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From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors


Abstract and Figures

Nowadays, most people carry around a powerful smartphone which is well suited to constantly monitor the location and sometimes even the activity of its user. This makes tracking prevalent and leads to a large number of projects concerned with trajectory data. One area of particular interest is transport and mobility, where data is important for urban planning and smart city-related activities, but can also be used to provide individual users with feedback and suggestions for personal behavior change. As part of a large-scale study based in Switzerland, we use activity tracking data to provide people with eco-feedback on their own mobility patterns and stimulate them to adopt more energy-efficient mobility choices. In this paper we explore the opportunities offered by smartphone based activity tracking, propose a general framework to exploit location data to foster more sustainable mobility behavior, describe the technical solutions chosen and discuss a range of outcomes in terms of user perception and sustainability potential. The presented approach extracts mobility patterns from users’ trajectories, computes credible alternative transport options, and presents the results in a concise and clear way. The resulting eco-feedback helps people to understand their mobility choices, discover the most non-ecological parts of their travel behavior, and explore feasible alternatives.
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From Location Tracking to Personalized Eco-Feedback: a Framework for
Geographic Information Collection, Processing and Visualization to
Promote Sustainable Mobility Behaviors
Dominik Buchera, Francesca Mangilib, Francesca Cellinac, Claudio Bonesanab, David Jonietzd, Martin
aInstitute of Cartography and Geoinformation, ETH Zurich, Stefano-Franscini-Platz 5, 8093 Z¨urich, Switzerland,
Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Galleria 2, Via Cantonale 2c, 6928 Manno, Switzerland,
cInsitute for Applied Sustainability to the Built Environment, SUPSI, Via Trevano, 6952 Canobbio, Switzerland
dHERE Technologies, Seestrasse 356, 8038 Z¨urich, Switzerland
Nowadays, most people carry around a powerful smartphone which is well suited to constantly monitor the
location and sometimes even the activity of its user. This makes tracking prevalent and leads to a large
number of projects concerned with trajectory data. One area of particular interest is transport and mobility,
where data is important for urban planning and smart city-related activities, but can also be used to provide
individual users with feedback and suggestions for personal behavior change. As part of a large-scale study
based in Switzerland, we use activity tracking data to provide people with eco-feedback on their own mobility
patterns and stimulate them to adopt more energy-efficient mobility choices. In this paper we explore the
opportunities offered by smartphone based activity tracking, propose a general framework to exploit location
data to foster more sustainable mobility behavior, describe the technical solutions chosen and discuss a
range of outcomes in terms of user perception and sustainability potential. The presented approach extracts
mobility patterns from users’ trajectories, computes credible alternative transport options, and presents the
results in a concise and clear way. The resulting eco-feedback helps people to understand their mobility
choices, discover the most non-ecological parts of their travel behavior, and explore feasible alternatives.
Keywords: Mobility Tracking, Mode Detection, Trajectory Clustering, Eco-feedback, Sustainability
Preprint submitted to Elsevier February 21, 2019
1. Introduction
Many smartphone apps perform automatic mobility and activity tracking, with increasing precision both
in path recognition as well as in the identification of transport modes [
]. While many applications focus
on health and fitness aspects [
], or passively track in the background, for example to give location-based
recommendations (e.g., [
]), there has been a recent surge in applications concerned with individual mobility
behavior [
]. Such apps are often driven by ecological incentives such as reducing CO
from travel or generally increasing the sustainability of personal transport. The key components aim at
making people aware of their mobility behavior, i.e., at showing them the impact of their behavior on the
environment, on their financial budget or on the time spent traveling.
Still, in many applications users just check the individual routes they travel and do not get a complete,
yet simple and immediate understanding of their mobility patterns. It is left to the app users to assess which
journeys are suitable for optimization and what the effects of a behavioral change on the environment are.
Getting the right eco-feedback, defined as “feedback on individual or group behaviors with a goal of reducing
environmental impact” [
], and making users aware of their mobility patterns and the consequences they
entail, is acknowledged as a necessary—though not sufficient—condition towards more sustainable mobility
patterns (cf. [
]). For example, current eco-feedback technologies lack the distinction between systematic
and unsystematic travel behavior, the provision of a personalized and meaningful assessment of possible
changes and the presentation of the various effects behavioral changes entail.
In the Swiss-based GoEco! project
], we addressed these issues, taking advantage of the wide
availability of smartphone devices to encourage people to reduce their use of cars. Using a specifically
developed smartphone app [
], we performed a large scale field test, monitoring the activities of several
hundred volunteer citizens from Southern Switzerland and the City of Zurich. During three distinct mobility
tracking periods distributed over a year, each one lasting at least six weeks, the movements and transport
mode choices of participants were recorded. To assess the effects of eco-feedback on mobility behavior,
no feedback was given to the study participants in the first and last period (i.e., they were only tracked
to collect a baseline dataset). Starting after the first period, they were also stimulated with eco-feedback
and gamified activities, with the aim of persuading them to improve their mobility behavior. While the
gamification elements (such as goal-setting, challenges, badges or leaderboards) were integrated directly into
the smartphone app, eco-feedback was additionally given in the form of a report booklet, sent to the study
participants via e-mail. In this paper, we mainly focus on this non-interactive eco-feedback report which was
sent to the participants at the end of the first period.
This report contained basic elements such as summaries of distances traveled, durations spent on different
modes of transport or energy consumption and CO
emitted as effect of the recorded travel behavior.
These summaries were available at various time scales, such as the overall study period or individual weeks.
Additionally, the report contained a section about detected systematic journeys (appearing multiple times
within a few weeks, such as traveling to work or school), highlighting which of them could be optimized with
little loss in travel convenience by adopting a proposed alternative. An estimate of the possible impacts on
the environment (energy consumption and CO
emissions) rounded off the report. In post-experimental
surveys we assessed the plausibility of the detected systematic routes and the proposed alternatives. Here, we
present the algorithms used to detect systematic travels and to compute feasible alternatives. In addition, we
provide an estimate what a sample of individuals like the one of the GoEco! project could potentially save in
terms of CO
emissions by changing mobility behavior to more sustainable modes of transport, according to
the proposed alternatives.
To the best of our knowledge, this is one of the first experiments of its type. Therefore, it required the
development of a framework for collecting, analyzing and reporting individual mobility patterns and potential
for changes towards more sustainable mobility choices. The proposed framework includes: (i) location
tracking; (ii) identification of transport modes; (iii) assessment of individual mobility patterns (modal share
and mobility footprint); (iv) identification of systematic itineraries; (v) identification of more sustainable
1See for details and recent updates.
mobility choices; (vi) presentation of the outcomes as personalized feedback. Section 2 introduces related
work from the domain of mobility behavior assessment and eco-feedback provision. Section 3 presents the
proposed framework, structurally following the six parts mentioned. In Section 4 we provide additional
details, illustrating the core methods of our framework. The results of the application of the above framework
and methods to data gathered as part of this study are presented in Section 5. Finally, we discuss the
strengths and weaknesses of our framework and point out remaining open questions in Sections 6 and 7.
2. State of the Art in the Analysis of Activity Data to Understand Mobility Behavior
In the past, several studies have aimed at not only analyzing people’s mobility behavior, but also at making
it more environmentally sustainable by using mobile applications and a feedback loop. Examples include
UbiGreen [
], Tripzoom [
], MatkaHupi [
], SuperHub [
] and Traces [
]. However,
these studies often involve small sample sizes (up to several dozens of users), short study periods (up to a few
weeks), lack a control group or solely perform qualitative assessments [
]. For example, UbiGreen [
was one of the first smartphone-based eco-feedback applications and featured a visual representation of the
ecological impact of travel behavior in the form of a tree that lost or gained leaves. If one choses sustainable
transport modes on a certain day, the tree would grow greener. The thirteen participants mostly responded
in positive ways to the application, but a systematic quantification of the UbiGreen effects was not performed
in the paper. Building on these previous studies, our research tries to apply their findings to a large sample
over the duration of one year, and studies the adoption of eco-feedback related to mobility and the resulting
changes in behavior.
However, before eco-feedback can be provided or sustainable travel alternatives can be proposed, movement
resp. mobility needs to be recorded. This is a topic which has attracted interest from various disciplines
and has also proven to be useful for numerous applications other than the promotion of sustainable mobility
behavior, including transportation planning (e.g., [
]) or personalized routing (e.g., [
]). Commonly,
movement is tracked using Global Navigation Satellite System (GNSS) receivers, e.g., the GPS sensor in the
mobile phone of a user. Numerous studies have proposed approaches to augment this tracking data, e.g., by
inferring the performed activities or the used transport modes by considering additional sensor data (see for
instance [
]). Such research has allowed the development of several apps for tracking users’ itineraries
and activities [
]. To obtain the results in Section 5, we applied the methods presented within this paper
to GPS data tracked by such an app (namely Moves
), which automatically recorded user trajectories
and identified visited places and routes. To improve the transport mode recognition, an additional new
classifier was developed that learned from previously tracked and validated data and additionally considered
spatio-temporal context (cf. [7]).
Once such movement data is available, it must be analyzed and mined for observable and interpretable
individual mobility patterns. The most basic of these patterns include modal splits and aggregate values (such
as total CO
emissions or travel duration). Most of the previously mentioned studies used such aggregate
values, e.g., in UbiGreen [
], where the daily number of “eco-friendly” journeys is compared to the number
of car journeys. In our framework, we provide methods to analyze mobility on a much more detailed (and
individual) level by inspecting individual journeys, with the aim of providing users with a more thorough
feedback and thus offering more assistance for behavior change. For this task, several trajectory pattern
mining techniques can be employed [
]. Previous studies on mobility data mining have particularly focused
on the identification of visited places from raw GPS-data by assessing prolonged permanence in a small area of
the moving person [
], and the derivation of traveled routes between these locations by map matching
techniques [
]. By assessing the similarities between trajectories of several individuals, these methods
generally discover knowledge about collective mobility behaviors, like common places of interest, frequently
visited locations, the way of moving, etc. This is useful information in urban planning and transportation
management for instance [
]. Other works, closer to our aim of generating meaningful eco-feedback,
2A fitness tracking app discontinued in July 2018.
use pattern mining techniques to detect individual mobility habits by looking for regularities in personal
trajectory data [54, 36, 27].
After finding patterns in the trajectory data, personal feedback and proposed sustainable travel alternatives
can be provided. Typically, this requires a personalized, multi-modal routing application. When computing
optimized routes, personalization is often achieved by incorporating individual preferences and constraints as
parameters of route segment traversal costs into the routing algorithm (e.g., [
]), or using a preceding
heuristic [
]. A range of techniques has been developed especially for computing routes on large-scale,
dynamic multi-modal transportation networks (e.g., [
]). An appropriate aggregation of the output of
such planning systems can then be given to the user as feedback on possible more sustainable alternatives.
Giving meaningful eco-feedback is a well-researched topic (cf. [
]). Commonly, one tries to satisfy
psychological (autonomy, competence and relatedness) and social needs (achievement, affiliation and intimacy,
and leadership and followship) and should respect the current context of a user (e.g., how motivated a person
is, if a user has the ability to perform a certain behavior, if the timing of the feedback is right, and if a
user is in the correct stage of a behavioral change process) [
]. In terms of eco-feedback particularly for
mobility behavior, there are many more open questions, as mobility is highly individual and to a large degree
depending on personal, temporal and environmental context [
]. As it is generally difficult to passively
collect data on a user’s context (e.g., if the user needs to carry heavy luggage for a certain journey), in
looking for available more sustainable alternatives we here primarily respect temporal and environmental
context factors, such as the available transport modes at a given date and time.
3. A Framework for the Generation of Eco-Feedback
Generating eco-feedback is a complex procedure involving different steps. Figure 1 shows the individual
steps of our framework to induce behavioral change in the context of mobility choices. The system includes a
mobility tracker installed on the personal smartphone of each user. The tracker’s task is to automatically and
passively register all routes traveled by each participant. The system then assigns a transport mode (TM) to
each detected journey, based on a transport mode classification algorithm and/or on direct interrogation of
the user (e.g., the recipients of eco-feedback are often given the possibility to validate and adjust a recognized
transport mode). The collected routes are then stored in a database and analyzed following three main goals:
to profile the user mobility patterns, in terms of modal share and related footprint (global analyses);
to evaluate the potential improvement each user can achieve regarding overall sustainability of her
mobility choices;
to identify similar trajectories (i.e., systematically traveled routes), for whom targeted and realistic
suggestions for more sustainable mobility choices (alternatives) can be proposed.
Finally, the outcomes of this threefold analysis (eco-feedback ) are presented to the user in a report.
3.1. Mobility Tracking
In our mobility tracking framework, a trackpoint is the most basic object. It consists of a longitude, a
latitude, and a timestamp. Trackpoints are aggregated into routes which are defined as transfers from a point
of interest (POI) to another, where a POI is a place visited on purpose, to perform or attend some activity
(such as working or shopping). Each route represents a process, which has a well-defined start and end point,
both in space as well as in time (these start and end points are central for several parts of our method, while
the actual trackpoints of the route play a minor role). The distance traveled is the sum of the distances
between all trackpoints, and the duration of a route is the difference between the timestamps of the end and
start positions. The longitude and latitude coordinates of the start and end points of a route will be referred
to as visited positions, whereas the POIs connected by a route are physical places that cannot be described
by a single pair of GPS coordinates; indeed, also because of the inaccuracies of the tracking system, different
(yet close) visited positions may refer to the same POI.
Figure 1: The proposed personalized eco-feedback framework generates a variety of feedback components from the classified
movement trajectories: global analyses, individual transport mode alternatives, and concrete suggestions for systematically
traveled trips.
Each route can be traveled with more than one transport mode. Segments, on the other hand, are parts
of a route covered with a single mode of transport. A stay point is any point where a user spends some
minutes, e.g., when changing from one transport mode to another. For example, a route traveled by [walk
walk] would consist of two walk segments, and one train segment and will include two stay points
(i.e., the train stations).
The goal of a tracking app is therefore to collect a thorough yet concise set of trackpoints, and to identify
the visited places and stay points which allow to partition the whole set of trackpoints into routes and
segments. Common issues in the development of tracking apps for large-scale mobility data collection studies
]) include battery consumption, availability on diverse operating systems, and varying classification
accuracies due to different sensors (cf. [
] for an overview). There are several apps that promise off-the-shelf
solutions to these problems (with varying success). For example, in our study, the commercial app Moves
was employed, which allowed custom apps to connect and download tracking data through an application
programming interface (API). Moves pre-processed the data from mobile sensors and provided trajectories
as sequences of trackpoints already partitioned into segments, routes and stay points, which were of sufficient
quality for eco-feedback and mobility analyses [
]. While the exact workings of Moves were not disclosed
publicly, from our experience it recorded a single trackpoint approx. every 5 minutes (depending on the
transport mode and GPS availability), used the accelerometer for transport mode identification and treated
places where users spend more than approx. 10 minutes as staypoints. Alternative options to employing
Moves would have been to either use a more pricey paid app, integrate an open-source solution, or develop
a new tracking app from scratch. As the focus of this study was on the exploration and analysis of an
eco-feedback intervention, there were not sufficient resources available for any of the alternatives (especially
the development of a reliable tracking app from scratch is a non-trivial task).
3.2. Transport Mode Detection
In order to assess the ecological impact of peoples’ mobility, a transport mode has to be allocated to
each segment. Similarly to most commercial trackers currently available, Moves only distinguishes between a
small set of transport modes, specifically walk,bike and motorized transport. As a more detailed classification
is necessary for ecological footprint analyses, we developed a classifier with the ability to distinguish between
thirteen transport modes (car,electric car,motorbike,scooter,train,bus,tram,plane,ship,bicycle,electric
bicycle,kick scooter and foot). Details of the algorithm for transport mode classification are given in [7].
To improve the reliability of the assigned transport modes, we present all the routes identified by Moves
to the users, showing the transport modes assigned by the classifier. The users are then asked to validate
the routes, i.e., confirm or change the mode of transport and delete a route if it is “particularly wrong”,
either regarding the places visited or the travel time. This possibility to delete routes was mainly put in
place as sometimes the recorded routes are completely unrealistic (e.g., on the border of a country, where
due to changing mobile phone network carriers, the cell tower positioning method yields bad data) and
would greatly distort following data processing. On the one hand, this manual validation supplies a set of
labeled data that can be fed back into the algorithm to improve the accuracy of the future transport mode
classification, while on the other hand, it ensures a high accuracy of the collected data. As the user validation
continued along the whole study period, the dataset of routes and transport modes used as a base for all our
computations can be considered as reasonably correct and not affected by the accuracy of the classifier.
3.3. Mobility Patterns: Modal Share and Mobility Footprint
The most basic individual mobility features, which summarize the multitude of data automatically
gathered by the app, are the total distance traveled and the related travel duration, averaged over a specific
time period. In this study, we opted for a weekly time period, as it is easily understood by users, it allows
including both systematic and non systematic routes (i.e., routes that are resp. are not traveled several times
within a short time frame, such as a week), and accounts for the variability of daily mobility needs arising
from individual weekly schedules.
A second set of mobility features includes the individual shares of the different modes of transport used.
Our eco-feedback shows this modal share based on the average weekly kilometers traveled by each mode of
transport, as this is directly related to energy consumption and CO
emissions. Of course, it would also be
possible to show the modal share based on traveling times or the number of routes, however, these aspects
are more relevant when optimizing schedules than for improving ecological footprints.
To simplify the feedback, some transport modes are aggregated, such as car (both internal combustion
engine and electric car), public transport (train, tram and bus), bicycle (both conventional and electric),
and foot, while other encompasses all the remaining modes of transport. Finally, the eco-feedback includes
information on users’ ecological footprints, expressed in terms of energy consumption (kWh) and CO
emissions. Both their average values per week and per kilometer traveled are shown to the user. To provide
such estimates, we relied on the Mobitool
consumption and emission factors [
], which depend on the
mode of transport, refer to a single kilometer traveled in Switzerland and take into account the consumption
and emissions of the full life-cycle (see Table 1). For many transport modes these factors heavily depend
on the involved power generation systems (e.g., power from renewable sources leads to much fewer CO
emissions than power generated by fossil-fuel power plants). As such, these factors and the resulting energy
consumption and CO
emission values are specific to Switzerland. In order to apply the framework to
another region, the power generation systems within that region have to be analyzed and taken into account
3.4. Systematic Mobility
A central element of our eco-feedback are the suggestions of mobility alternatives specific to the itineraries
traveled by each individual user. Since it is not possible (and neither useful) to suggest alternatives for all
ever traveled routes, we focus on the systematic ones, i.e., those traveled multiple times within a certain
(short) time frame. Systematic itineraries are of interest, as a behavioral change in these situations is easier,
since they can be planned in advance and become part of the daily routines. Also, they have a larger potential
to reduce energy consumption and CO2emissions, as they are repeated over time.
To provide applicable eco-feedback and suggest credible alternative modal choices, our approach moves
from individual routes to loops of routes starting and ending at the user’s home place. Without this step (i.e.,
if suggestions of alternatives are based on routes only), the system might end up suggesting unreasonable
or conflicting solutions within the same loop. For example, in a car-based [home
city is a Swiss platform for mobility management tools and curated environmental data.
Transport Mode Energy Required CO2Produced
(kWh/km) (gCO2/km)
Car 0.91 197.23
Bus 0.64 145.41
Tram 0.55 37.47
Train 0.14 7.32
Bicycle 0.04 7.64
Walk 0.00 0.00
Table 1: Energy consumption and CO2emission factors used to estimate a user’s ecological footprint.
home] loop, suggesting to replace the car-based route [workplace
city centre
workplace] with a bicycle-based one is not plausible, since it cannot be assumed that a bicycle is available
at the user’s workplace. Or, in a car-based loop [home
home], suggesting
public transport to reach the workplace might not be acceptable, if soon after work the user directly goes to
a large supermarket in the suburbs, which is not served by public transport. To avoid such inconsistencies,
we need to explicitly take into account the constraints that choosing a means of transport on a specific route
imposes on the following routes. In conclusion, by considering clusters of systematic loops, we can account
for systematic mobility patterns of users and at the same time identify reasonable alternatives.
The specific algorithm adopted for the identification of systematic and non-systematic loops is described
in Section 4.1. Instead of blindly searching for clusters of similar itineraries based on standard trajectory
clustering algorithms, we focus on the needs and the limitations of this study and develop a more interpretable
algorithm by answering to some questions such as “how should a loop be defined?”, “how different can loops
be, which belong to the same cluster?”, “how many times and how frequently has a loop to be traveled, in
order to be considered systematic?”. Answering these questions, we arrived at our definition of systematic
loops and at the selection of heuristics adopted by the algorithm, by keeping in mind the final goals of the
intervention, i.e., to suggest feasible and meaningful alternatives that can effectively induce a behavioral
change, and to assign realistic values to the individual potentials for change. The developed algorithm also
accounts for the limitations and inaccuracies of the tracking system and tries to correct them.
3.5. Alternatives and Potential for Change
The assessment of someone’s potential for change is primarily based on the comparison to optimal travel
behavior, where optimal in this case is computed with respect to CO
emissions. While many factors could
be included in this computation (such as possibilities to telecommute, or the rescheduling of activities),
here we only consider the individual routes and the modes of transport chosen. This choice is primarily
made based on the available data, which is not detailed enough to infer accurate schedules and contexts of
users. We consider the contribution of all the systematic loops and all other routes (i.e., routes for which no
systematic loop could be constructed) to the overall CO
production and, for each of them, we identify an
optimal alternative solution, i.e., a different itinerary and modal choice that allows reaching all the POIs
visited in the original loop or route, while reducing the overall CO
production. Note that this means that
we consider the alternatives for all traveled journeys to compute the optimal mobility behavior. However, in
the final feedback, suggestions for concrete alternatives are only given for routes that are frequently traveled.
The potential for change is then computed as the difference between the actual emissions and those that
would have been achieved by replacing all loops in each cluster of systematic loops and all other routes by the
corresponding optimal alternative. When no alternative is found, the original loops or routes are retained.
Therefore, given any route consisting of a number of segments annotated with a mode of transport, we
compute the CO
produced as the sum of all the segment lengths multiplied by an emission factor which
depends on the mode of transport. Again, we refer to the Mobitool values already introduced in Table 1,
specific to Switzerland. Duration, distance, and start and end points of routes and segments are defined as
in Section 3.1.
A range of different optimization criteria could have been used instead of CO
emissions, such as the
total covered distance, the duration spent traveling, or the required energy. We focus on CO
as we are concerned about giving eco-feedback to people (which ultimately should result in a reduction of
greenhouse gases emissions), and this has the benefit of reducing the required energy as well, as there is a
strong correlation between energy consumption and CO2emissions.
Concerning the identification of alternative solutions, in order to successfully replace a route, an alternative
has to respect the following requirements:
It cannot use a mode of transport which is not available to the user (e.g., if someone does not own
a bicycle, alternatives containing bike segments are invalid). While this can be extracted from the
validated routes themselves, the study participants were also initially asked which modes of transport
they have access to.
Its duration should not be excessively long (e.g., if traveling by public transport takes a user three
times as long as traveling by car, she will most likely ignore the suggestion). This is less problematic
for short routes, as there the metric of interest is usually the additional time an alternative takes (e.g.,
replacing a 10 minutes car route with a 20 minutes bicycle route is acceptable, even though it increases
the traveling time by 100%).
Its CO
production should be substantially lower than the original one (e.g., more than 5% lower, to
account for tracking and positioning inaccuracies). This is usually the case when switching to a more
eco-friendly mode of transport.
Requirement 1 is particularly important when considering loops, especially those including car and bicycle
transport modes. When starting a loop with one of these modes of transport, we need to make sure it is
brought back to the starting location (i.e., the user’s home place). On the other hand, if the first route
does not start with any of these two modes, they cannot be used later on in the loop either. While the
first requirement imposes a strict criterion for removing unacceptable alternatives, the other two form an
optimization problem. The algorithm to identify alternative solutions for routes and loops that meets the
requirements above is described in Section 4.2.
3.6. Report
The analyses described above lead to an eco-feedback which is presented to each user in the form of a
short report. The report is divided into two parts: the first part analyzes the potential for change when
only systematic loops are replaced by the proposed alternatives, while the second part includes the complete
set of loops and routes recorded for each user. As changes induced in systematic itineraries may have a
larger impact on the overall energy savings and may be more easily undertaken and maintained by the user,
the first part of the eco-feedback details both the identified loops, and the suggested alternatives. For each
systematic loop recognized by our algorithm, users receive a feedback similar to the one depicted in Figure 2.
On the left hand side, a representative loop, selected among the original loops of the systematic cluster, is
shown on the map. In the example of Figure 2, the representative loop consists of three POIs and starts with
a short walk, followed by tram segments. On the right hand side, the suggested alternative is presented. It is
suggested to take the bicycle, which is not only more eco-friendly, but even faster in this case. It produces
0.18 kg CO
each time the loop is traveled and can be completed in 48 minutes, while the original modal
choice produces 0.28 kg CO2and takes 1 hour.
In addition to these individual loop assessments, the second part of the report provides aggregate results
about the overall travel duration and CO
emissions that could have been achieved if the user had always
adopted the optimal alternative suggested by the system. An example of the summary displaying the total
potential for change of a user is shown in Figure 3. In this example, a user, originally choosing the car
for 93.9% of her routes, can see that she could potentially reduce its use to 32.6%, resulting in average
savings of 56.7 kg per week (from 78.5 to 21.8 kgCO
/week). In a similar way, on average the energy
consumption could be reduced by 238.6 kWh per week (from 369.6 to 131 kWh/week).
While the feedback about systematic loops shows concrete suggestions for improvement, the overall
potential for change (Fig. 3) is a less tangible indication. For this reason, in the second part of the report we
Figure 2: View of the alternative for a systematic loop as displayed in the eco-feedback report. On the left is the original loop,
on the right the alternative.
Figure 3: The modal choice of a single user, as identified based on six weeks of monitoring using a tracking app (A), and the
modal choice suggested in the provided eco-feedback (B). Next to each figure, the average weekly traveled distances are shown.
also provide general rules for making non-systematic routes more sustainable. All these routes are divided by
mode of transport and then into three categories (less than 1 km long, between 1 and 3 km long, and longer
than 3 km), and for each of them an alternative route is computed. The routes are then presented in tabular
form, showing the number of times a certain combination was tracked (e.g., the number of car routes shorter
than 1 km), and how many of those could be replaced with a more sustainable alternative, thus giving the
user an idea of how effectively her usual choices of transport modes could be improved. This table contains
all non-systematic routes traveled by a user, irrespective of their appearance within a loop.
4. Methods
In this section we provide more details about the methods to identify loops and systematic loops and to
suggest valid, realistic and more sustainable alternatives for each user mobility choices.
4.1. Identification of Systematic Loops
In a first step, individual trajectories must be aggregated into loops, which then can be clustered into
systematic loops, based on the frequency in which they appear.
To identify loops, we first introduce the concept of connected routes. Two consecutive routes
are connected if the first one ends at the same location where the second one starts (here we are assuming
that routes are chronologically ordered). In practice, the GPS coordinates of the end position of a route
seldom coincide with those of the start position of the subsequent route, and their distance can even be
rather large, due to the (occasionally substantial) inaccuracies produced by the activity tracking tool used in
this project. Therefore, we consider as the same place any two positions whose distance is less than 500 m.
For the loop identification, all routes of a user are considered.
We begin from the first route that starts at the user’s home place (which is known from a study pre-survey)
and follow the sequence of connected routes until we find a route that ends at the user’s home. It can
happen that the ending point of a route (hereafter referred to as route 1) and the starting point of the
next one (hereafter referred to as route 2) have a distance larger than 500 m, for instance due to GPS
malfunctioning. In this situation, one could artificially connect the two consecutive unconnected routes by
adding an unobserved route going from the end point of route 1 to the start point of route 2. However, we
preferred not to add unobserved routes, partially because the cause of such a situation is not necessarily
the loss of a route, but it may also be an error in the GPS positioning system that localizes the user in a
different position than she actually is, and opted for deleting unconnected routes. To this purpose, we used
the following algorithm.
Case 1:
If route 2 starts at the end point of a previous route of the loop under construction, we remove
the intermediate routes and continue the construction of the loop.
Case 2:
If the starting point of route 2 has never been reached before in the loop under construction,
route 2 is discarded and the construction of the loop continues with the next route.
To better understand this algorithm, consider the following example (see Figure 4).
Let us assume that user X has visited the sequence of points [home, work, restaurant, work, home].
Case 1:
If only the [home
work], [work
restaurant] and [work
home] routes are recorded by the
activity tracker, we will have a route ending at restaurant (the second one) followed by one starting at
work (the third one), and it would not be possible to complete the loop. However, we notice that work
has already been visited before, and thus, by removing the [work
restaurant] route, we complete the
home] loop. The identified loop is just a subsection of the correct one, yet it has
been actually traveled and should be taken into account.
Case 2:
If, instead, the activity tracker missed the [work
restaurant] route, the second, i.e., [restaurant
work] route will start from a position which was not seen before in the loop under construction.
Then, the [restaurant
work] route is removed and the construction of the loop continues with the
third [work
home] route, which is connected to [home
work]. Once again, the subset [home
work home] of the original loop is obtained.
Figure 4: Two possible ways a loop of routes is mistakenly recorded by the activity tracker.
Systematic Loops.
Once all loops have been reconstructed, to identify clusters of systematic loops
we look for groups of loops that are similar. To compare loops, we represent them as sequences of POIs in
chronological order. POIs are identified by analyzing the geographical distribution of the visited places. As
already mentioned, several different (yet close) visited places may correspond to the same POI, not only
because of the spatial extent of the POI but also because of the inaccuracies of the GPS and tracking systems.
Therefore, it is necessary to define a threshold distance
below which different GPS coordinates are assumed
to represent the same POI. Once again, the threshold
= 500 m is adopted. Such a choice implies a low
resolution in distinguishing two close POIs. Clearly, this is an application specific setting that should depend
both on the scope of the analysis and the precision and accuracy of the collected data.
The aggregation of similar items (in our case, geographical positions of visited places) is typically
done using a clustering algorithm. As the number of clusters is unknown in this case, we use hierarchical
clustering [
], which allows building the set of clusters by setting a maximum acceptable distance between
two items belonging to the same cluster. The POIs of a user are obtained by applying hierarchical clustering
to the set of her
visited places, i.e., the start and end points of all the routes included in her loops.
Hierarchical clustering builds a hierarchy of clusters by starting from
clusters containing a single place
each and aggregating at each next level of the hierarchy the two clusters of the previous level which are
closest, based on a specified dissimilarity measure. We apply the average linkage distance strategy (cf. [
that defines the dissimilarity between two clusters as the average distance between pairs of elements (one
in each cluster). The aggregation process is stopped when the distance between the two closest clusters
exceeds the predefined threshold of
= 500 m. Finally, each POI is identified by the centers of mass of the
corresponding cluster of visited places.
Each loop can then be translated into an ordered sequence of POIs, so that loops represented by identical
sequences of POIs can be assigned to the same cluster of similar loops. All clusters including at least three
loops are considered to identify the user’s systematic loops. The criteria defining a systematic loop are
subjective and depend on the meaning assigned to the word systematic. Depending on the goal of the
analysis, one may consider as systematic only loops traveled daily, or include also loops traveled once every
two weeks. In this work, having used a monitoring period of only six weeks, and considering that some loops
have been lost due to the limited accuracy of the tracking system, the number of three occurrences implies
that a systematic loop has been traveled on average at least once every two weeks, which was considered
enough to accept it as part of a user’s routines. We used a sample of ten users to test that the detected
systematic loops were actually considered systematic by the users themselves.
A second loop processing step accounts for situations such as the one presented in the example below.
Let us assume that user X travels twice the loop [home
holiday house
home], once the loop [home
holiday house
gas station
home] and twice the loop [home
holiday house
(see Figure 5). With the threshold set up above, none of these loops would be identified as systematic.
However, we believe that the loop [home
holiday house
home] is relevant, although sometimes it has
some additional stop-over, and would like to be able to identify it as a systematic loop.
Figure 5: Similar loops treated as the same systematic loop.
If a loop does not belong to a cluster of systematic loops (that is, its sequence of POIs has been observed
less than three times), but it includes all POIs of another loop in the same order, the two loops are counted as
if belonging to the same cluster of loops, described by the sequence of POIs of the shortest loop. Indeed, the
former is considered an extension of the latter. In the example above, the sequences [home
holiday house
gas station
home] and [home
holiday house
home] will be counted as if belonging to
the [home
holiday house
home] cluster, which will thus be identified as a systematic cluster, as it now
includes five loops. This way some point of interest may be lost, yet more sequences of points of interest that
are actually part of the user mobility patterns and routines can be identified. Our choice is also motivated by
the fact that the activity tracker is not always capable of distinguishing between real POIs and trackpoints,
such as stations, bus stops, gas stations or other stay points where users just temporarily stop along the way
to their actual destination. This creates a number of spurious loops that are, indeed, formally equivalent to
systematic loops, but would not be taken into account in the assessment of the user mobility patterns. Also,
in this case, processing would not be necessary (being, instead, detrimental) whenever the POIs were more
reliably identified, for instance, by a more accurate GPS tracking device or by asking the user to validate
4.2. Identification of Alternatives
In this section, we present the algorithm developed to identify optimal, low-carbon alternatives for all
observed travels.
Identification of Alternatives for Routes.
In a first step, a route planner computes a set of eligible
routes, by considering the different modes of transport (MOT) mentioned in Table 1. As different MOT can
often be combined with a positive effect, we additionally use the OpenTripPlanner (OTP) route planner
and an implementation based on the model introduced in [
] to find multi-modal trips. The OTP uses an
implementation of Round-Based Public Transit Routing [
] for schedule-based transport modes, but is able
to combine this method with street-level routing to compute multi-modal journeys. As the implementation
takes into account the exact departure times of public transport, we picked a representative time (the time
of the route that represents the loop “best”, in the sense that it shares most POIs with other routes in
the same loop and has the smallest departure time difference to them) for each systematic route when
querying the OTP. The exact departure and arrival time of the resulting routes were discarded, though,
and only the overall duration was kept for analysis. The approach presented in [
] is primarily used for
less frequently used combinations, which cannot be handled by the OTP (such as combinations of bicycle
and public transport). It essentially uses a heuristical two-step procedure, where first potential transfer
nodes are computed (between different modes of transport), which are evaluated for feasibility in a second
round (e.g., determining if there is actually a train connecting two potential transfer nodes). In terms of the
overall framework, the concrete routing application could be replaced with any routing service. We primarily
relied on OTP as it is one of the few freely available public transport routing applications (which removed
any constraints on the number of alternatives we could retrieve from it). For all the alternatives retrieved
by the route planning algorithms as well as for the original route, we then compute key features, such as
the duration and the total CO
production. For each original route
, this results in a set of alternatives
denotes the alternative route with its trackpoints and modes, which has an associated
total duration daand CO2production ca) that could be used to replace the original route ro.
In a second step, impossible and highly unlikely suggestions are removed from
, if they fulfill any of the
following conditions.
Any of their modes of transport appears in the user’s list of unavailable modes of transport.
The original and alternative routes are too similar. To assess this, we define a similarity measure
between the two routes as the fraction of distances between the trackpoints of the original route and
the closest trackpoints of the alternative that are below 200 meters. Two routes are considered too
similar whenever the modes of the original route and the alternative coincide and the similarity measure
exceeds 70%.
The alternative does not reduce the CO2production by more than 5% (compared to the original).
The total slope (in elevation) of a bicycle-based alternative is more than 1.5%.
The OpenTripPlanner ( is an open source route planner that is able to find multi-modal public transport
The duration of the alternative is too long, i.e., it exceeds the threshold dth:
dth = (do+tmax)tmax
1 + do·tmax
where the parameter
represents the maximal increase in duration and
denotes the duration
in hours of the original route. In our implementation, we set the maximal prolongation of the route
duration as
= 1
2 hours (=1:12 h). Of course, this limit is not reached for shorter durations (e.g.,
a route with an original length of 5 min can maximally be replaced by one of 11.5 minutes). Figure 6
shows the value of dth as a function of do.
Figure 6: Threshold dth for the alternative duration.
It would be possible to filter out routes with long distances as well, even differentiating them by MOT
(e.g., preventing alternatives where one would have to walk for 10 km), but in practice such routes are already
filtered out by the duration constraint, except if the original route is itself in a similar range. For this reason,
we do not use any distance filter.
Finally, the remaining alternatives must be ranked according to some criteria. While more elaborated
choices could be considered (especially if one wished to account for user preferences, such as wanting to lead
a healthy lifestyle, or disliking some form of transport), here we simply rank the remaining routes according
to the CO2produced, in order to give feedback about the sustainability potential of an alternative.
Identification of Alternatives for Systematic Loops.
For each loop, we can identify alternatives
in a similar way as we did for routes. There are some circumstances that have to be specially considered,
though: when generating an alternative loop, the sequence of transport modes has to be respected. Moreover,
the total additional duration of an alternative loop should be further bounded. Using above formula, for
example, a loop of four routes of 30 minutes (a total of 2 hours) could be increased up to 3:48 hours, which
is clearly unacceptable for users. Therefore, requirement 2 of Section 3.5, is satisfied by bounding the total
time of an alternative loop using Formula 1 with a maximal time increase of
= 1:24 hours. Then, in the
above loop of four routes, which originally lasted 2 hours, the total loop duration can be increased by at
most 1:02 hours, instead of the original 1:48 hours.
The requirements are implemented by sequentially processing the routes of the original loop, and building
a graph of options, storing, at each node, the visited location and all available modes of transport. Figure 7
shows an exemplifying graph. The blue POI depicts the user’s home, where the transport modes walk,car,
bike and public transport (PT) are available. While in A) the algorithm suggests to take public transport to
the first POI, in B) the use of the car is suggested. In the first case, all consecutive route segments need to
be of type public transport or walk, as neither the car nor the bicycle are available anymore. In the second
case, taking the car would, in principle, force the second segment to be of either walk,car or public transport
type; however, since the car needs to return to the user’s home in the end, all modal choices except the car
are pruned after processing the last segment.
Figure 7: Example of computation for loop alternatives.
5. Results
To get a deeper insight in the methods described above and to validate them, we have interrogated the
participants of the here presented study. Having obtained a confirmation of their validity, we have then
analyzed the aggregated results of the performed computations, by estimating the overall potential for change
of the participants: such aggregated analyses can provide a (rough) upper bound of the potential effects of a
successful intervention.
5.1. Survey-based User Assessment
To assess the effectiveness and accuracy of the data analyses presented above, we performed two surveys,
targeting the users themselves. The first survey aimed at assessing the users’ perceptions about their overall
satisfaction throughout the project, and dedicated a few questions to investigating the quality and usefulness
of the individual eco-feedback reports. It was delivered to all participants, by means of an online questionnaire;
between 102 and 104 responses were collected for the subset of questions regarding the eco-feedback report,
equivalent to 39.5% of the sample of 261 study participants. This complete sample was recruited by means
of a public communication campaign, attracting around 700 voluntary people living in two Swiss regions
(the city of Zurich, a dense urban context characterized by high quality public transport provision, and
the Canton Ticino, a much less dense context, characterized by urban sprawl, where car is the dominant
transport mode). Filtering due to data incompleteness and for representativeness purposes led to the final
261 participants. Referring to a 7-point Likert scale (where 1 = “totally disagree” and 7 = “totally agree”),
we investigated the level of agreement of users with the following sentences:
The reports correctly identified my reference mobility patterns, in terms of percentage of use of the
means of transport:
M = 5.81, SD = 0.98 (n= 104);
The reports correctly identified my reference mobility patterns, in terms of systematic journeys:
M = 5.73, SD = 1.26 (n= 103);
The reports suggested realistic and feasible alternatives for my systematic journeys:
M = 4.31, SD = 1.47 (n= 102);
Overall, these responses show a definitely positive assessment by users, especially about the system’s
capacity of automatically identifying the means of transport and the systematic loops (for the sake of
simplicity, called “systematic journeys” in the survey questions). To get a deeper insight, we developed a
second survey, again administered by means of an online questionnaire, aimed at collecting the perception of
accuracy by each user on each single systematic loop we had identified and on the related alternatives found.
We obtained responses by 85 users, namely 32.6% of the overall sample of study participants (261). Users
were shown a map for each systematic loop we had identified, presenting the sequence of points of interest
defining each of them, and were asked (Q1) whether they had “traveled between the points shown in the
figure, for at least three times during the first monitoring period (March-April 2016)”. Possible responses
where Yes/No/Partially. Then, they were shown an updated map, also reporting route connections between
such POIs, and were asked a second question (Q2): “Would you classify this route as systematic (namely, a
route that you frequently travel)?” Possible responses ranged from 1 to 5, where 1 = “definitely no” and 5 =
“definitely yes”.
The collected answers refer to a total of 651 systematic loops, by 85 different users. Question Q1 obtained
responses for 550 systematic loops: 86% of them were “Yes” and 6.8% were “No”. This confirms a high
accuracy in the identification of repeatedly traveled loops. Moreover, from responses to question Q2 (again
obtained for 550 systematic loops), we got the confirmation that the frequent loops we had identified were
generally perceived as systematic: the mean value of Q2 responses was 3.98 (standard deviation = 1.40),
with 58% of the identified loops classified as definitely correct and 69% of them classified as either correct or
definitely correct (Likert scores equal to 4 and 5, see Figure 8). From the comments to questions Q1 and Q2,
we noticed that several users did not perceive those loops frequently traveled for shopping or leisure reasons
as systematic; others have judged loops as non-systematic because they have changed their routines, but
admitted that those loops where systematic during the monitoring period. This explains why the percentage
of positive answers to Q2 is smaller than for Q1.
For each loop for which a CO
-efficient alternative was automatically found, the questionnaire also
showed the user the alternative (consisting of the suggested route and the means of transport between the
same POIs). We identified 235 such alternatives for the 651 systematic loops (36.1%). Based on these 235
alternatives, we asked a final question (Q3): “Do you think the suggested route and means of transport are
plausible?”. Again, possible responses ranged from 1 to 5, where 1 = “definitely no” and 5 = “definitely yes”.
Overall, we received responses for 42 out of the 235 alternatives for systematic loops (17.9%). In this case,
the perceived accuracy by the users was lower, indicated by a mean value of 3.38 (SD = 1.62) in answers to
Q3. However, if we consider as plausible alternatives those assessed with a score of 4 or 5, they result in 50%
of the alternatives automatically found (see 8). A score of 3 (neutral assessment) is attributed to 17% of the
alternatives, thus leaving a 33% of alternatives that are assessed as not plausible. Due to the way the Q3
question was framed, these answers to Q3 could at least partially be affected by a “social desirability bias”
effect [
]. As a consequence, we acknowledge that the actual percentage of plausible alternatives might be
slightly lower than indicated by current figures, though we are not able to quantify how much.
Figure 8: Assesment provided by survey respondents for (a) each systematic loop and (b) each alternative to a systematic loop
we had identified, according to a 5-point Likert scale.
5.2. Analysis of the Overall Potential for Change
Finally, we analyzed the results obtained when applying the previously introduced methods to all the 261
users of our study. Although affected by the approximations necessarily involved in real data collection, these
results give the scale of potential energy savings that can be achieved by fostering changes in the mobility
behavior of comparable communities or groups of individuals. Taking into account the above limitations
related to the automatic identification of alternatives, it is important to note that the results presented below
should be interpreted as upper boundaries for the potential savings, which can be achieved only in the ideal
situation where no other constraints limit user choices beside those applied here to the travel duration and
mode availability. Further, plane trips have been removed from the collected data, as they strongly impact
Tot. number of users: 261
Tot. number of routes: 33’855
Tot. number of SL: 1’375
Average SL repetitions: 5.2
Tot. number of SL alternatives: 638
Original SL Alternatives SL Original NS Alternatives NS
Weekly distances 76.9 km 45.5 km 310.2 km 273.3 km
Weekly energy consumption 45.2 kWh 17.8 kWh 200.4 kWh 159.9 kWh
Weekly CO2emissions 8.6 kg 3.0 kg 39.1 kg 30.5 kg
Use of car 43.6 % 18.9 % 53.1 % 39.3 %
Use of public transport 32.3 % 40.2 % 33.6 % 43.3 %
Use of bicycle 12.5 % 25.0 % 4.2 % 7.2 %
Use of walking 8.1 % 13.1 % 5.9 % 7.4 %
Other 3.5 % 2.7 % 3.2 % 2.8 %
Table 2: Analysis of the mobility patterns and potentials for change over all users. SL stands for “systematic loops” while NS
stands for “non systematic” and refers to routes. Reported are the average values per user, if not indicated otherwise (e.g., the
average user travels 387.1 km (76.9 + 310.2) per week, but could potentially reduce this to 318.8 km (45.5 + 273.3) per week).
the overall CO
emissions obtained for the short monitoring period (six weeks), and at the same time can
hardly be substituted by any realistic alternative. Also, in the computation of alternative values, all routes
were considered, namely those included in systematic loops (for which a loop alternative was computed), and
those which could not be included in any loop (for which a route alternative was computed). This means
that for every route an alternative was computed and taken in the case it was found to be feasible (according
to the criteria introduced in Section 4.2).
Table 2 shows some global results obtained when applying the methods described above to the routes
collected for all 261 users monitored during the project. The first part of the table gives the total number
(tot) of users monitored, routes recorded and loops reconstructed, the average number (avg) of systematic
loops (SL) identified for each user and the average number of times each SL is traveled (SL repetitions).
One can see that on average, mobility patterns of a user can be characterized by about 5.3 systematic
loops, repeated about 5.2 times in a six week period. The second part of the table presents the distances
traveled, the energy consumed and the CO
emitted each week, reported as the mean (over all users) of the
average weekly values. All values are separately reported for systematic loops (SL) and for all the other
non-systematic routes (NS). The “alternative” columns compare these values to what could be achieved by
using the alternative solutions identified by our algorithms. Finally, in its third part, Table 2 analyzes the
overall modal choice, i.e., the fraction of the total distance (summed over all users) traveled with each mode
of transport. Figure 9 provides more detail about the distribution of such indicators among users, showing
box-plots for the modal choices and the average distances traveled, energy consumed and CO
emitted weekly.
Each row shows both the orig inally tracked data and the computed alternatives, considering the systematic
loops as well as all remaining non-systematic routes. The individual dots in the figure are outlier users, while
the boxes represent 50% of all users. Looking at the first rows, for example, one can see that the parts of
systematic loops that are traveled by car could be replaced by more sustainable means of transport in many
cases, while the non-systematic car routes have a much smaller potential for replacement (often due to the
fact that they cover long distances to places far away from public transport stops, preventing both the use of
a bicycle as well as public transport). It can be seen how a large part of the potential energy savings stem
from a transition from car to public transport, bicycles, and walking. Energy savings, however, can also
come from a decrease in the weekly traveled distance. These savings come from situations similar to the
example in Figure 2, where the detours of a public transport journey are replaced by a more direct and thus
shorter bicycle path.
Figure 9: Distribution of modes of transport, distances traveled, energy consumption, and CO
emissions for the actual (orig)
and alternative (alt) routes, both for systematic loops (sys) and non systematic routes (nonsys).
6. Discussion
As presented in the previous section, the user survey highlights that the systematic loops found were
generally of satisfactory quality. It is more difficult to propose meaningful alternatives to users, partially
shown by the fact that we “only” found 235 alternatives for 651 systematic loops, partially by the lower
score of Q3. There are multiple reasons why it is impossible to find suitable alternatives for all systematic
loops, but we would like to highlight two important ones here. First, as the loop length increases, it
becomes more difficult to find a suitable alternative, because each segment of the loop requires a suitable
alternative, and in sequence they must satisfy the mode availability constraints. This implies that finding
alternatives for individual routes is much easier than for complete loops. Second, in several surveys and
interviews we conducted, we found that our study sample is biased towards people who regard ecological
sustainability as a requirement for the future of humanity (pro-environmental attitude). Such people seem to
be naturally inclined to participate in experiments that try to make behavior more ecologically sustainable.
This attitude manifests in their systematically traveled loops, which are shorter and often covered more with
sustainable modes of transport than their non-systematic counterparts. In our two study regions, we found
that non-systematic routes are 3.4 (Zurich; urban) and 4.1 (Ticino; rural) times longer than routes appearing
in systematic loops. In Ticino, 74% of the routes of systematic loops are traveled by car, compared to 71% of
the non-systematic ones. In Zurich, however, 36% of the routes of systematic loops are covered by car, in
contrast to 51% of the non-systematic ones. This means that in general we have higher chances to find a
promising alternative for a non-systematic route.
To explain the lower score for the automatic identification of alternatives (Q3), one should consider
that our methods do not include many factors which may influence mobility choices and that the analyses
performed are necessarily affected by the inaccuracies of the tracking system. For instance, if a visited place
is not recognized properly by our method, the suggested alternative does not visit this place of actual interest
for the user. This in turn will cause the user to assess it as “not useful”. Moreover, users might have specific
constraints, related to the need for accompanying family members, for having to carry weights, or for being
capable of suddenly changing schedules and routes due to family or job reasons. Such specific elements are
highly user dependent and cannot be taken into account by the system. Therefore, even if theoretically
speaking a more efficient alternative from the point of view of CO
emissions is available to connect the
visited points of interest, specific needs of single users might lead to assess it as unfavorable and not realistic.
As this is highly dependent on the context of a user, it can also happen that someone classifies an alternative
as feasible, but still is not able to use the alternative in a majority of cases. While in the here presented
study we only looked at the overall CO
and energy savings, it would be interesting to analyze the individual
route choices of people over a certain time span. In a “living lab” without particular constraints on the
participants, observing such changes is difficult, though, as people change their places of living and work,
their personal circumstances, etc.
The eco-feedback provided only marginally accounts for individual differences between users. As mentioned
above, personal needs and preferences cannot always be recognized from automatic tracked data. As such,
some reasonable requirements constraining the modal choice have been assumed to apply indiscriminately to
all users. A more involved interaction with the users (e.g., asking people to state the purpose of each trip), as
well as improved tracking accuracy, would improve the accuracy of the suggestions and allow for some degree
of personalization of the feedback. In retrospective, a more accurate tracking app, allowing the inclusion
of travel survey techniques (e.g., asking for additional data about each trip) could have been beneficial,
in particular for the derivation of more meaningful alternatives. Relying on Moves not only discouraged
asking users for additional travel data, but also meant relying on the given accuracy for the identification of
points of interest, and thus recognizing of loops. Ultimately, these circumstances also influenced the upper
bounds presented in Table 2 and Figure 9, as the tracking inaccuracies can lead to points of interest not
being recognized, and thus not being respected in the computation of alternatives.
However, increasing the frequency of interactions between the mobility tracking app and its users would
need a careful assessment. In fact, in order to make smartphone-based tracking a viable solution for real world,
large-scale mobility monitoring, very little or no interaction with the user should be requested. This also
made it impossible to employ a “traditional” travel survey (where people manually enter traveled journeys
into a system). Additionally, in projects and studies aimed at assessing the effectiveness of behavior change
interventions in controlled trials, namely by comparing mobility patterns before and after an intervention, and
with respect to a control group, any interactions between the app and its users would potentially influence
their behavior and, thus, the outcome of the study. As shown by high drop-out rate registered through
the three phases of our project and by final interviews we performed with project participants, there is a
tangible risk that users feel under pressure by the interaction effort requested by the app and, to avoid it,
directly opt for quitting app use. Therefore, the availability of a smartphone-based tracking tool requiring
none or very limited interaction with the users would be largely beneficial also for other research purposes,
besides pure mobility tracking. This induces that the tracking system essentially includes a reliable transport
mode classifier, as this would remove the need of asking for the actual transport mode used (due to the
unavailability of a highly accurate classifier, in our study, users were asked to manually validate each recorded
The eco-feedback presented here was primarily delivered to users in terms of a static report as well
as with the use of several gamification elements. Especially the potential for change can be used as an
individual starting point for goal setting, concrete suggestions, or rankings of users, as these elements are
highly user- and context-specific (e.g., creating a leaderboard of “most eco-friendly” users would be heavily
biased towards people living in cities or having short journeys to work). Alternative or supplementary choices
to the above presented eco-feedback include monetary and educational (any impersonal information about the
ecological sustainability of certain travel behaviors) incentives. Within this study we focused on automatically
delivered personalized feedback for various reasons: monetary incentives are purely extrinsic motivators,
and are thus known to only show temporary effects [
]. Additionally, while educational incentives are
especially valuable for uninformed people, they show smaller effects for people who are already aware of a
certain problem, and are considering a change, though are still caught in a typical “attitude-behaviour” or
“value-action” gap [
]. As several surveys and interviews accompanying the study showed, our participants
were already inclined to optimize their travel behavior with respect to ecological sustainability, but did not
know how to improve it or lacked discipline in changing their behavior.
7. Conclusion and Outlooks
We have presented a method to analyze automatically tracked location trajectories and provide eco-
feedback to their users. The method builds upon the identification of systematic mobility patterns in user
trajectories and the suggestion of alternative modal choices for such frequent travels. In particular we have
focused on the identification of systematic loops starting and ending at a user’s home. We argue that these
loops build a solid foundation to compute eco-feedback, as they reflect the actual mobility needs of each user,
allow providing reasonable and feasible solutions, and offer a good level of abstraction to communicate to
users. For each loop, the proposed approach computes an alternative optimal solution that minimizes the
emissions while respecting a set of constraints (e.g., on the total duration of the travel) that can make it
acceptable to the average user. This approach can effectively be applied in other countries than Switzerland,
where we originally developed it, provided that country-specific
emissions factors for each transport
mode are considered, for example by retrieving them from the ecoinvent Life Cycle Assessment database [
The generated eco-feedback is mostly of educational nature, i.e., it does not create an incentive for a
person to change her mobility behavior, but simply shows possible ecological gains from such a change. Of
course, this assessment can be used as a base to create incentives (e.g., by building gamification elements on
top of it), thus making the suggestions provided to users more effective [
]. Alternatively, behavior change
could be fostered by pointing out benefits of modal changes. This can include time for other activities (such
as reading or working on a train) as well as economic aspects, ranging from simply showing the user how
much money she would save by performing a certain behavior to actively encouraging certain behavior by
travel vouchers, benefits, etc. In order to give people more meaningful travel alternatives and thus better
eco-feedback, however, more accurate tracking technology as well as more detailed data about the individual
journeys (e.g., the purpose, or additional constraints like luggage or people the user is traveling with) is
required. A challenge for future research activities is therefore to look for a closer collaboration with app
users, in order to better understand their mobility needs and receive a more detailed feedback about the
quality of loop recognition and the applicability of alternatives, while at the same time trying to limit the
amount of compulsory interactions with the app as much as possible.
Another line of research can concern the method itself. Transport options such as carpooling or car-
or bikesharing make viable alternatives for many mobility demands, which were not included in this work.
As the exact journeys people travel are known, an automated assessment of possible carpools and shared
vehicles would be possible. This could be used to create eco-feedback in the form of suggestions to work
together with other app users. Another extension could consider rescheduling user’s activities, in order to
create an optimal daily schedule in terms of mobility and sustainability. Again, this is highly dependent
on various individual factors (e.g., trip purpose, or interaction with other people’s schedules) which would
require a much stronger interaction of the user with the app.
Concerning the eco-feedback itself, its immediacy plays a big role in making it an effective tool for
fostering more sustainable mobility behaviors [
]. While in our case the feedback was delivered passively
after a certain period of time, the prompt recognition of a starting journey and the real-time suggestion of
an alternative could greatly increase its effectiveness. The main challenge in this real-time approach is to
anticipate the intentions (e.g., the destination) from the tracked trajectories of a user. This research area has
recently received a lot of attention though, and will increase in importance as location based services get
more and more prevalent.
This research was supported by the Swiss National Science Foundation (SNF)
within NRP 71 “Managing energy consumption” and is part of the Swiss Competence Center for Energy
Research SCCER Mobility of the Swiss Innovation Agency Innosuisse.
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... Since 2019, our Traffic4cast competition series at NeurIPS has contributed both methodological and practical insights, advancing AI-driven traffic forecasting and research on the general applicability of the resulting methods to predicting other spatial processes. Although such traffic forecasts are thought to form the basis for building and managing our cities to provide efficient and sustainable mobility (Bucher et al., 2019;Jonietz et al., 2018;Lee et al., 2018), this form of traffic prediction is still largely considered to be an unsolved problem (Guo et al., 2019a). In our first edition of Traffic4cast competition at NeurIPS 2019, we encouraged contestants to predict traffic flow volumes, velocities, and dominant flow directions 15 minutes into the future on a unique, large, real world data set (Kreil et al., 2020). ...
... • Anecdotal evidence stemming from the attempt by some contestants indicated that trying to add additional prior static or dynamic location knowledge about the spatial bins did not seem to improve results significantly (Martin et al., 2019;Herruzo and Larriba-Pey, 2020), suggesting that such information was already encoded in the complex traffic data itself. Moreover, no successful strategies in our competition used recurring traffic patterns based on time of day or day of week, which would have been possible in the competition design, providing further anecdotal evidence that traffic is 'quasi-markovian', i. e. mostly dependent on the immediate past only. ...
... • Most successful solutions indicated (Choi, 2019;Martin et al., 2019;Yu et al., 2019) that our discretized 'majority heading' channel was the least informative and was also hardest to predict. ...
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The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 10^12 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input.
... Trackintel 8 is a library for the analysis of spatio-temporal tracking data with a focus on human mobility. The core of Trackintel is the hierarchical data model for movement data [4] that is widely adopted in GIScience [9], transport planning [13] and related fields [45]. We provide easy-to-use and efficient functionalities for the full life-cycle of human mobility data analysis, including import and export of tracking data of various types (e.g, GPS trackpoints, location-based social network (LBSN) check-ins, call detail records), data model generation and preprocessing, analysis, and visualization. ...
... Trackintel focuses on the mobility of individual persons or objects (e.g., as opposed to crowd flows) and all functionalities are implemented as user-specific, based on unique user identifiers that link data to the respective tracked users. Trackintel is implemented in Python and is built mainly on top of Pandas [38] and GeoPandas [30] using accessor classes, a method to extend Pandas classes 9 . This design makes Trackintel easy to use for Python users and ensures its broad compatibility with other Python spatial analysis libraries. ...
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Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-source Python library for human mobility analysis. Trackintel is built on a standard data model for human mobility used in transport planning that is compatible with different types of tracking data. We introduce the main functionalities of the library that covers the full life-cycle of human mobility analysis, including processing steps according to the conceptual data model, read and write interfaces, as well as analysis functions (e.g., data quality assessment, travel mode prediction, and location labeling). We showcase the effectiveness of the Trackintel library through a case study with four different tracking datasets. Trackintel can serve as an essential tool to standardize mobility data analysis and increase the transparency and comparability of novel research on human mobility.
... Both the systems require a huge amount of investment, as the mobile travels to the particular destination [23] [24]. Normally, its future position is highly correlated with its personal mobility profile, the cells viewed currently, as well as the time spending in each position [15]. While positioning by neural networks, it is believed that each mobile has a mobility profile linked to the cell and the user's personal requirements. ...
... Furthermore, the q th layers are similar to m th feature map, the feature values in the location g; n ð Þ is determined using S q g;n;m as per Eq. (15). Likewise, the m th filter value is provided to the q th layer. ...
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In the modern world, it is necessary to monitor a user's mobility in everyday life to provide advanced mobile services. Mostly, the location-based services (LBS) depend on both the present and future location of the user, which shows the increasing demand on predicting the user's future location. The prediction of trajectories among two different locations is very important as it aids to optimize travel paths among the locations. Moreover, existing techniques rely on high-quality data to deliver optimal results. In this research, we proposed a new optimized deep learning architecture for mobility tracing. The prediction model is based on Optimized DCNN (Deep Conventional Neural Network) that is already trained with the traced location of the user and from the user’s trained movement the model can predict its next location. For precise prediction, the weight and activation function of DCNN is optimally selected with the aid of HS-EH (Hybrid SeaLion -Elephant Herding) algorithm, which is a hybridization of Sea lion Optimization (SLnO) and Elephant Herding Optimization (EHO). Eventually, the proposed method performance is executed in MATLAB 2019a and compared with state-of-art methods with certain performance metrics. Especially, the accuracy of the proposed DCNN + HS-EH algorithm at training rate 10 is 82.96%, 48.88%, 79.27%, and 61.67% better than existing methods.
... Triggering content at the right time and place and pushing notifications in terms of advisory service can increase the chances of engaging users in behavior change [50]. The findings are congruent with the research of [51,52], which shows that personalized eco-feedback and interventions toward environmental issues using location tracking can promote environmental behavior and alter users' attitudes toward the protection of the ecosystem. ...
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Environmental awareness refers to the understanding of the importance of protecting the natural environment. Digital technologies can play an important role in raising awareness of environmental issues. In view of this compelling need, this paper presents a novel way to promote environmental awareness with the use of smartphones. To achieve this, it employs personalization techniques, and specifically the Analytic Hierarchy Process (AHP) and PROMETHEE II. In more detail, the mobile application incorporates a user model that holds information, such as location (city, mountain, sea, etc.), age, interests, needs and indicators of waste management, economy of natural resources, general environmental protection, and biodiversity. At the first interaction of the user with the application, the user model is initialized; then, the system uses AHP and PROMETHEE II to provide personalized advice to users in order to help them raise their environmental awareness. The criteria, used to evaluate environmental advice, include the current location, living environment, habits, interests, needs, age, and seasonal suitability of the user. The novelty of this paper is the combination of AHP and PROMETHEE II for personalizing the environmental awareness using mobile technologies, taking into consideration the user profile as well as the surrounding area where the user is at the time that the advice is provided. The presented application has been evaluated regarding the system usefulness and environmental awareness. The findings indicate the high acceptance of this approach and its positive impact on users’ attitude and behavior with regard to reducing their environmental footprint.
... With regard to these numbers, it is clear that a quick decarbonization of the transport sector has to become a high priority in view of reaching the goals formulated in the Paris Agreement. Among the strategies to decarbonize the transport sector we find soft incentives targeting mobility behavior [24][25][26], policy interventions that use a top-down governing approach [27], and technological innovations such as synthetic fuels [28,29] or battery improvements that foster quicker transitions to electric vehicles [30]. While many of these proposals consider a wide range of different decarbonization measures, our study focuses only on BEVs as electrification is likely to be the main factor for the decarbonization of transport. ...
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The introduction of battery electric vehicles (BEV) and the expansion of rooftop photovoltaic (PV) power generation are both progressing at a fast pace to decarbonize the transport and the energy sector in Switzerland. These parallel developments have an enormous synergy potential as the actual decarbonization impact of BEVs depends heavily on the carbon footprint of the power source and the PV expansion requires local storage as a buffer to reduce negative impacts on the distribution grid. We present an empirical analysis based on a detailed 10-month data set of the charging and mobility behavior of 78 BEV users in Switzerland. It is combined with a fine-grained digital surface model of Switzerland to extract the detailed roof geometry and the corresponding rooftop PV generation capacity of each of the BEV owner’s houses. We test four different smart charging strategies with a varying degree of complexity and find that when charging uncontrolled (the strategy used during the study), BEV owners can only cover 15 % of their BEV’s demand using PV generated from the roofs of their own houses. A simple controlled charging approach greatly increases the average coverage to 56 % and up to 90 % or 99 % when using an optimized charging strategy without or with a home battery storage. All charging strategies ensure that the individual mobility behavior of the BEV owners is not affected. We further show that using rooftop PV power generation for BEV charging has a large potential to further decrease the climate impact of BEVs and propose simple adjustments to consider in charging strategies that help to increase the owners’ PV consumption.
... Following a general approach also suggested by Welsh et al. (2014), and specifically the methodology and tools proposed by Bucher et al. (2019), Bellidea relied on an already existing activity tracking app named Moves (now discontinued), which tracked all basic mobility data, organized them in 'routes' and 'activities' (segments of a route that are travelled by the same transport mode), and automatically detected the transport mode used for each activity (walking, cycling and general 'motorized transport'). Data collected by Moves where then automatically accessed by Bellidea, which then processed them by an on-purpose developed random decision forest algorithm, to further classify the 'motorized transport' mode. ...
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App-based persuasive technologies emerged as promising tools to promote sustainable travel behavior. However, the opt-in, self-selection framework characterizing their use in real-life conditions might actually lead to wrongly estimate their potential and actual impact in analyses that do not rely on strict randomized controlled trials. To investigate evidence of such biases, we analyze mobility data gathered from users of a persuasive app promoting public transport and active mobility launched in 2018 in Bellinzona (Switzerland). We consider the users' baseline mobility data: km per day (total and by car) traveled during the app validation period, when behavior change motivational features were not enabled. To estimate the possible self-selection bias, we compare these data with the reference population, using data from the Swiss Mobility and Transport Census; to study the possible attrition bias, we look at the relations between baseline mobility and the number of weeks of app's active use. We find evidence of neither self-selection nor critical attrition biases. This strengthens findings by earlier non RCT-based analyses and confirms the relevance of app-based persuasive technologies for mobility behavior change.
The use of smartphone apps has numerous advantages for app providers and users. However, the users of many smartphone apps are confronted with a trade-off between usage benefits and preferences for personal data protection. We investigate the acceptability of data sharing in different vignettes describing five types of these apps from key industries of the digital transformation. The vignettes are embedded in a representative survey for the German population (N=1,013). For each vignette we examine to what extent the acceptance of data sharing differs with respect to potential recipients, collected information attributes, and the promoted benefits of data sharing. We differentiate the promoted benefits in two treatments according to monetary (or personal) and environmental (or public) benefits. Our results show no treatment effects but significant differences in acceptance values for different recipients and information attributes. We further observe that participants with stronger green consumption values, participants with a stronger risk propensity, men, and younger participants show a higher acceptance towards data sharing in the described scenarios.
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In the 21st century, two main challenges for economic research are to propose effective solutions to shape the digital transformation and mitigate human-induced climate change. Research on digital transformation is closely linked to various privacy-related issues, which mostly relate to the preferences and decisions of individuals. In contrast, climate change research examines which factors impede effective cooperation among multiple individuals and investigates how common goals, such as limiting climate change, can be achieved. The link between economics of privacy and environmental economics is that many digital technologies have the potential to generate positive externalities that can contribute to the provision or maintenance of public goods. However, in many cases these digital technologies are characterized by the fact that their use requires the disclosure of personal information. The potential success of these technologies and institutional mechanisms therefore largely depends on social acceptance towards these technologies and institutional mechanisms. Each paper in this cumulative dissertation contributes to the broader question of how economic experiments can contribute to evaluate and potentially increase the efficiency of institutions and technologies that can provide or maintain public goods. The first paper investigates whether the publication process of journals in the field of experimental economics can potentially be improved. The remaining five papers focus directly or indirectly on different but related public goods problems which are closely linked to privacy or environmental issues. Methodologically, the six papers share the feature that they either directly apply the experimental method for their individual research questions or use the results of experimental literature to derive hypotheses and explain empirical outcomes in specific privacy-related contexts. In the field of privacy, the dissertation identifies factors that influence data sharing in several smartphone apps from key industries of the digital transformation and on employer review platforms. In the area of environmental economics, the first paper proposes an institutional mechanism that can increase the willingness to contribute to recycling systems, and the second paper shows that the ability to exploit a public good can impede cooperation to mitigate climate change.
Conference Paper
The IARAI Traffic4cast competition at NeurIPS 2019 showed that neural networks can successfully predict future traffic conditions 15 minutes into the future on simply aggregated GPS probe data in time and space bins, thus interpreting the challenge of forecasting traffic conditions as a movie completion task. U-nets proved to be the winning architecture then, demonstrating an ability to extract relevant features in the complex, real-world, geo-spatial process that is traffic derived from a large data set. The IARAI Traffic4cast challenge at NeurIPS 2020 build on the insights of the previous year and sought to both challenge some assumptions inherent in our 2019 competition design and explore how far this neural network technique can be pushed. We found that the prediction horizon can be extended successfully to 60 minutes into the future, that there is further evidence that traffic depends more on recent dynamics than on the additional static or dynamic location specific data provided and that a reasonable starting point when exploring a general aggregated geo-spatial process in time and space is a U-net architecture.
Conference Paper
The IARAI Traffic4cast competition at NeurIPS 2019 showed that neural networks can successfully predict future traffic conditions 15 minutes into the future on simply aggregated GPS probe data in time and space bins, thus interpreting the challenge of forecasting traffic conditions as a movie completion task. U-nets proved to be the winning architecture then, demonstrating an ability to extract relevant features in the complex, real-world, geo-spatial process that is traffic derived from a large data set. The IARAI Traffic4cast challenge at NeurIPS 2020 build on the insights of the previous year and sought to both challenge some assumptions inherent in our 2019 competition design and explore how far this neural network technique can be pushed. We found that the prediction horizon can be extended successfully to 60 minutes into the future, that there is further evidence that traffic depends more on recent dynamics than on the additional static or dynamic location specific data provided and that a reasonable starting point when exploring a general aggregated geo-spatial process in time and space is a U-net architecture.
Conference Paper
Full-text available
The ability to travel long and short distances at any time enables a high degree of individual flexibility on the one hand, but it also results in a number of adverse effects, including massive impacts on traffic flow and a constantly growing lack of space. Increasing emission levels (CO2, NO2) and the associated fine dust pollution are to be considered serious barriers for meeting internationally agreed targets on environmental preservation; they will also negatively affect the health of city residents. As a result these factors will increase community and state expenditure (through accidents, parking tickets and so on) as a direct result of insufficient sustainable and future-oriented individual traffic behaviour. In course of the state funded project "Traces" we aim to tackle these challenges by promoting sustainable mobility in the urban area using a contextual gamification framework. During the past year behavioural theories have been combined with state-of-the-art ICT technologies in order to create an immersive playing experience. During the game players will be motivated to choose from a variety of inter-and multimodal mobility options. The gamification framework uses a pervasive gaming approach with location-based elements for changing behavioural patterns surrounding individual mobility choices. The main goal is to achieve a change in long established behavioural patterns, demonstrate feasible alternatives and establish an authentic gaming experience, creating an incentive to use inter-and multimodal mobility forms (persuasive design). Through the integration of modern ICT technologies target groups will be addressed in real life conditions. The bi-directional transfer between the virtual and real game world will be additionally enhanced by offline-campaigns in urban space (pervasive design). In order to achieve our objectives, the game concept as well as the mobile application demo will be developed within living laboratories (labs) in order to stay as close as possible to the end-users' needs. The basic game mechanics, functionality and usability of the game framework are optimized following an iterative design process. Additionally, experts will be involved in dedicated workshops for enabling a professional integration of best-practice experiences. The project results are constantly evaluated and will lead to an action plan for promoting and enhancing inter-and multimodal mobility as well as an impact analysis of the behavioural and cognitive effects.
Full-text available
Mobile phone applications to monitor and influence one's behavior are numerous. Most developed appear to be health applications but in the past decade, " persuasive technology " has also been leveraged and applied to promote sustainable travel behavior. We discuss the health applications and review and evaluate existing behavior change support systems (BCSS) designed to promote sustainable travel behavior. We extract the persuasive features embedded in these systems and evaluate their persuasive potential by using the persuasive systems design (PSD) model that has been used to evaluate BCSSs in the health domain. Our evaluation reveals that some features crucial for successful travel behavior change, such as tunneling, rehearsal and social facilitation, are missing. Furthermore, we assess studies conducted to evaluate the effectiveness of these BCSSs in changing behavior and find indications that effect sizes are mostly small though methodologically robust studies are largely missing and hence no definitive conclusion yet can be derived. Based on these findings as well as literature related to public health where BCSSs appear to be further developed, we then derive three important suggestions on research needs and applications for further development of BCSSs in the transport policy realm.
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Current popular multi-modal routing systems often do not move beyond combining regularly scheduled public transportation with walking, cycling or car driving. Seldom included are other travel options such as carpooling, carsharing, or bikesharing, as well as the possibility to compute personalized results tailored to the specific needs and preferences of the individual user. Partially, this is due to the fact that the inclusion of various modes of transportation and user requirements quickly leads to complex, semantically enriched graph structures, which to a certain degree impede downstream procedures such as dynamic graph updates or route queries. In this paper, we aim to reduce the computational effort and specification complexity of personalized multi-modal routing by use of a preceding heuristic, which, based on information stored in a user profile, derives a set of feasible candidate travel options, which can then be evaluated by a traditional routing algorithm. We demonstrate the applicability of the proposed system with two practical examples.
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This paper presents results from a longitudinal study aimed at evaluating the effectiveness of different behavior change strategies for supporting sustainable transport choices in urban areas. We provided eight users with a mobile app deploying goal-setting, self-monitoring, rewards, social and sharing features and observed a positive impact of the app on users' behavior and environmental awareness.
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How can we encourage people to engage in more sustainable mobility lifestyles, reducing car use? Taking advantage of the wide availability of smartphones, we overcome the traditional awareness-raising approach and exploit eco-feedback, social norms and peer pressure elements in an ICT-based motivation system. We developed two smartphone Apps, which are currently being tested in a real-life, large-scale living lab experiment. The GoEco! Tracker App monitors the mobility patterns of the participants, identifying the routes they travel and the means of transport they use, and it is primarily meant to collect baseline data. Exploiting individual achievement and competition game mechanics, the full GoEco! App additionally nudges users towards personal goals for change and engages them in individual and collective challenges, strengthening competition with a “hall of fame” section. In this paper we introduce the GoEco! Apps and their theoretical eco-feedback and gamification framework, describe their key functionalities and comment on the main strengths and limitations after one month of large-scale testing of the GoEco! Tracker App.
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The present urban transportation system, mostly tailored for cars, has long shown its limitations. In many urban areas, alternative and effective transport modes are already available, ranging from well-established systems such as public transportation and slow mobility networks to emerging alternatives like vehicle-sharing systems. However, these transport modes still tend to be neglected due to a deep-rooted car dependency. How can we encourage people to use them? In the Swiss-based GoEco! project we overcome the traditional awareness-raising approach. We develop a set of two smartphone Apps leveraging eco-feedback and game elements and create a medium to large-scale “living lab” experiment to test their effectiveness in motivating people to modify their mobility behaviour. The GoEco! living lab is developed in two contexts differing both in the supply of mobility options and in the socio-cultural attitude of the population towards mobility: the City of Zurich and the Canton Ticino region. The experiment envisions three mobility tracking periods: the first one to identify the reference mobility patterns, the second one to identify the nudged mobility patterns, under the direct effect of the GoEco! App, and the last one, one year later, to assess long‐term behaviour change towards less car-dependant mobility styles. Focus groups and semi‐structured interviews with randomly selected participants will provide us with additional qualitative insight on the users' perceptions and attitudes. After an introduction the GoEco! living lab experiment and methodological approach, we present preliminary insights on the data collected during the first mobility tracking period.
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The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.
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Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI databases for such data, and the ecoinvent database is the largest transparent unit-process LCI database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the database, set the foundation for a truly global database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments. Methods Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the database, and global background data was added, often as an extrapolation from regional data. Results and discussion In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions. Conclusions With version 3, the ecoinvent database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding ecoinvent from a mostly regional into a truly global database and offers many new insights beyond the thousands of new and updated datasets it also introduced.
We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.