to the contextual information elements, the and emissions of each element in the list. The ‘Recommen-
weather conditions are good, traffic is low and the trip is for dation List 1’ has the highest utility for John. The accept-
leisure. A number of results are retrieved from the routing able difference indicates that the recommendation lists one
engine per travel profile. to three should be considered and from those, list 2 has the
lowest emissions and is presented to John:
4.2 User Preference Elicitation
1. Using only his car, John can reach his destination
John is asked to define the poor, fair and good levels of
within 30 minutes.
each option per criterion (Figure 3.a). Normalized scales
are selected for the criteria in order to make the alternatives 2. Using his car to a parking spot near his destination and
comparable. Similarly to [16] we employ qualitative scales then walk for 15 minutes, John can reach his destina-
which are then transformed to numerical values according tion within 40 minutes but save 20% of CO2 emissions.
to the rank order rule for further processing. The numerical
mapping is 1 for poor, 2 for fair and 3 for good. 3. Using his car to reach a bus stop close to his home
Furthermore John specifies the relative importance of cri- John can reach his destination within 30 minutes and
teria on a percent range, with weights summing up to a total save 30% of CO2 emissions.
of 100% as shown in Figure 3.b. Changes in one of the slid-
ers in Figure 3.b adapt the values of the rest of the criteria John decides to follow option 2 to reach his destination and
so as to preserve the total of 100. In order to ease user in- save 20% of CO2 emissions.
put we can determine a set of predefined profiles (e.g. in the
Figures we see that the ‘Leisure’ preferences profile has the 5. RELATED WORK
Delay criterion set to 10-30 minutes and the ‘Importance on Commonly, recommender systems generate prioritized lists
Delay’ option assigns higher weight to the ‘Delay’ criterion). of unseen items, e.g., music, books, by trying to predict
a user’s preferences based upon their profile. Travel rec-
4.3 Utility Calculation ommender systems are designed to support travel planning
decisions before travel or while on-the-move [6]. These sys- menders. Users are presented with route alternatives that
tems capture user preferences, either explicitly or implicitly reside within the limits of their preferences and yield re-
and suggest destinations to visit, points of interest (POIs), duced carbon emissions. Furthermore we described a system
events or activities and/ or alternative routes. The main architecture which combines multi-criteria recommendation
objective of a travel recommender system is to ease the in- techniques with profile matching methods.
formation search process of the traveler and to convince her There are various aspects that need further research. First,
of the appropriateness of the proposed services [10]. the field of MCDM encompasses a number of methods which
With respect to route suggestion, certain systems consider could potentially fit into our problem, such as the Analytical
multi-modal itineraries (i.e. routes that involve the use of Hierarchy Process and the Linguistic Ordered Weighted Av-
more than one transportation means, for example reaching eraging (LOWA). Our plan is to examine the applicability of
the destination with a combination of car, bus and walk- these methods as well as compare them. Second, implement-
ing). Tumas and Riccie [18] present a personalized mobile ing and comparing the suggested algorithms for striking a
city transport advisory system that allows users to receive good balance between routes utility and eco-friendliness will
recommendations for personalized paths between two arbi- reveal which are best suited for this problem. Third we
trary points in the city of Bolzano on their mobile phone. will investigate combinations of the proposed recommender
They specify travel and user profiles which are then utilized with persuasive interfaces for eco-feedback. We expect that
to rank different multi-modal routes in the city and present such interfaces, by informing users of their carbon footprint,
the top ranked to users. They focus on computing sugges- can persuade them to choose the recommendations with low
tions according to users’ travel-related preferences captured environmental impact. Last, we are going to evaluate our
through questionnaires and based on four criteria: walking, proposed approach in real life situations. To this direction, a
bus changes, time of arrival at the destination and sightsee- prototype system that materializes the approach and related
ing. Zenker and Bernd [21] combine event recommendations architecture is under development within the Peacox FP7
and pedestrian navigation with (live) public transport sup- project. In addition two field trials in Vienna and Dublin
port in order to assist passengers in finding interesting events have already been planned. In these trials a set of 65 users
and navigating to them. will use and evaluate the proposed recommendation service
Decision making is a central component in route planing in their everyday life for a total duration of two weeks.
applications [8]. In this respect, MCDM techniques have
been employed to model combinations of user desires and to 7. ACKNOWLEDGMENTS
allow users to specify their personal decision strategies while
Research reported in this paper has been partially funded
receiving personalized alternatives adjusted to their needs.
by the European Commission in the Information Society
This view is similar to recent definitions of recommenda-
Technologies (IST) project “Ecological aware navigation: us-
tion problems as MCDM problems. Multi-criteria based
able persuasive trip advisor for reducing CO2 consumption”
recommenders provide suggestions by modeling a user’s util-
(Contract no.: 63761).
ity for an item as a vector of ratings along several criteria.
A comprehensive study of recommender systems based on
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