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Vision Paper: Using Volunteered Geographic Information to Improve Mobility Prediction

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Fine-grained real-time movement prediction is becoming increasingly important, with smartphones and vehicles constantly tracking our position and trying to guess our next location to timely provide us with recommendations, traffic forecasts, or driver assistance. Depending on the tracking accuracy, the recorded locations are first mapped to street segments, using a mobility model to choose the most likely road in case of ambiguities. The main prediction procedure uses a similar movement model (possibly incorporating additional user-specific data) to assess likely future travel choices. While the exact street topology is not essential on a very high level (e.g., when predicting the "next place" someone is going to be), it becomes more and more important if we try to predict the exact position of a person or vehicle. Similarly, different data sources (such as points of interest, land use zones, or building footprints) should be used for predictions at different levels of accuracy. In this paper, we assess current research trends concerning various types of volunteered geographical information (VGI), how this data can be used in different models to compute mobility predictions , and we present our vision for an integrated system that is able to use crowdsourced geographic data to perform mobility prediction at different levels.
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Vision Paper: Using Volunteered Geographic
Information to Improve Mobility Prediction
Dominik Bucher
ETH Zurich, Institute of
Cartography and Geoinformation
Stefano-Franscini-Platz 5
Zurich, Switzerland 8093
dobucher@ethz.ch
ABSTRACT
Fine-grained real-time movement prediction is becoming increas-
ingly important, with smartphones and vehicles constantly track-
ing our position and trying to guess our next location to timely
provide us with recommendations, trac forecasts, or driver as-
sistance. Depending on the tracking accuracy, the recorded loca-
tions are rst mapped to street segments, using a mobility model
to choose the most likely road in case of ambiguities. The main pre-
diction procedure uses a similar movement model (possibly incor-
porating additional user-specic data) to assess likely future travel
choices. While the exact street topology is not essential on a very
high level (e.g., when predicting the “next place” someone is going
to be), it becomes more and more important if we try to predict
the exact position of a person or vehicle. Similarly, dierent data
sources (such as points of interest, land use zones, or building foot-
prints) should be used for predictions at dierent levels of accu-
racy. In this paper, we assess current research trends concerning
various types of volunteered geographical information (VGI), how
this data can be used in dierent models to compute mobility pre-
dictions, and we present our vision for an integrated system that
is able to use crowdsourced geographic data to perform mobility
prediction at dierent levels.
CCS CONCEPTS
Applied computing Transportation;Information sys-
tems Location based services; Global positioning systems;
KEYWORDS
ACM proceedings, crowdsourcing, volunteered geographic infor-
mation, mobility, prediction
ACM Reference format:
Dominik Bucher. 2017. Vision Paper: Using Volunteered Geographic Infor-
mation to Improve Mobility Prediction. In Proceedings of PredictGIS’17:1st
ACM SIGSPATIAL Workshop on Prediction of Human Mobility , Redondo
Beach, CA, USA, November 7–10, 2017 (PredictGIS’17), 4 pages.
https://doi.org/10.1145/3152341.3152344
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©2017 Association for Computing Machinery.
ACM ISBN 978-1-4503-5501-8/17/11.. .$15.00
https://doi.org/10.1145/3152341.3152344
1 INTRODUCTION
Nowadays, most people carry a smartphone capable of determin-
ing its position via GSM, GPS or WiFi wherever they go. Several
applications already use these location measurements to passively
record the movement of people, for example to provide them with
feedback on their mobility behavior [1, 2, 20], to timely notify them
about upcoming appointments (at another location) [32], to give
location recommendations [38], or to warn them about trac inci-
dents. However, these high-level applications often only consider
the momentary location of a user. If they do mobility prediction,
they usually do not take the exact path someone might travel in
the near future into account, but instead consider the transition
probabilities from the current location to previously visited points
of interest (such as home, workplace, or the parents’ home).
In a similar manner, most vehicles are equipped with GPS sen-
sors, and recent trends in autonomous driving add cameras, radars
and laser measurement devices for environment surveillance and
position tracking. Highly accurate measurements from these sen-
sors make it possible to predict future positions of a vehicle, in
particular when combined with high-resolution maps of roads.
Between simply predicting the “next place” a person is going
to be and predicting the exact position of a vehicle, there is path
prediction. Several applications could benet from being able to
predict mobility on such an intermediate level. For example, due
to the recent trend towards increasingly multi-modal mobility be-
havior [23], it would be great to notify people about alternative
travel options along their traveled path, such as other cars driving
the same route (for carpooling), trains about to leave, or bikeshar-
ing stations with bikes available. Another prominent application
concerns all sorts of advertising (for restaurants, gas stations, etc.),
which requires a high prediction accuracy to timely suggest up-
coming places. Accurate mobility prediction is also valuable when
location tracking becomes unavailable, for example inside build-
ings, tunnels, or urban canyons.
In this paper, we summarize current research trends from the
various elds involved in dierent levels of mobility prediction,
and present ideas and suggestions where these elds could be headed
in the future. We conclude by envisioning an integrated system
that is able to predict mobility at various levels of detail, for any
future point in time.
PredictGIS’17, November 7–10, 2017, Redondo Beach, CA, USA Dominik Bucher
2 VOLUNTEERED GEOGRAPHIC
INFORMATION
Arguably, volunteered geographic information (VGI) [14] is best
known for its use in open mapping applications, most notably Open-
StreetMap [13]. Next to the road network, OpenStreetMap con-
tains data about all kinds of spatial objects, e.g., points of inter-
est, land use zones, public transport routes, or bikesharing stations.
Goodchild [11] notes that not only is volunteered geographic infor-
mation relatively cheap to produce, but that it also is available to
almost everyone, which makes it an interesting base for the com-
putation and improvement of mobility predictions.
2.1 Road Network Data
Traditionally, road networks are formalized as graphs G=(V,E),
connecting vertices viVwith edges (vi,vj) ∈ E. Research
on autonomous vehicles recently initiated a shift towards high-
resolution maps [30, 35]. While street data has been collected using
GPS sensors in cars for a while now, we can expect high-resolution
maps for a large portion of the road network in the near future, be-
cause more and more cars are being equipped with high-precision
data collection tools, as these are required for elaborate forms of
driver assistance. Though most data collection currently happens
within proprietary bounds, it can be safely assumed that similar
tools will be freely available soon.
Thus, it will be essential to provide a repository for volunteered
high-resolution maps. Such a repository will require new data stor-
age formats, where road network vertices and edges are enhanced
by their exact extents and other street peculiarities. This will even-
tually allow very ne-grained movement predictions, which are
especially important for all forms of autonomous mobility.
In a similar manner, as indoor tracking improves and measuring
of indoor spaces (e.g., using light detection and ranging (LIDAR)
systems) becomes widely feasible, VGI services for storage and re-
trieval of indoor data should be developed and provided [10].
2.2 Other Geographic Data
Most prediction algorithms work on clustered user location his-
tories (GPS trackpoints, social media checkins, etc.), and do not
take into account other geographical information (except the road
network). Aggregated over all users, such location histories allow
predicting the movement of other users in similar situations [15].
While these algorithms show good prediction accuracies, they are
not able to predict the exact path someone might take, which is de-
pending on the (geographical) context a person is in. For example,
a bicycle rider is more likely to choose a less dangerous road along
a park, a street with a smaller inclination, or to take a bus in case
of rain, even though this might prolong the travel duration signi-
cantly [cf. 3]. Such geographic data is not necessarily available in a
graph-based form, but can consist of vector or raster data, e.g., de-
picting landuse data, marking dangerous spots or air quality. These
heterogeneous data sources either need to be integrated into exist-
ing models of mobility (e.g., using a spatio-temporal form of map
algebra [19]), or new models should be developed that incorporate
contextual geographical information.
2.3 Real-time Data
Fine-grained mobility prediction will also largely be driven by real-
time data. Vehicular ad-hoc networks (VANET) allow vehicles to
share information about their movement and surroundings, and to
coordinate future movement [24]. Adding real-time data about the
trac state, potential obstacles (such as construction sites or acci-
dents), and special events to VANET data will strongly inuence
the mobility behavior of people and vehicles. As such, prediction
models need to be able to quickly adapt to new circumstances, i.e.,
they need to be continuously updated.
3 MOBILITY PREDICTIONS
The location prediction problem can be formulated as follows. Given
a movement trajectory
T=⟨(lon1,lat1,t1),(lon2,lat2,t2), . .., (lonn,latn,tn)⟩
consisting of time-stamped coordinates, we want to nd the future
path of a user, i.e., (lonm,l atm,tm)for some m>n(resp. tm>tn).
High-level movement prediction associates points of interest with
location tuples (lon,lat )and is interested in a future sequence of
points of interest. This requires clustering of user locations, which
is usually done either grid-based or using some neighborhood- or
density-based approach. For ne-grained path prediction we usu-
ally associate street intersections (i.e., road network vertices) with
a location tuple, and interpolate on the roads between. This means
that we are not strictly interested in a sequence of future locations,
but rather a function f(T,t)which assigns a location (lon,lat )to
every t>tn. In practice, most algorithms yield a tree of possible
future paths, where each edge is associated with a certain probabil-
ity that the person travels this route. f(T,t)would simply assign
the location of the most probable path to time t.
A very closely related eld is map matching, which uses similar
mobility models to assess which roads a person most likely took. In
the following sections, we will refer to both movement prediction
as well as map matching.
3.1 Motion Functions and Heuristic Methods
On a very low level, linear motion functions describe the move-
ment of physical objects well. While not directly applicable to pre-
dictions in a road network (due to the fork dilemma, which de-
scribes the necessary turn at a T-shaped intersection [18]), they
are commonly used for indexing moving objects in databases [29].
However, with respect to the increase in location recording fre-
quencies (e.g., in autonomously driving cars), it becomes necessary
to integrate motion functions with a higher level of mobility path
prediction.
Heuristic-based mobility models (and expert systems) are usu-
ally used in routing applications, where parameters (e.g., for stop
times at trac lights, left turn penalties, or acceleration) are man-
ually set, as no information about the application user is known
and the resulting route should be optimized for duration [e.g. 26].
Such models should form a basis upon which learning models can
build, e.g., by learning user-dependent parameter values.
Volunteered Geographic Information for Mobility Prediction PredictGIS’17, November 7–10, 2017, Redondo Beach, CA, USA
3.2 Markov Models and Machine Learning
A large body of literature covers the prediction of “next places”,
i.e., important spots in either a person’s life or within a certain geo-
graphical area [e.g., 5, 9, 21, 33, 39]. This focus on individual points
of interest (POIs) has several reasons: Knowing about stops at im-
portant places provides a sucient base for many applications, as
they are primarily interested in performing actions related to POIs,
and not the paths between them. In addition, many datasets are
available in such a “clustered” format, be this due to restrictions
during the data collection (e.g., mobile phone usage that is strictly
linked to cell tower ids), or due to privacy reasons (clustering user
locations into grid cells, inter-user clusters, etc.). Finally, focusing
on POIs also reduces the computational complexity of the prob-
lem, and conceptually maps very well to state-based models (e.g.,
Markov models, where each state corresponds to a single point of
interest). A variety of machine learning methods have been applied
to the next place problem, such as Markov models, Support Vector
Machines and decision trees [28].
When looking at more ne-grained mobility prediction, we nd
applications of similar methods as above (where each road junction
is treated like a point of interest or the trackpoints are aggregated
into cells) [22, 31, 37, 39]. When predicting a future route, these net-
work mobility models usually resemble graph-based routing with
adapted edge weights [18]. The same models are often also used
for map matching [27], where ambiguous trackpoints need to be
associated with a single road segment.
A less common approach is the use of lters for short-term mo-
bility prediction. Kalman or particle lters have been applied suc-
cessfully to location prediction in vehicular ad-hoc networks [17].
In contrast to above methods, they are able to predict positions of
objects on a very ne-grained level, as they do not have to consider
cells or location clusters.
3.3 Deep Neural Networks
Many applications of neural network models to mobility predic-
tion are based on a similar “next place” problem denition [6, 7, 12,
25], reaching accuracies around 60% [7]. Liu et al. [25] incorporate
spatial and temporal context by adding time- and distance-specic
transition matrices. This is closely related to the research by We
et al. [36], which pays special attention to keep road and context
information, and to enable processing of long location sequences.
Other works in the eld of neural networks for the processing of
spatio-temporal data include movement pattern extraction for vi-
sual analysis [4] and the combination with spatio-temporal graphs
that encode high-level expert knowledge [16].
While the work by Jain et al. [16] is not directly related to human
mobility prediction within a city or geographical area, studying
spatio-temporal dynamics of humans is a hot topic in computer
vision and neural networks research [8, 34].
We expect future research on the use of neural networks for mo-
bility prediction to increase, as neural networks are well suited to
process spatial data. Convolutional networks are commonly used
to detect patterns in images, which could directly be used to pro-
cess geographical raster data, or to assess rasterized street net-
works.
4 DISCUSSION AND OUTLOOK
Figure 1 summarizes the dierent parts required for mobility pre-
diction as presented in this paper. As mobility predictions at level 3
(considering sequences of points of interest) have mostly been in
the focus of previous research, there is a wide variety of methods
available which have also been successfully deployed in commer-
cial applications (e.g., Google Maps notifying you when it is time to
leave work to be home on time). These high-level predictions usu-
ally do not make heavy use of volunteered geographical informa-
tion, except to train models across users, e.g., by using Foursquare
checkins to learn common movement patterns (“after having din-
ner at a restaurant, people commonly go home”).
We expect the lower two levels to be more in the attention of re-
searchers in the near future, not only because the increased amount
of data and increased tracking accuracies allow us to train more
ne-grained models, but also because new forms of mobility re-
quire path predictions (e.g., for real-time multi-modal transport
suggestions), in the case of autonomous mobility even the pre-
diction of the exact position on the road (both for the vehicle it-
self, as well as for any other moving object in its vicinity). While
the predictions at level 1 are mostly based on physical models of
movement (however, taking into account restrictions as given by
high-resolution maps), level 2 has to incorporate the biggest wealth
of volunteered geographic information: street data, landuse zones,
real-time trac updates, and so on.
We ultimately envision a system (based on future research in
the eld) that integrates the dierent levels of mobility prediction,
and thus can be used to compute a set of probable locations for any
given time in the future.
Acknowledgements. This research was supported by the Swiss
National Science Foundation (SNF) under the National Research
Program NRP71 “Managing Energy Consumption” and by the Com-
mission for Technology and Innovation (CTI) within the Swiss Com-
petence Center for Energy Research (SCCER) Mobility.
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