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Development of a Novel Evaluation and Benchmarking Standard

Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
D4.1 Development of a Novel Evaluation and
Benchmarking Standard
DATA science for SIMulating the era of electric vehicles
Project reference: 270833
Status: Execution
Programme acronym: FP7-ICT (FET Open)
Subprogramme area: ICT-2009.8.0 Future and Emerging Technologies
Contract type: Collaborative project (generic)
1. (UHasselt) Universiteit Hasselt
2. (CNR) Consiglio Nazionale delle Ricerche
3. (BME) Budapesti Muszaki es Gazdasagtudomanyi Egyetem
4. (Fraunhofer) Fraunhofer-Gesellschaft zur Foerdering der Angewandten Forschung E.V
5. (UPM) Universidad Politecnica de Madrid
6. (VITO) Vlaamse Instelling voor Technologisch Onderzoek N.V.
7. (IIT) Technion Israel Institute of Technology
8. (UPRC) University of Piraeus Research Center
9. (HU) University of Haifa
Prof. dr. Davy Janssens
Universiteit Hasselt Transportation Research Institute (IMOB)
Function in DATA SIM: Person in charge of scientific and technical/technological aspects
Address: Wetenschapspark 5 bus 6 | 3590 Diepenbeek | Belgium
Tel.: +32 (0)11 26 91 28
Fax: +32 (0)11 26 91 99
Work Package: 4
Deliverable: D4.1
Dissemination level: xxx
Nature: xxx
Contractual Date of Delivery: 31.08.2013
Actual Date of Delivery: 31.08.2013
Total number of pages: 42
Authors: Daniel Schulz, Bruno Kochan, Christine Kopp, Michael May, Luca Pappalardo, Nikos Pelekis,
Salvatore Rinzivillo, Filippo Simini, Marios Vodas
Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
This document contains the first deliverable of WP4 (D4.1). It represents the results of the DATASIM project
in WP4 during Year 2.
This report is DATASIM Consortium 2012. Its duplication is restricted to the personal use within the
consortium and the European Commission.
Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
1 Introduction ..................................................................................................................................... 5
2 Evaluation of Spatio-temporal Microsimulation Systems................................................................ 8
2.1 Introduction .............................................................................................................................. 8
2.2 Properties of Mobility Data ...................................................................................................... 9
2.2.1 Dimensions of Mobility Data .......................................................................................... 10
2.2.2 Observation Space ........................................................................................................ 10
2.2.3 Sampling Coverage ....................................................................................................... 11
2.2.4 Resolution ...................................................................................................................... 13
2.2.5 Missing Data .................................................................................................................. 13
2.3 Data Input and External Model Validation ............................................................................. 14
2.3.1 General Measures for Comparing Categorical and Numerical Variables ..................... 14
2.3.2 Evaluating the Distribution of Movement Positions ....................................................... 16
2.3.3 Evaluating the Distribution of Differences between Movement Positions ..................... 18
2.3.4 Evaluating Sequential Dependencies between Movement Positions ........................... 19
2.3.5 State-of-the-Art of External Model Validation ................................................................ 20
2.4 Internal Model Validation ....................................................................................................... 21
2.4.1 Validation on the Level of Model Components .............................................................. 21
2.4.2 Validation of Model Variability ....................................................................................... 22
2.4.3 Example of a Model Components Validation ................................................................ 22
2.5 Validation Use Cases ............................................................................................................ 25
2.5.1 Evaluating GPS and GSM Data in the Region of Pisa .................................................. 25
2.5.2 Evaluating Mobile Phone Data in the Region of Lausanne ........................................... 29
2.6 Conclusion ............................................................................................................................. 31
3 On Benchmarking a Trajectory DBMS .......................................................................................... 32
3.1 Introduction ............................................................................................................................ 32
3.2 Related Work ......................................................................................................................... 32
3.3 Overview of the Benchmark Architecture ........................................................................... 34
3.3.1 Storage Model ............................................................................................................... 35
3.3.2 Partitioning Scheme....................................................................................................... 37
3.3.3 Queries .......................................................................................................................... 37
3.4 Roadmap ............................................................................................................................... 38
4 References .................................................................................................................................... 40
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Programme - Contract ICT-FP7-270833
Table 1: Example of a confusion matrix ............................................................................................... 15
Table 2: Predictive performance of discrete (D) and continuous (C) decision trees ............................ 24
Table 3: Comparison of average daily travel distances (in km) ............................................................ 30
Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
1 Introduction
The goal of deliverable D4.1 is to provide a novel, better and more detailed standard for the evaluation
and benchmarking of agent-based spatio-temporal microsimulation systems. In this spirit we
performed various kinds of validations of mobility models, simulation models and mobility sets of
different sources (e.g. GPS, GSM, frequency counts). The deliverable is organized according to these
Chapter 2 describes the validation of spatio-temporal microsimulation systems. The main challenge for
a correct and meaningful evaluation is posed by the inherent complexity of movement data. The
combination of spatial, temporal and population variables allows to derive a nearly unlimited number of
mobility characteristics. State-of-the-art evaluations, however,rely on partial evaluations of only a few
selected mobility characteristics. For example, comparing the simulation results with existing traffic
counts (Gao et al., 2010; Horni et al., 2009; Meister et al., 2010), and thereby constraining the view on
frequencies at several locations, is still the dominating approach. The increasing application range of
spatio-temporal microsimulation systems requires a holistic view on validation, able to evaluate many
different aspects of movement behavior. For example, many different parameterizations of a
simulation model would be able to generate routes that can pass the test of comparing frequencies at
a sparse set of locations, although the underlying trajectories and flow structures of its agents are very
different. Therefore, tests on frequency counts alone would not be sufficient, for example, when being
interested in main directions of flow.
Chapter 2 assembles available validation techniques and provides an overview on the state-of-the-art
of validating spatio-temporal microsimulation systems. It collects and structures various additional
aspects that have to be considered for the validation and comparison of movement data. In particular,
in terms of processes, or phases, we differentiate validation of the input data, validation of the internal
model as well as validation of the external model. The validation of input data is crucial because
incorrect or missing values in the input data usually are propagated and emphasized through the
simulation processes. Only high-quality input data leads to reliable simulation results. Internal model
validation indirectly addresses the issue of how well the “inner” working of the model is “consistent with
reality”. This is measured by testing how well the model is able predict the given input data. Finally,
external model validation confronts the model results with external data and determines the final
quality of the model results. The chapter concludes with examples of using big data sources for the
extraction and validation of movement characteristics. More details on the validation are given in the
appended papers. Section 2 is a joint research effort by WP4 partners and will be published as book
chapter within the “Data on Science and Simulation in Transportation Research” book.
Chapter 3 addresses a system point of view by referring to the moving object database (MOD). In the
recent past, several DBMS-based solutions for storing and efficiently querying historical trajectory data
were already proposed in the form MODs, (Almeida et al., 2006) (Pelekis et al, 2008) (Pelekis et al,
2011). The DATASIM project revealed the need for a MOD to support new queries that consider the
semantics of a trajectory in addition to its pure spatio-temporal components. Correspondingly,
benchmarking such a MOD must also address these semantic components. Chapter 3 describes a
first sketch of characterizing what is at stake in this context: (1) a storage model (2) a partitioning
scheme and (3) syntactic and semantic queries / operators. A concrete benchmark on this basis has
not yet been implemented.
In order to summarize the results of this deliverable, and to highlight its innovation, we created the
following table where each line corresponds to one its sections. In addition to a short summary of the
content, the table sketches the state-of-the art and relates it to the novel contribution of the project’s
results. The last column provides a sample reference to the work done.
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Programme - Contract ICT-FP7-270833
Chapter 3 addresses a system point of view by assessing the moving object database (MOD). In the
recent past, several DBMS-based solutions for storing and efficiently querying historical trajectory data
were already proposed in the form MODs, (Almeida et al., 2006) (Pelekis et al, 2008) (Pelekis et al,
2011). The DATASIM project revealed the need for a MOD to support new queries that consider the
semantics of a trajectory in addition to its pure spatio-temporal components. Correspondingly,
benchmarking such a MOD must also address these semantic components. Chapter 3 describes a
first sketch of characterizing what is at stake in this context: (1) a storage model (2) a partitioning
scheme and (3) syntactic and semantic queries / operators. A concrete benchmark on this basis has
not yet been implemented (which was also not stipulated in the DOW).
During year 2 of DATASIM, various validations on large-scale data domains were performed. We
compared mobility characteristics derived from heterogeneous sources, developing various techniques
for the extraction of similar information in different data sources. In year three we will intensify the
validations and integrate them with the microsimulation model. In more detail, paper a) performs
internal model validation to evaluate the stochastic error of a simulation depending on the level of
geographic detail. Paper b) contains the evaluation standard presented in Section 2. Paper c)
compares mobility characteristics derived from GPS data with traffic counts in Pisa, Italy while papers
d) and e) compare large-scale GSM and GPS data. Finally, paper f) evaluates the performance of
different mobility models.
Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
List of Publications:
a) Q. Bao, B. Kochan, T. Bellemans, D. Janssens, G. Wets. Activity-based Travel Demand
Forecasting using Micro-simulation: Stochastic Error Investigation of FEATHERS Framework.
In D. Janssens, A. Yasar and L. Knapen (Eds.), Data on Science and Simulation in
Transportation Research. IGI Global, forthcoming.
b) C. Kopp, B. Kochan, M. May, L. Pappalardo, S. Rinzivillo, D. Schulz, F. Simini. Evaluation of
Spatio-temporal Microsimulation Systems. In D. Janssens, A. Yasar and L. Knapen (Eds.).
Data on Science and Simulation in Transportation Research. IGI Global, forthcoming.
c) L. Pappalardo, S. Rinzivilo, Z. Qu, D. Pedreschi, F. Giannotti, Understanding the Patterns of
Car Travel, The European Physics Journal Special Topics, Volume 215, Issue 1, pp 61-73,
d) L. Pappalardo, F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti, Comparing General Mobility
and Mobility by Car, Proceedings of the Complex Systems Symposium (CompSysS) at
BRICS-CCI & CBIC 2013 conference, forthcoming.
e) L. Pappalardo, F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti. The Origin of Human
Heterogeneity: Analyzing Mobility Behavior through GSM and GPS Data, Poster at ECCS
2013, 2013.
f) F. Simini, A. Maritan, Z. Néda. Human Mobility in a Continuum Approach. PLoS ONE 8(3):
e60069. doi:10.1371/journal.pone.0060069, 2013.
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2 Evaluation of Spatio-temporal Microsimulation Systems
2.1 Introduction
Modeling individual movement behavior is a complex task and requires thorough validation throughout
the modeling process. The complexity of spatio-temporal microsimulation systems originates mainly
from the vast solution space of individual movements in geographic space and time. The size of the
solution space is closely linked to the spatial and temporal resolution of the microsimulation, which is
typically predetermined by the application. The outcome of a microsimulation is a complete mobility
model for a given region, time period and population. Based on a synthetic population it provides a
detailed schedule about who moves when and where using which mode of transport. This
comprehensive information makes the validation of spatio-temporal microsimulation systems
In current practice the evaluation of microsimulations relies on a partial evaluation of mobility
characteristics. Most often a comparison with traffic counts is performed as e.g. in (Gao et al., 2010;
Horni et al., 2009; Meister et al., 2010). Traffic counts have the advantage that they can be
comparably easy obtained. However, traffic counts do not contain origin-destination information and
are typically available for vehicular traffic only. Thus, they cover only a small aspect of individual
movement. In addition, most traffic counts are available for major roads only and are therefore not
representative for the whole street network. In other words, traffic counts are a vital source for the
validation of microsimulation systems but their application is limited to a subset of model
The example of traffic counts illustrates that a new holistic validation concept for spatio-temporal
microsimulation systems is needed. Only a validation which considers a broad set of mobility
characteristics can ensure that the outcome of a microsimulation is a truthful reproduction of reality.
The required variety of validation data to implement such a concept is just becoming available due to
the advancement of information and communication technology. Thus, we are at the right moment of
time to animate the discussion about a new validation standard. In addition, research on spatio-
temporal data mining and analysis has made tremendous advances during the past decade. As a
result, a rich set of preprocessing, feature extraction, indexing and data mining methods are available
to exploit and handle spatio-temporal data.
The validation of microsimulation systems is a manifold task. In general, the validation workflow of the
modeling process can be divided into three parts, namely input data validation, internal model
validation and external model validation (see Figure 1). Input data validation ensures that the model is
build using high-quality data. Usually, the provided input data comes from secondary data sources and
has originally been collected for a different purpose. For this reason it is important to show in a first
validation step that the input data is an appropriate source for the goal of the overall modeling
process. Internal model validation measures how well the model can predict its input data, i.e. how
reliable a predictive model will perform on (unseen) training data. A high internal quality is a
prerequisite for the external quality of the model. If the input data of the model is difficult to predict, this
is likely to be the case also for unknown data. In addition, as microsimulation systems typically rely on
non-deterministic algorithms, the variability of the model is subject to internal model validation. Finally,
during external model validation the model output is compared to either independent real world data or
other model results with known high quality. This step ensures the final quality of the model results. If
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the model outcome meets a given quality standard the process of modeling is finished. Otherwise,
either the input data or the modeling process are subject to change and to a repeated validation,
leading to a validation cycle.
Figure 1: Quality cycle in microsimulation systems
In the following section we attempt to compile a comprehensive overview on the state-of-the-art of
validating spatio-temporal microsimulation systems. We provide a structured review on the various
aspects of movement data evaluation and give practical insights into challenging problems based on
real-world examples. More detailed, Section Properties of Mobility Data recapitulates general
properties of mobility data that influence the comparability of mobility data sets. Section Data Input and
External Model Validation describes mobility characteristics and validation techniques that are
commonly used to compare two mobility data sets while Section Internal Model Validation focuses on
the estimation of internal model quality. In Section Validation Use Cases we provide real-world
examples outlining the challenges of comparing mobility data sets. Finally, the Conclusion summarizes
the vital points and draws a roadmap for the further advancement of evaluating spatio-temporal
microsimulation systems.
2.2 Properties of Mobility Data
Due to the manifold techniques to record mobility information, mobility data sets can differ in various
aspects. In order to make valid statements about the similarity or dissimilarity of two data sets it is
therefore essential to have a clear understanding of the delimitation of each set with respect to its
spatial, temporal and population dimension. In this section we will discuss these dimensions as well as
different properties of mobility data sets. The properties include the observation space, sampling
coverage and resolution. In addition, we consider missing data properties because they can influence
the representativeness of a data set. Note that our grouping of mobility properties results mainly from
our experience in practice. A partially differing compilation is, for example, chosen in (Andrienko et al.,
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2.2.1 Dimensions of Mobility Data
Mobility is inherently connected to three basic dimensions: space, time and population. Geographic
space defines where movement takes place, time defines when movement takes place and the
population or object dimension specifies who is moving. In this section we will give a short introduction
to all three dimensions, summarized from (Andrienko at al., 2008; Körner, 2012).
Commonly, we refer to geographic space as the three-dimensional Euclidean space that co-rotates
with Earth and is centered at its center of mass, i.e. the physical space we observe in everyday life. In
order to specify the position of an object in physical space, spatial reference systems such as the
Cartesian or Geographic reference system are used. Typically, the spatial component of mobility data
is specified using two-dimensional geographic coordinates, i.e. the longitude and latitude of the
moving object’s positions.
Time is a one-dimensional extent which describes the ordering and duration of events. Today
Coordinated Universal Time (UTC) is used as standard temporal reference system to refer to a
specific moment in time. UTC uses the Gregorian calendar to reference days. It further divides a day
into hours, minutes and seconds. Due to the Earth’s rotation and its revolution around the sun, we
perceive a natural structure of time into cycles. Over the year we observe the change of seasons, our
working activity is typically organized by a weekly cycle, and our day / night rhythm repeats every 24
hours. Be aware that those cycles are nested and form hierarchies as e.g. year / month / day-in-month
or year / week-in-year / day-in-week. Such hierarchies are especially relevant for the validation of
mobility data because the data sets belong often to different time periods, and the aggregation of data
into time cycles provides the only way for comparison.
The population dimension specifies of which entities the movements are observed. Entities may be
animate (e.g. persons, animals) or inanimate (e.g. parcels, airplanes). As this book is placed in the
area of transportation research, we tailor the following description to humans. Persons have numerous
sociodemographic characteristics such as their gender, age, occupation or income. Sociodemographic
characteristics are known to influence movement behavior (Curtis & Perkins, 2006; Scheiner, 2010).
For example, the age determines whether we can travel independently by ourselves or whether we are
allowed to drive a car while the occupation determines whether we have regular work trips. Another
important variable that influences mobility is the place of living (Curtis & Perkins, 2006; Schwanen et
al., 2005). For example, the trip length and preferred mode of transport varies between urban and rural
In addition to the three basic dimensions that characterize movement, the movement itself can be
described by physical and semantic properties. For example, movement possesses speed,
acceleration, direction and turn characteristics. In addition, it has some means of transportation and is
conducted with some specific activity in mind (e.g. when going to work).
Together the four dimensions provide a good way to structure properties of mobility data sets, which
we will discuss next.
2.2.2 Observation Space
The observation space delimits the spatial, temporal, population and movement dimensions for which
the comparison shall be made. More precisely, in the spatial dimension it restricts the region in which
the movement is observed. Typically, this is a city, community or even larger administrative area. In
addition, the spatial observation may be restricted to a specific type of geographic objects. For
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example, instead of monitoring continuous space, we may observe movement only on the street
network. Furthermore, we could observe only highways or the pedestrian area of a city.
In the temporal dimension the observation space defines the time moment, time interval or time cycle
in which the movement takes place. If a data set consists of raw measurements it typically refers to a
specific time period, e.g. the month January 2013. Often data sets are already aggregated to a specific
time cycle, e.g. an average week. In such cases it is still important to know how the cycle relates to the
greater time hierarchy. Is it, for example, an average week in January (with slippery roads in the
northern hemisphere) or in June (holiday season), and which year does it represent (e.g. 1980)?
Furthermore, an observation may be restricted to selected time intervals within, e.g. we may observe
only the working days of a week or the daytime between 6 and 20 o’clock.
The population observation space defines the set of persons whose movements are studied. It is
closely connected to the data collection process. If a survey is conducted, the population typically
represents the residents of a defined area, e.g. the inhabitants of a city or country. Further attributes
may be used in the selection process. For example, only persons above 18 years of age or persons
who commute to work could be considered. This definition has the advantage that it is compatible with
many official statistics. However, as geographic space is not a closed system, it lacks the mobility of
externals visiting the area as e.g. commuters, freight carriers or tourists. In contrast, the population
may comprise all persons travelling in a certain area as observed, for example, when using induction
loops for traffic monitoring.
Finally, mobility data may be limited to certain mobility characteristics. For example, GPS tracking
devices may be installed into the car of a person and record only vehicular movement. Induction loops
have a similar effect as they monitor only motorized traffic. The studied movement may also be related
to certain activities only as, for example, shopping, working or vacation.
2.2.3 Sampling Coverage
The sampling coverage is closely related to the observation space. As it is typically not possible to
monitor the complete observation space, the data set is only a sample of the spatial, temporal and
population dimension. Samples are generally characterized by their distribution and size. Both
characteristics are important to know because they determine the representativeness and sampling
error of a data set with respect to the observation space.
In the spatial dimension sampling processes are most visible when a decision about the placement of
(a limited number of) stationary sensors has to be made. Examples of such sensors are induction
loops, light barriers, cameras, WiFi or Bluetooth scanners which count the number of passing vehicles
or persons. Note that those sensors are typically placed at strategic points with high traffic volume and
may therefore not be representative for the observation space. Similar problems can arise if mobile
sensors are used. Although each person moves in space and records data for various locations, a
really large sample is needed for a complete coverage of the street network. For example, Hecker et
al. (2010) analyzed a data set with 42,780 test persons of mixed GPS and CATI (Computer Assisted
Telephone Interview) records of up to seven days in Germany. The trajectories covered barely 26.7%
of the German street network. Andrienko et al. (2013) identify yet another bias inherent to event-based
mobility data. The authors have analyzed sequences of geo-referenced Flickr data. Naturally people
take pictures of interesting places, which are therefore over-represented in the data set. In order to
detect abnormalities in the spatial sampling coverage a first step is to plot the data on a map. Co-
location of data with certain geographic objects (e.g. highways, points of interest) or clearly delineated
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sectors with / without data indicate a spatial sampling bias. However, as mobility is not equally
distributed in geographic space further analyses may be required to detect irregularities.
In the temporal dimension the sampling coverage defines how frequent measurements are taken. As
time is a uniform quantity in one dimension, the analysis of its distribution and sample size is easier
than in the spatial dimension. Andrienko et al. (2013) distinguish between the analysis of the length
and regularity of time intervals between measurements, the coverage of the observation interval as
well as of relevant time cycles. If the time interval between two measurements is short enough to
permit a good interpolation of an object’s position, the authors call the data quasi-continuous.
Elsewise, it is considered episodic. For example, GPS data with a time interval of one second between
measurements is quasi-continuous. Call detail records (CDR), which accumulate only during a user’s
phone activity, are episodic. Both examples illustrate also the difference between regular and irregular
measurement techniques. Regular measurements guarantee a homogeneous coverage of the
temporal observation space. However, only in combination with frequent measurements, a
representative temporal sample can be formed. Consider, for example, traffic counts which are
observed for five minutes at noon every day. Because movement activity varies over time, it is not
obvious how to relate these measurements to the movement activity of a whole day. Without an
assumption about a relationship between noon and daily (or day-of-week) traffic, we would even not
be able to make assumptions about the weekly or yearly variation of traffic outside of the observed five
minute time intervals. Thus it is important to cover all relevant time cycles within the temporal
observation space sufficiently. In order to analyze the temporal coverage of a data set, Andrienko et
al. (2013) propose to plot histograms or the cumulative distribution function of either the number of
measurements or the time interval between consecutive measurements for different time cycles.
Spatio-temporal microsimulations have the scope to model population movement. Therefore, the
sampling coverage of input or validation data must typically be representative for some national
population. As mobility data sets are often secondary data sources only, a variety of sampling biases
can arise. As mentioned in (Körner et al., 2012) a first cause of sampling bias is the different affinity of
people to either the companies or devices collecting mobility data. On the one hand, companies (e.g.
mobile network providers) target specific customer groups. Their data collection is therefore biased
towards those sociodemographic groups. On the other hand, data collection relies increasingly on the
usage of mobile devices (e.g. CDR, Bluetooth). However, mobile devices are not equally distributed
and used within the population, but show a clear bias towards the young generation. A second cause
for a biased population sample is the uncontrolled relationship between persons and data collection
devices. A person may carry multiple tracking devices (e.g. mobile phone(s), tablet) and thus be
included several times. Similarly, an observed device may be shared by one or more persons (e.g. a
car) and therefore represent multiple users. The assessment whether a data set contains a population
bias is a complex task because most secondary mobility data sets contain only numeric identifiers due
to privacy reasons. In such a case expert knowledge as well as good reasoning capabilities are
required for the analysis (Andrienko et al., 2013).
Finally, a sample bias may also be introduced by selection processes in the dimension of movement
characteristics. For example, Bluetooth scanners require up to ten seconds to detect all devices within
their range. In consequence, Bluetooth-enabled devices that pass the sensor range with a high
velocity have a smaller probability to be detected than slow-moving devices (Gurczik at al., 2012;
Schmietendorf, 2011). In addition, a sample bias may be introduced during data preprocessing. For
example, when cleaning GPS data, points above or below a certain velocity or trips below a given
lengths may be removed as noise. An empirical detection of such biases is hard because it is not
obvious for which type of bias to look in the first place. Therefore, it is important to have a good
understanding of the data collection process and the performed preprocessing steps.
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2.2.4 Resolution
We use the term resolution to refer to the level of aggregation or the amount of detail in a data set. In
the population dimension the smallest possible measurement unit is a single person, which
corresponds to the natural resolution of microsimulations. However, input and test data sets may not
be that fine-grained. For example, sociodemographic characteristics are typically aggregated for larger
geographic areas in order to be privacy-preserving. Similarly, movement information may be available
only in aggregated form or without sociodemographic references. In some mobility data sets identifiers
are routinely changed so that the association to a specific unit is lost over time.
The spatial resolution of a data set can vary between a few centimeters and several kilometers
depending on the monitoring technology used to collect the data. For example, GPS data has a very
high resolution while CDR data may relate to very large GSM cells in suburban areas. However, also
the spatial resolution of microsimulations can vary. Some systems perform a simulation on the level of
the street network as, for example, MATSim (Balmer et al., 2006) while other systems operate on the
level of traffic analysis zones as, for example, FEATHERS (Bellemans et al., 2010).
In the temporal dimension the resolution of a data set corresponds to the time span of a single
measurement. For example, traffic counts are typically aggregated at the level of hours while most
data sets containing time stamps have a resolution of seconds or milliseconds. The temporal
resolution is often confused with the temporal sampling rate. However, a data set may have a very low
sampling rate (e.g. one GPS point every hour) while the temporal resolution of the measurement is
very high (e.g. a timestamp of the format JJJJ-MM-DD:hh:mm:ss).
The resolution of movement characteristics can vary in a broad range. For categorical variables (e.g.
type of activity, mode of transportation) the resolution depends on the employed ontology or
classification system. For derived numeric movement characteristics (e.g. speed, acceleration) the
resolution depends on the aggregation level of the spatial and temporal dimension.
2.2.5 Missing Data
Missing data typically originates from uncontrolled events or processes during data collection and
extends over all dimensions of mobility data. For example, technical devices may be defective or the
human recollection of movement incomplete. Missing data pose a problem to data evaluation for
several reasons. First, the amount of missing data may be so high, that an exclusion of incomplete
data records would strongly reduce the data set. Second, summary statistics may be considered for a
given time interval. If the missing data is ignored (i.e. substituted with zero values) an underestimation
of movement behavior will be induced. Third, a relationship between the absence of data and the
mobility behavior of a person may exist (Körner, 2012). For example, people between 30 and 39 years
of age show with an average of 53 kilometers per day the highest mobility while teenagers travel
around 30 kilometers per day and people above 74 years travel only 16 kilometers on average
(Bundesministerium für Verkehr, Bau und Stadtentwicklung, 2010). If certain characteristics of such
groups relate to the intensity of missing data, for example, elder persons may be more reliable to carry
a GPS device than teenagers, the pattern of missing data is not any more at random. Therefore it is
important to detect and analyze a data set for missing data as e.g. proposed in (Körner, 2012;
Andrienko et al., 2013; Hecker et al., 2010).
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2.3 Data Input and External Model Validation
In general, the validation process of model input data does not differ from the validation process of
model output data. In both cases various characteristics of the data set in question have to be assured
by comparison against external data sources. Both times we are interested in mobility characteristics
of a given population. Thus we have the common task to compare mobility characteristics between
two mobility data sets, which is the main topic of this section. We will start by an introduction to
general measures for the comparison of categorical and numerical data sets. Next, we give a
systematic overview on the various mobility characteristics that can be used to describe mobile
behavior. We will structure this part according to movement characteristics considering single
movement positions, differences between movement positions (e.g. length and distance) and
sequential dependencies between movement positions. In this way we increase the spatio-temporal
complexity of the observed characteristic step by step. Finally, we will discuss the state-of-the-art of
external model validation.
2.3.1 General Measures for Comparing Categorical and Numerical Variables
Due to the wide spectrum of movement characteristics, a number of different error or distance
measures can be applied for the comparison of two mobility data sets. In general, each measure is
based on a particular definition of error or distance, and its estimate will thus reflect the characteristic
features and properties of the underlying error / distance function. Therefore, there is no absolute best
measure, and the validation method has to be chosen considering the features that one wishes to
evaluate and the characteristics of the data sets. In most cases it is recommendable to use several
measures in order to have a comprehensive picture of the error / distance.
In this section we will introduce general measures for the comparison of (one or many) pairwise
observations as well as for the comparison of distributions of numerical and / or categorical data. For a
comprehensive overview we refer the reader to (Hyndman and Koehler, 2006) and (Cha, 2007).
Let A=(a1,a2, …, an) and B=(b1, b2, …, bn) denote two data sets with pairwise observations. For all
error measures we will assume that data set B contains the ground truth. If the data is categorical the
error is typically specified as average of the 0-1 loss, i.e.
bal ii
ii 1
The error can be further analyzed using a confusion matrix. The rows of a confusion matrix represent
the ground truth while the columns represent values of the second (predicted) data set. All coinciding
data records are located in the diagonal of the table. Table 1 shows a fictitious example of a confusion
matrix for evaluating a model that predicts the mode of transportation.
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data set A
public transport
data set B
public transport
Table 1: Example of a confusion matrix
In total the ground truth data set contains 23 objects. The confusion matrix shows that of the eight
actual cars the system predicted five as cars and three as public transport. Of the six public transports
it predicted three correctly, two as cars and one as bike, and of the bikes it predicted eleven correctly
and two as public transport. We can see from the matrix that the system in question has trouble
distinguishing between cars and public transport but distinguishes well between bike and other types
of the mode of transport.
For numeric data the most commonly applied error measures are mean absolute error (MAE), mean
absolute percentage error (MAPE) and root mean square error (RMSE). They are defined as follows:
nbaMAE n
 
nbaRMSE n
In addition to error functions, also various distance functions can be applied to measure the
(dis)similarity between pairwise observations. Most well-known is the Minkowski distance
iiM bad
which results in the Manhattan distance for p=1, the Euclidean distance for p=2 and the Chebyshev
distance for p=. In the Minkowski family the weight of large individual errors increases with
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increasing p, amounting to the maximum absolute pairwise difference for p=. One way to reduce the
effect of large errors is to apply, for example, the Lorentzian distance
iiiL bad 1)1ln(
For visual inspection, numerical pairwise observations can also be depicted in a scatter plot. The
closer the points are to the diagonal representing equal values, the more similar are both data sets.
The linear dependence between the data sets can be quantified using e.g. Pearson’s correlation
For the comparison of distributions, methods for categorical, discrete and continuous data exist. One
way to treat categorical distributions is to view the two frequency values of each category as a
pairwise observation and to apply the above described methods for numeric data. Another way of
comparison is to perform a statistical test to assess how likely both data sets origin from the same
statistic population. For categorical data the chi-squared test for homogeneity can be used for this
Discrete valued distributions can be treated similar to categorical distributions. However, due to their
numerical nature, we can derive and compare further moments of the distribution as, for example, its
mean, variance or skewness. The mean of two data sets can be compared using, for example, a two-
sample t-test or Z-test whereas the variance can be compared using the F-test (under the assumption
of normally distributed data).
Finally, continuous valued distributions can be compared using the Kolmogorov-Smirnov test for
homogeneity. Similar to discrete valued distributions, we can compare the mean, variance or
skewness of the distributions. In addition, the distribution may be discretized and treated similar to
categorical distributions.
2.3.2 Evaluating the Distribution of Movement Positions
In this section we consider count-based evaluations of movement characteristics that describe the
population’s whereabouts for given instances in time, space or a combination of both. We will begin
with general statistics related to the population and the population’s movements. Afterwards we will
consider their spatial, temporal and spatio-temporal distribution. General count-based population and
mobility statistics are the:
distribution of sociodemographic attributes,
distribution of activities,
distribution of trips,
distribution of mode of transport.
Sociodemographic attributes that are closely related to a person’s mobility are, for example, age,
gender, occupation or income. It is important to compare not only the distribution of each single
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attribute but also their joint probability distribution. Activities are typically the cause for mobility. For
example, we travel to work, go shopping or to the cinema. An interesting mobility characteristic is
therefore the distribution of trip activities, stating the percentage of trips that are made for a specific
purpose. In addition, we can consider the distribution of activities within the population. Which
percentage of the population performs a certain activity? How often is an activity performed on
average? Körner (2012) defines a family of quantities that can be used for the evaluation of such
questions. Similarly we can analyze the number of trips or home-based tours, and (the split of) the
mode of transport (e.g. on foot, by bike, by car or public transport).
Next, we consider the spatial distribution of the population at a given moment or interval of time
using a specific mode of transportation,
travelling with a specific speed,
performing specific activities.
The spatial distribution of the population becomes visible, for example, in the traffic volume. The traffic
volume states the average or total number of passing vehicles or persons in a given time interval, e.g.
an hour or a day. When derived from local sensors (e.g. induction loops, camera systems, Bluetooth),
the traffic volume is available only for a selected set of locations. However, data mining techniques
can be used to predict traffic frequencies for sites without sensors (May et al., 2008a, May at el.,
2008b). Traffic counts can also be derived from GPS trajectory data as investigated in (Pappalardo et
al., 2013). In such a case the geographic coverage of measurements increases, however, only a
sample of passer-bys is recorded. Traffic counts can further be differentiated according to the mode of
transport or the speed class. In addition, differences between the spatial distribution of counts and
other numeric characteristics (e.g. speed) can be quantified by calculating pairwise differences
between the values of each location. For visualization the spatial distribution of the values or their
differences can be depicted on a map using e.g. a color encoding similar to (Andrienko & Andrienko,
2010). In addition to the distribution of the population when travelling, the distribution of the population
during the performance of activities can be analyzed. Similar to traffic counts, the number of visitors at
train stations, shops etc. can be compared. While those visits are of short duration and show a high
variability, we can also analyze the distribution of long-term activity locations as the home or work
place. Depending on the spatial aggregation unit, statistics about the number of persons living or
working in a given street, sector or city may be compared.
In the following we analyze the temporal distribution of the population at a certain location or in a
certain area
o using a specific mode of transportation,
o travelling with a specific speed,
o at the begin / end of certain activities,
o at the begin / end of a trip.
The temporal usage of a location can be measured by continuously monitoring the time of passage of
moving objects. Depending on the data source, a stream of events or an already aggregated count for
a series of time intervals may be given. If one or both of the time series are discretized, the data sets
have to be adapted to contain the same regular intervals of time. The temporal distribution can further
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be differentiated according to a specific mode of transport, speed class or average speed. In addition
to the temporal distribution of movements, the temporal distribution of activities or trips can be
compared using their start or end time. For a visual comparison of temporal distributions, a temporal
histogram as shown in (Andrienko & Andrienko, 2010) can be used. Note that the temporal distribution
of passages may also be gained from GPS trajectory data as described in (Schreinemacher et al.,
2012). Here again the problem arises that only a sample of passers-by are observed and the data may
be sparse in time or space.
Finally, a joint comparison of mobility characteristics in the spatial and temporal dimension can be
made. Due to the high complexity of spatio-temporal data, such a comparison typically relies on a
sequential aggregation in space and time. For example, the data can be first divided into a set of time
moments or intervals. For each time slice a measure comparing the spatial distribution of two data
sets is computed. The computation is repeated for each time slice and then summarized in a graph by
its mean or variance. Similarly, the data set can be divided by locations first. For each location a
comparison of the temporal distribution is performed. Subsequently, the results are aggregated over
all locations. A spatio-temporal comparison can also be aided by visualization. For example, a
geographic map containing temporal mosaic diagrams as in (Andrienko & Andrienko, 2010) is able to
show the full variation of some variable (or the difference between two data sets) in space and time.
However, such maps are very complex because of their high information density. Visual analysis can
then be aided by classifying similar situations. For example, Schreinemacher et al. (2012) cluster
street segments according to the temporal distribution of passages. The street segments were then
colored according to their types in order to identify spatial relationships. If a set of speed profiles is
already given, each location can be assigned to its most similar profile. Spatio-temporal differences
between two data sets can then be observed by visualizing the spatial distribution of the assigned
profiles. Andrienko et al. (2012) performed a clustering the other way around. They determined the
spatial distribution of visitors for a run of 30 minute time intervals and subsequently clustered the
spatial distributions. For such a comparison of two data sets, a set of spatial distributions has to be
given in advance, according to which a classification of time intervals can be made in both data sets.
The results can then be compared in a time graph.
2.3.3 Evaluating the Distribution of Differences between Movement Positions
In this section we consider movement characteristics that are derived from the difference of two
positions in either space or time, namely length and duration. In the most simple form, we can compile
the total length (duration) of all trips performed in a given period of time, e.g. one year. The total sum
can be further differentiated according to the used mode of transportation or the traversed type of
street. For example, we can calculate the total number of kilometers (hours) travelled by car on
highways. Instead of the total number, we can also compare the distribution or averages of the length
(duration) with respect to different categories or a combination of them:
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For example, the distribution or average trip length (duration) per person and day is a very
characteristic information about a population’s movement behavior. Those statistics can also be
calculated for single trips. Next, we can calculate the length (duration) to reach specific locations.
Imagine, for example, that a shop is interested to determine the catchment area of its customers or a
city council wants to estimate how far (long) incoming or outgoing commuters travel daily to reach their
work location. Further, the length (duration) that people are willing to travel to perform certain activities
can be compared. For activities we can additionally compute their duration. So far we have considered
absolute values of movement characteristics. Another interesting feature is the relative value of the
length (duration) of a route compared to the shortest possible route (in space or time). Such a
comparison is especially useful if a traffic assignment step is performed during the microsimulation in
order to map the movement between zonal areas to the street network. While time is a one-
dimensional extent, movement takes place in three-dimensional Euclidean space. In order to measure
the spatial extent (spread) of a person's mobility, typically the radius of gyration (ROG) is used. The
radius of gyration rg is defined as
 with 
 .
Hereby represent the     positions recorded for a given user. In the mobility context, is a
two-dimensional vector describing latitude and longitude of a user’s movements.
Similar to the previous section, we can evaluate the spatial and temporal distribution of the above
characteristics. When choosing the spatial dimension, the movement characteristics are typically
attached to the place of living of a person or the targeted activity location. For example, from national
statistics as (Bundesministerium für Verkehr, Bau und Stadtentwicklung, 2010) it is known that the
average number of kilometers travelled per person and day of people that live in large cities is shorter
than of people living in suburban areas. In the temporal dimension movement characteristics are
typically attached to the start or end time of an activity or trip. This allows to compare, for example,
whether two data sets contain the same characteristic variation of the trip length (duration) over the
day or between week days and weekends.
2.3.4 Evaluating Sequential Dependencies between Movement Positions
In this section we describe characteristics that originate from the sequence of movement positions. In
general, we can distinguish between the physical and semantic representation of movement
sequences. In the first case a trajectory consists of the sequence of passed or visited geographic
locations. In the second case a trajectory is a sequence of activities as, for example, “home → work
shopping → home”.
The most common representation of movement dependencies between geographic locations is an
origin-destination (OD) matrix. An OD matrix states for each pair of start and end locations the number
of performed trips in a given time interval. Such a matrix can also be evaluated for a specific mode of
transport (e.g. for the railroad network) or for specific trip purposes (e.g. home-work trips). Comparing
only the OD distribution of the start and end location of trips, however, does not guarantee that the
“right” route choice has been made. Therefore, we can compare the OD distribution for movement
positions within a trip. If the movement is mapped to the street network we can calculate an OD matrix
for each passed street segment from those trips which pass the segment. Having derived a single
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error value from the OD matrix comparison for each segment, a color-encoded map or average error
value can be computed. Another way to compare local movement behavior is the calculation of flows
between neighboring areas or street segments. Given a movement trajectory, its sequential
information is discarded with exception to the very next step, i.e. we create a Markov chain from the
data. Such flows describe, for example, turning probabilities at cross roads or hand-over information of
GSM cells. If we extend such flows to contain several steps, we obtain movement rules as, for
example, given that a person moves from A→ B then the probability of moving to C is 25%. The
detection of movement patterns, however, is a complex task. One possibility is to find frequent sub-
trajectories in the data sets as proposed by Giannotti et al (2007). Another possibility is to build a
compact model of the location dependencies using e.g. Bayesian Networks (Liebig et al., 2009).
In the case of semantic trajectories, we have to find an appropriate measure to compare activity-travel
sequences. First and foremost the goodness-of-fit measure has to be able to consider the different
dimensions of activity-travel patterns. However, another critical point is that the measure should also
be able to compare sequential information. The Sequence Alignment Method (SAM) is an appropriate
measure as it complies with all of the above aspects. Introduced by Wilson (1998) in time use
research, SAM has the right properties for working out comparisons between predicted and observed
activity-travel patterns. SAM works by calculating a distance between the predicted and observed
string of activity-travel information. Unlike the Euclidean distance, a specific distance is computed by
determining the amount of effort it takes in order to make the two strings equal. To calculate the total
amount of effort, a series of possible operations are performed on the strings. This way strings can be
made equal by using so-called ‘identity’, ‘substitution’, ‘insertion’ and ‘deletion’ operations. The
distance calculated this way is then taken as the measure of dissimilarity between the strings.
However a shortcoming of this approach is that SAM can only handle one-dimensional strings. This
means that SAM can analyze similarities for specific attributes of activity-travel patterns, such as
activity type, mode choice, location choice, etc. but not the inter-relationships between elements of
different attributes. Therefore, a multidimensional extension of the traditional SAM was developed
(Joh, 1999). In this way multidimensional activity-travel patterns can be compared. However, the
comparison relies only on the sequential activity information. For the inclusion of temporal information,
further, sophisticated similarity measures have yet to be developed. Nevertheless, similarly to physical
movement sequences, we can also extract and evaluate Markov probabilities and rules from the
activity-travel sequences.
2.3.5 State-of-the-Art of External Model Validation
The validation of spatio-temporal microsimulations with external data sources depends strongly on the
availability of data sources in the modeling region. Typically, three or four different aspects of the data
can be evaluated. In the following we will provide a cross section of common microsimulation
evaluations based on the MATSim and FEATHERS simulation systems. Most often, a validation of the
traffic volume, trip length and trip duration over a given time interval or a 24 hour time cycle is
performed. For example, Gao et al. (2010) evaluate the average trip duration and length per hour of
day, the aggregated traffic volume for two 4 hour time periods as well as the average speed on
highways for two 2 hour time intervals during peak traffic. They summarize the results using
(temporal) histograms, scatter plots, relative differences, RMSE and regression analysis. Horni et al.
(2009) focus in their work on the evaluation of leisure and shopping trips. They provide histograms
about the average shopping trip length and duration. In addition, they provide histograms to compare
the distribution of the length of shopping trips. The authors also evaluate the number of shopping
activities per location and provide a map showing the locations with either the largest positive or
negative differences for two configurations of the microsimulation. An evaluation of traffic counts is
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performed using scatter plots as well as box plots. the latter show the absolute and relative differences
over a 24 hour time cycle. Meister et al. (2010) target the evaluation of the mode of transport split.
They provide histograms showing the cumulative model split for increasing trip length and duration. In
addition, they visualize the deviation of the share of public transport for 5 x 5 km grid cells on a map.
Similar to the previous authors, a temporal histogram and box plot are used to show the absolute and
relative deviation of traffic counts in a 24 hour time interval. Kochan (2012) takes a broader view on
the evaluation of mobility characteristics. The author also evaluates traffic counts and travel lengths.
For traffic counts he uses Person’s correlation coefficient, and for travel lengths he calculates the total
vehicle kilometers traveled per year. In addition, the author performs a comparison of trip start times
and the distribution of trip origin-destination pairs. For the former he uses a temporal histogram over a
24 hour cycle. For the latter the author provides a scatter plot and calculates the coefficient of
determination of a linear regression model.
The provided literature review shows that many different mobility characteristics are considered during
evaluation. The validations included count-based evaluations of trips and activities. Evaluations of
different modes of transport and speed were performed as well as differences between movement
positions. Typically, the temporal distribution over a day was considered, and values for different
locations provided to show the distribution in space. Yet, the selection also shows that a uniform
evaluation standard is missing. Many characteristics are shown in temporal histograms without an
explicit quantification of the error. This makes it hard to set up a validation benchmark to enable the
comparison of results across different simulation platforms and data sets. In addition, most validations
focus on either spatial or temporal characteristics. Combinations of both dimensions as well as the
evaluation of dependencies and sequential information are greatly missing. Finally, all validations lack
a holistic view on validation and concentrate only on few movement characteristics.
2.4 Internal Model Validation
The internal validation of transport demand models tests the ability of the model to predict travel
behavior. It is typically performed on the level of model components and requires comparing the model
predictions with information other than that used in estimating the model. If the model output and the
independent data are in acceptable agreement, the model can be considered validated. As
microsimulation systems typically rely on non-deterministic algorithms, the variability of a model is also
subject to internal model validation. In this section we will first introduce general techniques for the
validation of model components and model variability. Afterwards we discuss a practical example of
internal model validation in the case of an activity-based transportation model inside the FEATHERS
framework (Bellemans et al., 2010).
2.4.1 Validation on the Level of Model Components
Different techniques exist regarding internal model validation. A widely known approach is the so-
called cross-validation method (Kohavi, 1998). This technique is generally used in prediction tasks
when one wants to investigate how reliable the model performs in actual practice. The task hereby is
to learn a model from data that is at one’s disposal as, for example, a travel survey. Such a model may
be a regression model or a decision tree or any other decision support tool obtained by means of a
learning algorithm. The difficulty of evaluating a predictive model is that it may possess strong
prediction potency on the training data set, but might do worse in predicting unseen data. This
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phenomenon is also called overfitting. Cross-validation avoids overfitting by separating the data set
into two parts: one is used to train or develop the model, and the other part is used to validate the
model. In ordinary cross-validation the training and validation data sets cross-over in sequential steps
such that each data record has the opportunity to be in the validation set once. In practice various
procedures exist for cross-validation of a model, namely: hold-out validation, k-fold cross-validation
and leave-one-out cross-validation.
In hold-out validation a common way is to divide the available data set into two non-overlapping
fractions: one for training and the other for validation. The test data is held out and not being used
during the training phase. This way hold-out validation prevents that training data and test data
overlap each other, yielding an estimation of higher accuracy for the generalization performance of the
learning algorithm. A well-known drawback of this approach is that this procedure does not make use
of all the data at hand and that secondly the outcomes are highly dependent on the choice for the
training and validation data sets.
In k-fold cross-validation the data is first subdivided into k equal-sized data parts or so-called folds.
Next, k repetitions of training and validation steps are carried out such that within each repetition
another fold of the data is held-out for validating the model while the k-1 folds left are used for
learning. An advantage of this approach is its accurate performance estimation. However, the
overlapping of training sets between repetitions is a drawback.
The last validation technique discussed here, leave-one-out cross-validation (LOOCV) is a special
application of the traditional k-fold cross-validation where k equals the total number of records in the
data set. In each iteration step all data records except one single record are used for training and the
model is tested afterwards based on that single record. The model accuracy estimation accomplished
by means of LOOCV is almost unbiased.
2.4.2 Validation of Model Variability
Activity-based models of travel demand using a micro-simulation approach inevitably include
stochastic error that is caused by the statistical distributions of random components. Indeed, for
making choices based on decision trees the transport demand system needs to make choices by
means of randomly picking out a choice alternative based on the probability distribution in the decision
tree nodes. As a result, running a traffic micro-simulation model several times with the same inputs will
obtain different outputs. Analysis of the impacts on the model outputs thereby is one of the vital steps
in the model development and validation. In order to take the variation of outputs in each model run
into account, a common approach is to run the model multiple times and to use the average value of
the results. The concept of confidence interval can then be applied with the purpose of determining the
required minimum number of model runs to ensure at least a certain percentile of zones in the
concerned study area reach stability i.e., with a certain level of confidence that the obtained average
value of each of these zones can only vary within an acceptable interval. However, how many runs are
really needed in order to reach stability depends strongly on the kind of activity-based transport
demand model under concern.
2.4.3 Example of a Model Components Validation
In this section we present a concrete example of a hold-out validation for an activity-based transport
demand model implemented in the FEATHERS framework (Bellemans et al., 2010). We first provide a
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short description of the FEATHERS framework and the activity-based transport model inside
FEATHERS in order to sketch the validation context. Subsequently we discuss the results of the
model components validation.
The FEATHERS framework is a versatile system that facilitates the development and maintenance of
activity-based models for transport demand. For this purpose FEATHERS provides all necessary tools
to develop and maintain activity-based models in a particular study area. Currently, the FEATHERS
framework incorporates the core of the ALBATROSS Activity-Based scheduler (Arentze et al., 2005).
This scheduler assumes a sequential decision process consisting of 26 decision trees that intends to
simulate the way individuals build daily schedules. The output of the model consists of predicted
activity schedules. They describe for a given day which activities are conducted, at what time (start
time), for how long (duration), where (location) and, if travelling is involved, the transport mode used
and the chaining of trips. The activity-based model inside FEATHERS uses a CHAID-based decision
tree induction method (Kass, 1980) to derive decision trees from activity diary data. The following
example validation therefore describes the quality of the resulting trained decision trees for each step
in the decision process model. Most decision trees involve a choice between discrete alternatives, for
example, the transport mode. However, activity duration and activity start time decisions are modeled
as a continuous choice and therefore a continuous decision tree is constructed for each of these kinds
of choices.
We tested the predictive performance of the decision tree models using a hold-out sample, i.e. only a
subset of the data was used during training to build the model. For each decision step, a random
sample of 70% of the cases (training set) was used to build the decision trees. The other subset of
30% of the cases (test set) was presented as unseen data to the models for the validation. The
accuracy of each discrete choice decision tree was calculated as percentage of correct predictions
while the accuracy of continuous valued decision trees was evaluated using MAPE (see Section
General Measures for Comparing Categorical and Numerical Variables).
In order to make a better judgment about the predictive ability of the discrete choice decision trees, we
additionally calculated a null-model for each decision tree. A null-model is defined as a model that
randomly selects a choice alternative. The performance of a null-model can simply be derived by
dividing 100 by the number of choice alternatives. By comparing the null-models with the respective
decision trees the relative performance of each tree can be determined, i.e. it becomes possible to see
whether or not the decision trees are vigorous enough to score better then the null-models.
As stated before the activity-based model inside of FEATHERS consists of 26 decision trees.
However, for illustration purposes, we show the validation of only a selection of decision trees. The
predictive performance of the selected decision trees on the training and validation sets are presented
in Table 2. As can be seen all selected trees perform better then the null-models where a random
choice alternative is being selected. Indeed, when looking, for example, at the first tree, which predicts
whether or not a work activity is included in an activity-travel schedule, it can clearly be seen that its
performance is much better than of its equivalent null-model. The null-model indicates that it would
correctly predict choices in 50% of the cases while the accuracy on the test set is 77.8% indicating that
the tree correctly predicts choices in almost 78% of the cases. Based on Table 2 it can also be
concluded that the degree of overfitting for the selected decision trees, i.e. the difference between the
training and the validation set is low. Therefore it can be underlined that the transferability of the
model, with regard to the selected trees, to a new set of cases is satisfactory. Keeping the first
decision tree as an illustration again, it shows that the training and the test set accuracy differ only by
about 0.1% meaning that the decision tree that was estimated with the training set clearly performed
well on the unseen test set.
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For all continuous valued decision trees in Table 2 the MAPE, determined on the validation and test
set, is shown. As can be seen, the MAPE for each decision tree is approximately the same for the
training and validation set, implying that the decision trees perform well in case of unseen data cases.
However, values differ much across all trees. This is caused by the fact that the nature of the different
choices to be determined is very diverse. For example, the duration of work episodes tend to be rather
stable as opposed to the duration of fixed episodes (e.g. bring / get activity). Nevertheless, overall the
continuous decision trees perform quite well.
Nr of choice
Null model
set (%)
set (%)
set (%)
Test set
Inclusion of work episode
Total duration of work episodes
Timing of work episodes
Work location, in/out home
Transport mode work episodes
Inclusion of fixed episode
Duration of fixed episodes
Timing of fixed episodes
Inclusion of flexible episode
Duration of flexible episode
Timing of flexible episode
Location, same as previous
Location, distance-size class
Transport mode non-work episodes
Table 2: Predictive performance of discrete (D) and continuous (C) decision trees
The example above demonstrated the application of the hold-out validation technique in case of an
activity-based transport model inside FEATHERS, however other validation techniques discussed
previously may be considered as well.
A dash (-) in the table indicates that the respective error measure is not applicable
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2.5 Validation Use Cases
In today’s world mobility surveys are still the main source to gather data as input for mobility models.
Unfortunately the implementation of a survey does have some significant disadvantages. It is usually
time-consuming and connected with costs. For this reason surveys are often restricted in the number
of participants, space or time. In consequence, it is often hard to build a comprehensive mobility model
that offers detailed information for a large geographical area for a longer period of time given the input
In contrast to surveys, more and more big data is available for analysis. In most cases the data is not
tailored to the use in mobility models. Yet, it offers a high potential to describe mobility of daily life.
Two of the most common big data sources are GPS and GSM. For this reason we will explore the
evaluation of mobility characteristics based on both big data sources in this section. This is done by
two examples. In the first example we compare a GPS data set in a central region of Italy with a GSM
data set of a European country. This evaluation is done by an indirect cross-data set validation were
we compare derived statistics from two distinct data sets portraying the same phenomenon. In the
second example both data sets are collected in parallel in the greater area of Lausanne, Switzerland.
2.5.1 Evaluating GPS and GSM Data in the Region of Pisa
Radius of Gyration
In this section we present a cross-data set validation using GPS and GSM data. The employed GPS
data set contains information of approximately 9.8 million different car travels from 159,000 cars with
on-board GPS devices. The GSM data was collected by a European mobile phone carrier for billing
and operational purposes. With respect to the dimensions of mobility data discussed in Section
Properties of Mobility Data, both data sets differ in several aspects. Regarding the observation space,
the GPS data refers to trips performed during one month (May 2011) in an area corresponding to a
region in central Italy (a 250 km x 250 km square). In contrast, the mobile phone data covers an entire
European country and a period of observation of six months. Moreover, with respect to the population,
the car travel data set represents a 2% sample of the overall population of cars in Italy, while the
mobile phone data set covers users of a major European operator (100,000 users). The Resolution of
the GPS data is higher than of the GSM data, providing very detailed information about the spatio-
temporal position of users, with an average sampling rate of a few seconds. Conversely, information
provided by the GSM data set is not very accurate in terms of space and time because an individual
may be anywhere within a tower’s reception area, which can span up to tens of square miles. Since
call patterns are bursty, for most of the time we do not know the actual position of the user.
Despite its low resolution, mobile phone data is very appropriate to study general mobility. It usually
includes all possible means of transportation. In contrast, GPS data may refer only to a particular kind
of mobility. For example, our study data set contains only car traces because the GPS devices were
installed into the cars. The fact that one data set contains aspects missing in the other data set makes
the two types of data suitable for an external validation of patterns emerging from human mobility
behavior. We do not expect to observe the exact same behavior in both data sets, but the same
tendencies and laws behind the movement patterns.
The example of external validation we discuss in this section has been analyzed in detail by
Pappalardo et al. (2013). The authors investigate whether known general mobility patterns found in
GSM data by González et al. (2008) also apply to car travel. Based on the GPS data set described
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earlier, they computed main mobility measures used in literature, such as the radius of gyration (see
Section Evaluating the Distribution of Differences between Movement Positions) and compared it to
the GSM data set. The distribution of the radius of gyration across the GPS data set resulted in a
power law with an exponential cutoff: P(rg) (rg+ro)- exp (rg/) with ro = 5.54, = 1.13 and = 39.76
(see Figure 2 left). Though the parameters differed from the earlier estimated parameters ro = 5.8,
= 1.65 and = 350 (see Figure 2 right) by González et al. (2008), the type of the curve agrees with
the previously found results. The results confirm that the vast majority of individuals tend to travel
within small distances, whereas some of them carry out very long journeys. The results further
strengthen the insight that a huge heterogeneity exists in the characteristic travel distance of people.
Moreover, the variability seems to be independent from the spatial observation space (a region with
GPS data vs. a whole country with GSM data) and temporal observation space (one month for GPS,
six months for GSM). As we see in the GPS plot of Figure 2 (left), there is a difference between the
predicted behavior and the observed behavior for people with a radius of less than 5 km. This is
presumably due to the sampling coverage, since people tend to cover small distances by foot, bike, or
bus, resulting in a low probability to find such travels in the car data set. This is a phenomenon we do
not observe in GSM data, since the data covers all types of travel.
Figure 2: Distribution of the radius of gyration; left: computed on the GPS data set (source: Pappalardo
et al., 2013); right: computed on the GSM data set (source: González et al., 2008)
Another interesting characteristic of individual mobility is the most frequent location L1, i.e. the zone
where a person can be located with highest probability when not moving, most likely the home or work
place. Estimating L1 in GSM data is rather immediate: it is the tower from which the user performs the
highest number of calls. Working with GPS car traces is more complex because the data does not
provide explicit information about the visited locations of a user but only of the used parking sites. In
order to solve the problem Pappalardo et al. (2013) applied the Bisecting K-means clustering algorithm
(Tan et al., 2005) on the sets of origin and destination points of the sub-trajectories. The most frequent
location L1 does not necessarily coincide with the center of mass pcm of movement positions, which is
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used in the calculation of the radius of gyration. Pappalardo et al. (2013) therefore calculated the
distance d(pcm, L1) between both locations and related it to the previously extracted radius of gyration.
The results are depicted in Figure 3 for the GPS data (left) as well as the GSM data (right). In both
data sets the distance tends to grow with the radius of gyration. However, the GSM data shows an
interesting “trail” emerging for higher radius of gyration that requires further investigation. The strong
correlation between the two variables is interesting and presumably due to the systematic nature of
human motion. Indeed, if a person travels arbitrarily in any direction from and to the same preferential
location, the distance between the center of mass and the most frequent location will tend to zero, and
the radius of gyration will have no relation with it. On the contrary, since each vehicle follows
systematic travels among few preferred places, the center of mass is pulled by these trips towards the
mean point of the frequent locations. Therefore, the more a vehicle travels away from its L1, the more
the center of mass tends to be distant from the most frequent location. This outcome in both data sets
suggests that the center of mass is not adequate to describe a realistic barycenter of individual
mobility, especially in the case of users with a large radius of gyration.
Figure 3: Correlation between the radius of gyration rg and the distance between the center of mass
and the most frequent location of users d(pcm, L1); left: computed on GSM data set
(source: Pappalardo et al., 2013); right: computed for GSM data set
In the last example the comparison between GPS and GSM is made only visually. In a future analysis
the values will also be compared numerically. In order to do so we need first to properly choose the
size of the bin used to sample users, second to choose an appropriate function to describe the
correlation and third to fit the data to it.
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Traffic Counts
Consider a data sample representing movements of individuals such as, for example, the GPS car
travel data set introduced above. A comparison with the ground truth, i.e. the movements of cars of the
whole population within the urban area, would be very useful both for validation and for traffic
prediction purposes. Although in general it is very difficult to obtain a whole population describing a
particular phenomenon, nowadays many sensing technologies are available that provide streets-
based traffic counts.
In (Pappalardo et al., 2013) the authors use a data set containing logs from Variable Message Panels
(VMP) to assess the generality of analytical results obtained from the GPS data set. VMPs are devices
situated in the entry gates of the city of Pisa with the purpose of counting all entering vehicles. The
information provided by VMPs has a coarse population resolution because it does not tell which cars
pass the devices but only how many vehicles pass through the gates. In terms of observation space,
the two data sets cover the same time period (although the VMPs cover a larger temporal extent). The
VMPs are constrained on specific positions over the road network. Thus, exploiting the spatial
precision of GPS data, all vehicle trajectories were intersected with the VMP locations. As for the
temporal accuracy, the VMPs provide an aggregated count of vehicles on an hourly basis. The
determined VMP passages in the GPS data were therefore aggregated hour by hour. Figure 4 shows
the hourly frequency counts of the VMP and GPS data of one gate in a time graph. It can be clearly
seen that there is a good match between the curves, which essentially differ for a scaling factor. This
is due to a different sampling coverage of the two data sets: VMPs are able to count any vehicle
passing, whereas the GPS data set contains only traces of a subset of all vehicles.
Figure 4: Traffic sensed by a VMP device and GPS traffic volume at one entry gate in Pisa
In order to perform a scaling between the VMP and GPS traffic counts, the authors applied a discrete
wavelet transform (DWT). A DWT is a mathematical tool that projects a time series onto a collection of
orthonormal basis functions and produces a set of coefficients, capturing information from the time
series at different frequencies and distinct times. From the coefficients the authors build a model to
scale the traffic counts obtained from the GPS data sample to the full population. Figure 5 shows the
real VMP series along with the scaled GPS signal and the measured relative error at a selected VMP
location. The error is low when the GPS traffic is high. During the night hours the relative error tends to
grow since there are too few circulating GPS vehicles, but the absolute error is still negligible.
However, for a traffic manager it is crucial to have a precise estimation during the rush hours in order
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to design ad hoc intervention options to avoid congestions, a situation for which our reconstruction
provides a very high precision.
Figure 5: Comparison between real and scaled GPS signal at a single gate in Pisa
2.5.2 Evaluating Mobile Phone Data in the Region of Lausanne
In our second example we explore GPS and GSM data in the area around Lausanne, Switzerland.
The data was collected during the Lausanne Data Collection Campaign, which was carried out by the
Nokia Research Center (NRC). During this campaign the NRC equipped about 200 people living
around Lake Geneva with smartphones (Nokia N95). Each smartphone, equipped with multiple
sensors, collected a wide range of data generated by each participant. All data was continuously
collected for about one year. In 2011 a data set of 38 persons was released to the research
community (
The data set itself consists of both social and geographic data. This includes information about the
GPS Position and the GSM-Cell-ID in which a mobile phone activity, like calls and SMS, took place.
With respect to the dimensions of mobility data discussed in Section Properties of Mobility Data, the
data set covers a large temporal observation space (one year), but is limited in its spatial observation
space (area around Lausanne). Especially the population sample (38 participants) covers only a very
small part of the Lausanne population. The resolution of the GPS data is, like in the first example,
higher than that of the GSM data. GSM only produces a record in the data set if the phone of a
participant is interacting with the mobile network of the operator. GPS, on the other side, is producing
a record every second if the quality of the satellite connection allows it. Another point to mention is that
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in this second example the GPS data is not limited to a particular kind of mobility (e.g. cars) since the
GPS data origins from smart phones, which were carried around by the participants all day long.
This combination of GPS and GSM data makes the data set very interesting, because it gives the rare
opportunity to compare GSM data with ground truth information about the mobility of a person. For this
reason we evaluated the usability of mobile phone data for the extraction of mobility quantities. In the
following we present an excerpt of our results published in (Schulz et al., 2012). In particular we were
interested in measurement quantities that help to enrich and improve existing mobility models. Two of
the analyzed quantities are presented in the following.
Travel distance
In the first analysis we compared the travel distance based on the GPS and GSM data sets. The GPS
data include the distance between any two consecutive GPS points excluding those points inside of a
stop as well as the travel distance considering only the centroid coordinates of stop locations. Against
it, the GSM data set contains the average daily travel distance between consecutive GSM activities
predicated on the estimated GSM-cell centroids. See the table below:
GPS Sequence
GPS Stops
GSM Activity
Table 3: Comparison of average daily travel distances (in km)
The results in Table 3 show that the distance calculated from GSM activity data is only a half of the
travel distance measured by GPS. This is s sign that GSM Data cannot characterize the real daily
travel. On the other hand, if the travel distance is reduced to distance calculated from GPS stops, both
measurements are similar. This leads us to the second analysis, which is about the identification of
frequent activity locations.
Activity Locations
In our second analysis we took a closer look at frequent stop locations. These are defined as locations
where people stay over a longer period of time to do some activities, like work, school or sports. For
this reason we can also call those stops activity locations. After identifying those locations from the
GPS data, we conducted two analyses. In the first one we determined the proportion of GSM activities
that take place within typical activity locations. In the second one we analyzed the number of stop
locations that can potentially be detected through GSM activity data. In both cases more than 2/3
respectively 50% of all frequent activity locations could be detected. In sum this means that GSM
activity data is a good source to identify and analyze activity locations. On the other hand it also
means that GSM activities mostly tell us about where people stay, not where they move, which is
consistent with our analysis about the travel distance.
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2.6 Conclusion
The increasing interest in spatio-temporal microsimulation systems as well as the diversity of available
spatio-temporal data sets call for a new evaluation standard for microsimulation systems. On the one
hand, the standard has to direct researchers and practitioners towards a holistic view on movement
validation. On the other hand, it has to broaden the scope of validation methods to seize the potential
of new mobility data sources. This section compiles a comprehensive overview on the state-of-the-art
of validating spatio-temporal microsimulation systems by providing a systematic overview about
properties of movement data, mobility characteristics, commonly used similarity measures and
validation schemes. A cross section of the state-of-the-art shows that an explicit quantification of the
external model error is often missing, which restrains the comparison of results across different
simulation platforms and data sets. In addition, most evaluations are limited to a small set of mobility
characteristics, focusing typically on either spatial or temporal characteristics. Furthermore,
comparisons are typically performed on the level of persons, trips, activities or movement sequences.
The validation of movement characteristics based on group patterns (e.g. convergence, moving
clusters), however, has not been considered in the literature so far. The major reason for this practice
is the complexity of movement data and the limited availability of external validation data. The former
problem requires a tight interaction between the mobility mining research community and the
transportation research community in order to turn currently complex analysis methods into standard
tools that are generally available. The latter problem is likely to decrease with the increasing
availability of big data sources. However, big data sets bring their own challenges as the data may
cover only a part of the observation space, differ in its temporal resolution or be not representative in
all aspects. Our validation examples using real-world application data show that differing data
properties often hinder a direct comparison, and additional research efforts have to be invested into
the development of data harmonization techniques. In summary, the validation of spatio-temporal
microsimulation systems is a complex task and vital research area which holds many challenging
research questions for future work.
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3 On Benchmarking a Trajectory DBMS
3.1 Introduction
A MOD system combines many different aspects of Mobility under one common framework that has to
be mature enough to support operations such as data loading, indexing, basic and advanced
querying, and data mining (e.g. clustering) efficiently. Also, the experience we gained from the
research project DATASIM recently, revealed the need for a MOD to support new queries that
consider the semantics of a trajectory in addition to its spatio-temporal components. The only
unbiased way to prove a system’s maturity and measure its performance characteristics is to test it
using a suitable benchmark.
We consider a MOD benchmark to consist of three basic dimensions: (1) storage model (2)
partitioning scheme and (3) queries / operators which cover all the aspects mentioned in the previous
paragraph. Each dimension can have multiple alternative implementations, therefore we can, for
example, compare the performance of a range query on two different storage schemes.
Measurements can either be based on the total execution time of an operation or the total space
requirements that a storage option has on disk. Also, an important contribution is that, to our
knowledge, this is the first attempt to introduce semantic queries to a benchmark.
After reviewing the related work in MOD benchmarking in the next section, we then provide a
theoretical description of our benchmark in section 3.3 and conclude with summarizing the current
status and describing the next steps in section 3.4.
3.2 Related Work
In this section, we present the current status of other benchmarks and we attempt to identify the main
characteristics of each approach.
The state of the art framework in the literature for benchmarking MOD engines is the BerlinMOD
(Düntgen et al., 2009). The paper focuses on moving object data generators and two sets of queries
for benchmarking MODs, namely range and k-NN queries.
First the authors present the spatio-temporal data types of the SECONDO system that are used to
implement the generator and the set of queries. The main data type is the moving point and it is
modeled using a “sliced representation” where an instance of this type is expressed by a set of units
(i.e. spatio-temporal segments). Besides, the data types the authors also present the operators that
are used to implement the set of queries.
The benchmark scenario uses synthetic vehicle positions generated using a graph representation of
the road network of Berlin, Germany. The vehicle’s owner is assigned a HomeNode, a WorkNode, and
a Neighbourhood and these nodes are used to generate the trips of the owner between his home and
working place. The scenario assumes that trips from home to work and back to home happen from
Monday to Friday between 8 a.m. and 4 p.m. There is a chance that additional trips might occur in the
spare time and in the weekends which will always end at home.
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To generate each trip a start and a destination node are selected and then a shortest path algorithm is
invoked between them. By following the path while taking into consideration the maximum allowed
speed of each road segment that the driver always respects (in fact he will accelerate in order to reach
that maximum speed every time). Also, the driver will sometimes stops because of traffic, obstacles,
etc. For the trips generated during the spare time the destinations are usually chosen from the
neighborhood of the vehicle owner.
The authors propose two ways to store the data, one that keeps the whole history of an object in one
row (OBA) and another that each trip of the object is in its own row (TBA). Some relations are common
to both approaches, such are: (1) Nodes, that contains all road network nodes (2) QueryPoints,
randomly chosen nodes from Nodes to be used in the queries (3) QueryRegions, random polygons (4)
QueryInstants, timestamps uniformly distributed within the observation period (5) QueryPeriods, time
periods where the starting timestamp is generated as in QueryInstants and the duration (in days) is
sampled from a Gaussian distribution (6) QueryLicences, contains the license plate numbers of the
vehicles. The moving object data are stored in different ways in the two approaches. In OBA the
movement data are stored in a relation named dataScar along with other information like the car type
and model. In TBA the relation dataMcar contains the car type and model information and the relation
dataMtrip contains the trips.
In order to categorize the set of the range queries, the authors identify five query properties (1) Object
identity (known/unknown) (2) Dimension (standard/temporal/spatial/spatio-temporal) (3) Query interval
(point/range/unbounded) (4) Condition type (single object/object relations) (5) Aggregation
(aggregation/no aggregation). By evaluating all the possible combinations of the previous query
properties the authors eliminate those that are meaningless and then proceed with the presentation
and explanation of the final queries.
The k-NN queries are only theoretically investigated as a possible extension to the benchmark in the
future. The problems identified are that there is little work done on k-NN queries for historical trajectory
data and that there is no standard way to formulate those queries in SQL. The authors identify three
query properties (1) Condition type (k-NN/r-NN/a-NN) (2) Query object type (static/moving) (3)
Candidate type (static/moving).
In (Theodoridis, 2003), the authors propose a specific schema for storing the trajectories of a
hypothetical scenario (no real or synthetic data) that involves shoppers moving around an area by
walking or using different transport modes. Based on that scenario the authors categorize a collection
of queries and discuss their performance on different kinds of spatio-temporal index access
mechanisms that exist in the literature.
The database schema stores the movement history of a shopper in one row using the moving point
data type, like the OBA paradigm in (Düntgen et al., 2009). There are other relations for storing
auxiliary data such as roads (polylines), buildings/shops (polygons), and shop offers (temporal
The benchmark queries include (1) loading and update operations (insert and update records and
index entries) (2) range/distance/k-NN/topological queries on stationary reference objects (3)
distance/k-NN queries on moving reference objects (4) join queries (5) unary operators.
The authors also present a taxonomy of spatio-temporal indices based on whether each structure
indexes past or current locations thus supports asking past/current/future queries.
Both (Düntgen at al., 2009) and (Theodoridis 2003), propose using a generator to create synthetic
datasets to be used for the benchmark.
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Other, well-known benchmarks like (Stonebraker et al., 1993) and (Myllymaki and Kaufman, 2003)
are usually limited to the spatial dimension of trajectory data with little or no focus on including the
temporal dimension in their data model and queries.
3.3 Overview of the Benchmark Architecture
In general, a benchmark’s goal is to test the system’s performance on as many “base case studies” as
possible in order to understand and measure its strengths and limitations. The main idea of the
proposed benchmark is the concept of dimensions which aims to identify and separate the most
influential factors in a benchmark. In our approach we identify three dimensions:
The “storage model” dimension is the way we choose to store the data in a relational system.
Essentially, it specifies the structure of the table’s record.
The partitioning scheme dimension is the way we partition (or not) our spatio-temporal
data. The options here could vary significantly since any attribute of the dataset can be
chosen as the partitioning key. In our approach we narrow this to three alternatives all of
which are based on range partitioning.
The “query” dimension is a common and useful question in the spatio-temporal domain that is
posed as an SQL query (e.g. range or k-NN query) to the MOD system and could be
implemented in multiple ways w.r.t. the available storage models. The goal is to measure the
execution time of every implementation of every query.
Figure 6 shows a visual depiction of the dimensions and the operations of the benchmark.
Physical Layer
1st Ba tch
Nth Batch
Storage Model
Semantic SubTrajectory
Range / k-NN
Partitioning Scheme
Space Partitioning
No partitioning /
Single table
Time Partitioning
Space-Time Partitioning
Figure 6: Benchmark Architecture
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3.3.1 Storage Model
The storage model dimension of the benchmark is probably the most important one and it is also the
one that is affected the most by the data types and the access methods the MOD system supports.
Assuming that the data is hosted in one table (either real or virtual in the case of partitioning), we
identify two basic options in this dimension the segment-based and the trajectory-based storage
models. In the first option each row of the table is a segment of the trajectory formed by two
consecutive sampled time stamped positions whilst in the second option each row contains the whole
trajectory of a moving object. The segment-based approach is, of course, the most fine grained
because a segment is the most basic part of a trajectory that represents movement.
The weaknesses of each approach are becoming obvious when we want to build appropriate spatio-
temporal indexes, usually a variation of the 3D R-tree (Theodoridis et al., 1996), for each one of them.
In the “trajectory per row” solution the indexed minimum bounding boxes of the trajectories are
expected to contain excessive dead space. On the other hand, the index on the “segment per row”
solution contains a much higher number of nodes (because every segment occupies a row in the
table) so its growing size is expected to affect its performance.
These shortcomings led to a hybrid third option where each row contains a sub-trajectory that
preferably introduces the smallest possible dead space. This sub-trajectory could have been created
based on semantic information, i.e. each STOP / MOVE episode is considered a sub-trajectory (see
Figure 7), or alternatively by applying a segmentation method on the trajectory to identify sharp
This benchmark focuses on trajectory data that might or might not be semantically enriched (Parent et
al., 2013). It is important that the data input formats are unbiased w.r.t. the loading procedure and
allow the update of existing data in the database since in a real-world cases the objects will
continuously generate new trajectories as they move.
We propose three CSV-based raw input formats that are generic enough to fulfil the previous
Basic-CSV file «ObjectID, TrajectoryID, Timestamp, Lon, Lat»: This is the simplest format in
which every row contains a sampled position of the object’s movement (see Figure 8).
o Note that a dataset in which the movement history of an object is not segmented into
trajectories can be represented with this input format simply by setting TrajectoryID
always to 1.
SubTraj-CSV file «ObjectID, TrajectoryID, SubTrajectoryID, Timestamp, Lon, Lat»: This format
is the same as Basic-CSV, only it is more refined since every position is assigned to a sub-
Semantic-CSV file «ObjectID, TrajectoryID, SubTrajectoryID, Timestamp, Lon, Lat, Episode,
Tags»: Each episode contains multiple contiguous positions of a trajectory thus it is also a
sub-trajectoryFigure 7. Episode can either be “STOP” or “MOVE” and Tags can take any text
value the user wants (see Figure 9).
Because data usually arrive continuously in batches (i.e. a dataset will be composed of multiple files)
and in order to be closer to reality all formats assume that the rows are sorted ascending according to
time and that a trajectory reconstruction phase has happened beforehand.
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Figure 7: Example of a semantically enriched trajectory with STOP episodes painted with dots
For a benchmark to be complete both a synthetic and a real dataset should be used if possible.
Unfortunately, finding a real dataset that is large enough in size to accommodate the benchmark’s
scalability experiments is not always possible. The lack of suitable real datasets has led to the
development of many spatio-temporal data generators (Düntgen et al, 2009), (Theodoridis and
Nascimento, 2000) (Brinkhoff, 2002). However, proposing a new generator is out of the context of this
section, although in the experimental evaluation of our benchmark we explain how we used a data
generator to increase the size of a small semantically enriched real dataset.
237397000,1,2008-12-31 19:29:30,23.5429166584333,38.0261883206083
239157000,1,2008-12-31 19:29:30,25.3790933261087,36.440536655699
239190000,1,2008-12-31 19:29:30,23.5537499918725,37.9476999874264
239203000,1,2008-12-31 19:29:30,25.2334783258433,36.6963183219695
239212300,1,2008-12-31 19:29:30,23.6227833251772,37.9346499874
Figure 8: Sample of Basic-CSV file
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4248,1,1,2013-05-08 07:48:36,23.656687,38.008889,STOP,HOME;RELAXING
4249,1,1,2013-05-08 07:48:36,23.644730,38.001192,STOP,HOME;RELAXING
5,1,2,2013-05-08 07:50:03,23.695998,37.975839,MOVE,TRANSPORTATION;WALKING
10,1,2,2013-05-08 07:50:03,23.696892,37.974372,MOVE,TRANSPORTATION;WALKING
11,1,2,2013-05-08 07:50:03,23.689542,37.967791,MOVE,TRANSPORTATION;WALKING
Figure 9: Sample of Semantic-CSV file
The loading operation takes a batch in a data input format and inserts new trajectories or updates
them if they already exist, i.e. trajectory refresh. For each call of the loader on a batch the total time of
execution is kept in order to see how the loader behaves as new data is inserted in the table and what
is the overhead that the spatio-temporal index introduces. Once the loading procedures complete for
two or more storage models then by comparing the results on space efficiency of each storage model
useful conclusions could emerge.
3.3.2 Partitioning Scheme
The majority of the spatio-temporal indexes proposed in the literature are based on partitioning time,
space, or both (Choi et at., 2004) (Ni and Ravishankar, 2007), Cude-Mauroux et at., 2010). Therefore,
it is very interesting to benchmark how each kind of partitioning performs in conjunction with the
different storage models when a query is executed.
In cases where the volume of the data is very big, which in the case of trajectory data are continuously
updated with new positions, then storing everything in a single table could eventually affect
performance of the queries even if an index is present.
3.3.3 Queries
In this section we identify the types of queries that are most common in the spatio-temporal domain
and for each query type we provide the corresponding expression in natural language. The actual SQL
expression of a query is different in each MOD system, therefore, they will not be discussed in detail
for now.
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Some of the most heavily used queries in trajectory databases are the timeslice, range, and k-NN
queries. The three query types are expressed in natural language bellow:
Q1: “Find the positions of all objects at a specific timestamp”.
Q2: “Find the movement of objects inside a specific area and at a specific timestamp”.
Q3: “Find the object that approached closest to a place and also find when it happened”.
This type of queries is very interesting because it involves operations between two sets. By
benchmarking this query it is possible to see how the system performs under a heavy operation such
as joins. In Q4 there is a self-join query:
Q4: “Find pairs of objects that were located closer than a specific distance at a short period of
time; for each pair, find their minimum distance of one object from the other”.
OLAP queries have the potential to indicate how the MOD system performs on demanding and
complex procedures such as the calculation of origin-destination matrices. The next query can be such
an operation:
Q5: “Perform equi-sized homogeneous partitioning in space and in time (e.g. a 10x10 grid in
space and a 1-day interval in time) and count the number of objects per cell”.
Mining trajectory data usually means that some kind of clustering must take place. These operations
are quite common because by analyzing the result the user can easily identify typical routes or outliers
in the movement of the objects. However, they are resource intensive and for that they are supported
by some kind of index. Practically, a trajectory clustering algorithm must be applied in way similar to
Q6: “Apply a clustering method on the trajectories using a similarity measure between
So far semantic queries are not investigated in a benchmark framework for MODs. Semantic queries
involve pattern matching operations in conjunction with spatio-temporal predicates. The queries bellow
present a few common semantic queries:
Q7: “Find the objects that stopped inside a specific area and within a period of time”.
Q8: “Find the objects that begin their trips from home then go to work and do not return to
home in a specific time period”.
3.4 Roadmap
Hermes MOD is currently in a mature state where it can support a wide range of storage models and
queries. Hermes has been extended to support the querying of semantically enriched trajectory data in
Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
addition of course to the pure spatio-temporal querying support (i.e. range, k-NN, join, OLAP, mining
etc.). The benchmark has been designed to test how Hermes performance scales when executing
queries on big volumes of moving object data. The next steps in this effort are towards applying the
benchmark on Hermes using semantically enriched datasets and to compare the results with those of
a simulated MOD on top of PostGIS. Preliminary tests have shown that Hermes performs better than
PostGIS in MOD-related operations.
Project funded by the European Community under the Information and Communication Technologies
Programme - Contract ICT-FP7-270833
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