# Impact of the COVID-19 pandemic on bike-sharing uses in two french towns

Angelo Furno  LICIT-ECO7, UMR_T9401, ENTPE-Gustave Eiffel University, ENTPE,
Cité des Mobilités, 69675 Bron, France.  Bertrand Jouve∗ LISST, UMR5193,
Toulouse Jean Jaurès University, CNRS, 5 allées A. Machado, 31058 Toulouse
Cedex 9, France  Bruno Revelli  LISST, UMR5193, Toulouse Jean Jaurès
University, CNRS, 5 allées A. Machado, 31058 Toulouse Cedex 9, France  Paul
Rochet  DEVI ENAC, 7 avenue E. Belin, 31055 Toulouse Cedex 4, Toulouse, France
Alix Rigal  LISST, UMR5193, Toulouse Jean Jaurès University, CNRS, 5 allées A.
Machado, 31058 Toulouse Cedex 9, France  Najla Touati  LISST, UMR5193,
Toulouse Jean Jaurès University, CNRS, 5 allées A. Machado, 31058 Toulouse
Cedex 9, France

(Juin 2021)

###### Abstract

Urban areas have been dramatically impacted by the sudden and fast spread of
the COVID-19 pandemic. As one of the most noticeable consequences of the
pandemic, people have quickly reconsidered their travel options to minimize
infection risk. Many studies on the Bike Sharing System (BSS) of several towns
have shown that, in this context, cycling appears as a resilient, safe and
very reliable mobility option. Differences and similarities exist about how
people reacted depending on the place being considered, and it is paramount to
identify and understand such reactions in the aftermath of an event in order
to successfully foster permanent changes. In this paper, we carry out a
comparative analysis of the effects of the pandemic on BSS usage in two French
towns, Toulouse and Lyon. We used Origin/Destination data for the two years
2019 (pre-pandemic) and 2020 (pandemic), and considered two complementary
quantitative approaches. Our results confirm that cycling increased during the
pandemic, more significantly in Lyon than in Toulouse, with rush times
remaining exactly the same as during the pre-pandemic year. Among several
results, we note for example that BSS usage is more evenly spread throughout
the day in 2020, peripheral/city center flow is more noticeable in Toulouse
than in Lyon and that student BSS usage is more specific in Lyon. We also
found that trip duration during the pandemic situation was longer on working
days and shorter on weekends.

##  1 Introduction

††$\dagger$ This research is party funded by the ANR projects
ANR-21-COVR-0027-01 and the LABEX SMS ANR-11-LABEX-0066.††$*$ Corresponding
author<EMAIL_ADDRESS>The authors are sorted by alphabetic
order.

The rapid spread of the COVID-19 pandemic has brought unprecedented challenges
and has been threatening normal human life and global public health. Since the
first identified case of COVID-19 in December 2019 in the city of Wuhan in
China, measures have been taken worldwide and at different scales to stop
virus spread. They concern of course the field of health (research and
administration of vaccines, rapid identification of new clusters, etc.), but
also transportation (limitation of international flights, temporary bans on
inter-regional transportation, etc.) as human mobility has significantly
contributed to virus propagation. In urban areas, many people voluntarily
chose to avoid using public transport, especially due to difficulties to
comply with barrier measures when traveling. In this context, individuals
often re-evaluated their travel options towards more isolated modes such as
private cars, personal or shared bicycles [BSH+20, Buc20].

Considerations of health risks, travel flexibility, traffic reduction, or the
desire to spend less money in times of economic crisis now shape individual
decisions about transportation. More than two years after the beginning of the
pandemic, the eradication of COVID-19 and its variants remains uncertain in
the short term [BLP+21] and the emergence of new pandemics is a scenario now
considered highly probable by the scientific community. Under these
circumstances, several observations show that cycling can be a valuable choice
[PCZZ20, FLBA19, SGG+18, SZQR22], and, according to some, even contribute to
faster economic recovery. In many cities, public authorities are implementing
temporary facilities, strategies derived from “tactical” urban planning, to
accompany this increase in cycling [CM20, Sil16]. Cycling is a largely
flexible, inexpensive mode of urban transport easily adaptable to a wide range
of situations, compared to, for example, public transport, whose adaptation is
often a longer-term challenge [GC21].

The effects of the pandemic on urban mobility have emphasized and amplified
some of the gradual transformations underway so far as a result of the
awareness of the ecological emergency [SSS+22]. These transformations tend to
favor public transport and active or “soft” mobilities [Bea14, RRMZ21]. Among
these, cycling, as opposed to public transport, encourages physical activity,
provides greater freedom of use, reduces environmental impact [ZCL21] and only
requires relatively low-cost deployment. On the other hand, its use is largely
dependent on the weather, topography and urban environment (speed moderation
and bicycle facilities [BIT21]). The development of cycling has been
encouraged by public authorities by setting up Bike Sharing Systems (BSS),
appropriate infrastructures and, more recently and in some countries, by
financial incentives for the purchase or repair of bicycles. These policies
are part of a new paradigm which advocates sustainable, multimodal mobility
(as opposed to the single-mode, infrastructure-focused paradigms of the
automobile and public transport). Thanks to their flexibility in multimodal
trips, BSS uses are strongly related to rail and tube connectivity [BAUT20] in
a noticeably different manner in urban centers and suburban areas [WLG+21,
LJFS20]. However, whenever possible, door-to-door trips are favored [FMC15].

Cycling thus appears to be a resilient and reliable urban mobility option in
the short and long term, compatible with both the health crisis as we know it
and the objectives of sustainable development. Since mobility habits are
difficult to alter [Roc15, MIFCM18, BMC22], taking advantage of changes in
behaviors during the current health crisis and of a cognitive appropriation of
cycling sometimes facilitated by the economic situation is an opportunity and
a challenge for many cities to sustainably transform urban mobility practices
and habits.

The development of BSS is an effective response by public authorities to
increase bicycle use in urban areas [DeM09, SGZ12, HVWM10, PDH10]. For
example, in the year following the introduction of Velo’v (the BSS deployed in
the city of Lyon), a 44% increase in bicycle trips [Büh07] was observed, with
96% new users who had not previously cycled in Lyon’s city center. Similarly,
in Paris, cycling increased by 70% with the launch of the Vélib BSS [Nad08,
SGZ10]. While effective in the short term, several studies have shown that the
development of bike sharing also contributes to a sustainable increase in the
cycling population [DeM09] and that BSS usage is related to bike usage as a
whole [PKD21]. Today, a little more than half a century after the first bike-
sharing system was implemented in 1965 in Amsterdam, BSS have been developed
in many cities around the world and grown increasingly successful [SGZ10,
MYVOH20, EU20] while often relying on quite similar technological solutions.
BSS appeared in France with a pioneer initiative in La Rochelle in 1976 [HP16]
and a fully automated solution (probably the first in the world) was
implemented in Rennes in 1998. But, in France, the generation of BSS as we
have it today (with the need for each user to register a personal account) was
first introduced in Lyon in 2005, followed by Paris and Toulouse in 2007.

The success of bike-sharing systems all over the world has given rise to a
rapid increase in the scientific research on these systems. There have been
several literature review papers summarizing the research done in this field
[EU20, TSeS21, Fis19, MDC21].

The effect of the pandemic on bike share usage has been widely studied, with
data mostly from the year 2020 and collected from cities all over the world.
Some studies use operator data, either Origin/Destination or simply traffic
data. Among these are studies comparing five cities in the USA [Tok20], about
New-York [TL20, WN21, PCZZ20], Chicago [HXLZ21], Lisbon [TSeS21], London
[LZZR21], Singapore [SZQR21], Zurich [LZHA20], Beijing [CGXJ20, SCB+21],
Kosice Slovakia [KKH21]. Several other studies are based on online or on-site
surveys in Thessaloniki in Greece [NAS20], San Antonio in the USA [JG21], in
several Italian towns [BIT21], distributed in several European towns
[MiBSRK22], or with participants from all over the world [BSH+20, BLP+21,
CSS+20].

In a recent paper, [SZQR22] summarizes recent progress in bike-share studies
related to COVID-19. They classify the issues in three main domains. The first
one focuses on spatio-temporal changing patterns in bike-share usage before
and after the pandemic crisis, mainly with data analysis methods applied on
BSS data. The second one is more about comparing different modes of transport.
The third one is centered on user behaviors via survey analysis. [HKB21]
provides an interesting summary of the diversity of published results.

Let us focus on some relevant recent papers falling within the first type of
approach.

With a Bayesian time-series model on monthly aggregated data, [HKB21]
estimates the impact of the pandemic on both trip duration and number of trips
in London. Confirming previous results, in (e.g., [LZZR21, WN21]), the authors
observe that trips are longer than expected during and after the spring 2020
lockdown.

[SZQR22] uses an original graph theoretical approach for comparing flows of
bikes during 4 COVID-relevant periods in Singapore. The proposed approach
provides an adequate means to measure the increase in local trips observed
during and after the lockdown. [PCZZ20] also uses a graph theoretical approach
on New-York BSS data showing the reconfiguration of BSS trips on Manhattan
Island during the lockdown.

[Tok20] leverages a General Linear Model (GLM) to formalize the association of
the month-to-month change in bike-share activity within a geographical tract
and its distance from the CBD. Using weekly aggregated data for 6 towns in the
USA over 6 months, they confirm previous results that distance to the CBD is
positively correlated to BSS usage [FIEEG+14, WLSH15, NSG16, YHTC20] and
clearly explains the dynamics of change due to COVID-19: CBD areas are more
responsive to the pandemic with usage declining faster but also recovering
faster.

To precisely evaluate the impact of the lockdown (and lockdown ease) on BSS
usage in London, [LZZR21] crosses two models: a segmented regression model and
a Bayesian structural time-series model, both on aggregated data at daily
level for the period from January 2019 to June 2020. Rainy conditions and BSS
2019 trips are included in the regression model as binary covariates. The
authors distinguish morning and evening peaks from the rest of the day and
three levels of travel duration (short, middle, long). Their main findings are
that the number of long trips increased during the lockdown and lockdown ease,
which was not observed for short trips, and that the lockdown ease had little
effect on the morning peak which remained low throughout the lockdown. They
also found that immediate effects on BSS usage were more important in highly
infected boroughs than in low infected ones.

With a similar goal of analyzing traffic-bike changes during the lockdown,
[BMC22] uses data collected through 13 and 20 bike counters respectively in
Basel and Zurich (Swiss). To estimate daily cycling traffic, two Random Forest
Regressors are used: one for Working days and one for Weekend days. Both
models depend on the weather, time of the day and whether the day is a holiday
(school or public). Among the main findings, daily traffic during the lockdown
is noted as more complicated than just a mix between pre-lockdown working and
weekend days traffic patterns. It is also found that bike traffic during the
second COVID wave followed the same pattern observed during the first one.
After the first lockdown, bike usage increased more in Zurich than in Basel
although cycling culture is more widely developed in Basel than in Zurich.

The study reported in [HXLZ21] is very similar to ours. Instead of a log-
linear regression model, the authors used Generalized Additive Models (GAMs)
for comparing regular BSS usage between March and July 2019 in Chicago with
the pandemic BSS usage for the same period in 2020. They considered weather
conditions, seasonality and holidays effects. Like previous studies, they
observed that trips were longer in 2020 than in 2019, and bike-sharing more
resilient than other transport modes. They also showed that the pandemic
impact on bike sharing was differential, depending on socio-demographic
patterns around each dock-station.

[SCB+21] proposes a novel method to calculate bikers’ trajectories and
estimate the environmental benefits during COVID-19. The exact route taken by
cyclists could prove to be rather complex to infer. With the same method as
the one proposed in [SCB+21] for Beijing data, we used OSMnx and OpenStreetMap
to calculate the shortest paths between origin and destination for Toulouse
BSS data. Checking the “bike” option in OpenStreetMap proved to be a poor
solution, leading to many highly improbable speeds. Checking the “pedestrian”
option proved instead to be a much better approach. It seems to indicate that
Toulouse BSS riders take shortcuts, not necessarily referenced as cycle
routes. In a still ongoing survey in Toulouse, we asked 125 riders to describe
their route and the results show that 88% match the “pedestrian” option of
OSMnx but the remaining 12% do not follow clear logics. Because of this
difficulty, in the following, we will not use any hypotheses on routes.

In this paper, we propose a spatio-temporal analysis of the COVID-19 impact on
BSS data usage in two French towns, Toulouse and Lyon, which were among the
first cities in the world to implement automated BSS and, therefore, where BSS
use is well established. For this purpose, we consider 2019 as a baseline year
against which we can compare corresponding periods in 2020. To our knowledge,
this is the first study of this type on French cities. In contrast to many
previous papers, we use several levels of data aggregation (week, day, 10
minutes) to improve the accuracy of the results. After describing the data
(Section 2), the following section (Section 3) provides general statistics and
shows that the number of bikes leaving or returning a dock-station is
accurately modeled by a log-linear function of time variables and of the rain
level. Section 4 uses matrix factorization to highlight the presence of latent
explanatory factors, which, for instance, can depend on the spatial
distribution of the stations on the territory, and study their dynamics before
and during the COVID pandemic. Finally, Section 5 provides some
interpretations of the results comparing the two methods and data from Public
Transport and road traffic.

Calculations were made using R software and python language, and maps were
produced with QGIS software.

##  2 Data description and context

In Lyon and Toulouse, a dock-based Bike-Sharing System (BSS) was installed by
JC Decaux in 2005 and 2007 respectively. Since th February 20 2020, and
progressively over the year, half of the Lyon fleet has been equipped with
electric assistance which riders may choose to use or not. From our data, we
have not been able to differentiate both types of users but we did not observe
any significant increase in usage when electric assistance was implemented.
So, we used the collection of all the Origin/Destination trips relative to JC
Decaux BSS for both towns and for the two years 2019 and 2020, without
distinguishing electric or non-electric usage. Each row of the dataset
includes departure time and arrival time (with an accuracy to the minute),
origin station and destination station (with geographical coordinates). The
year 2019 will be considered as a “normal” year, as opposed to 2020 when
France faced the onslaught of the first wave of COVID-19. For each year and
each town, a record of the dataset contains the positioning and timing
information of locking and unlocking of bikes, excluding that of rebalancing
operations. Because data are anonymized, it is not possible to follow a user
in his/her daily use of BSS. We removed trips with duration less than or equal
to 2 min with identical origin and destination, or with a duration more than
12 hours regardless of origin or destination. These data accounted for about
2.3% of each dataset, except for Lyon 2019 for which it is 3.7% with half of
them being trips with identical origin and destination and of less than 1
minute. Please note that our data correspond to real trips, that is, service
operations are counted apart. Following these filters, we considered 287 dock-
stations for Toulouse (no change between 2019 and 2020), for 3.83 and 2.95
million trips in respectively 2019 and 2020 (23% decrease). Lyon had 408 such
dock-stations in 2019 and 430 in 2020, for respectively 8.11 and 6.96 million
trips (14% decrease). As usual, the density of dock-stations is higher in city
centers (Figure 1). Total population is about 1.37 higher in Lyon-Villeurbanne
than in Toulouse (about 670000 people against 490000) for a number of BSS
trips more than twice as high in Lyon. It reveals that the BSS network is
better established in Lyon than in Toulouse, which is also revealed by the
mean number of trips per bike which is about 50% higher in Lyon.

Traffic changes between 2019 and 2020 are not homogeneous among the dock-
stations: while city centers show a high decrease in uses, more periphery and
residential dock-stations are less impacted and can even show some increasing
usage (Figure 1). These observations are consistent with [CGXJ20, HXLZ21,
Kim21] observations in Beijing, Chicago and Seoul respectively, but
inconsistent with [HCC+21] results in Nanjing (China).

Figure 1: Locations of bike-sharing docks in Toulouse and Lyon. Each station
is colored according to the percentage of traffic increase/decrease from 2019
to 2020. To avoid bias in case a station is out of order for a given period,
the corresponding periods of both 2019 and 2020 were dismissed in the
calculation of the percentage.

Dockless systems have emerged, but they are a complementary service to docked-
BSS, with significantly different impact and users [MYVOH20], and will not be
discussed in this paper.

We also have Public-Transport and traffic data for both periods and both
towns. The aim of this paper is not so much to specifically analyze these data
as to give an overview of the various transport dynamics in both towns and
make it easier to interpret the result on BSS use dynamics. We used Floating
Car Data (FCD) to evaluate traffic dynamics. The principle of FCD is to
collect real-time traffic data by locating vehicles via mobile phones or GPS
over the entire road network [ATYT17]. As for Public Transport data, we have
the number of validations per 15 minutes for the different networks (Bus,
Tramway and Subway). Each validation corresponds to an entry of one ID user
somewhere in the transport network. Users are anonymized in such a way that
two entries of the same user have different IDs and then users ‘daily-usage is
not reconstructed.

Weather observation data were provided by Météo-France for each hour of both
years. For each town, we considered data from the station closest to the city-
center (Bron and Blagnac for respectively Lyon and Toulouse). The "rain level"
variable has the highest negative correlation with BSS usages, which is
consistent with previous findings [BPC21]. Moreover, we observed strong
correlations between several weather-related variables. Therefore, in the
following we will just consider "rain level".

Both cities have similar dynamics in pandemic development with rates of
intensive care hospitalization following the same pattern
111https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-
lepidemie-de-covid-19/. After a peak at the end of March 2020, these rates
were very low during the 3 summer months (June-July-August) and then rose
again at the end of September to reach the peak of the second wave around
November 10, 2020. Because of these similarities, data relative to COVID cases
will not be taken into account in our analysis. Finally, below is a short
timeline of the main 2020 dates of pandemic-related measures taken in France:

  * •

January 24: the French Minister of Solidarity and Health confirmed the first
cases of COVID-19 in France;

  * •

March 12 and 13: closure of nurseries, schools, colleges, high schools and
universities; extension of the winter break for two months. Gatherings of more
than 100 people were prohibited;

  * •

March 17: beginning of the first lockdown;

  * •

May 11: end of the lockdown, but meetings with more than 10 persons remained
forbidden;

  * •

June 22: reopening of schools;

  * •

October 17: curfew from 9PM to 6AM declared in the largest cities, including
Toulouse and Lyon;

  * •

October 30: beginning of the second lockdown. Non-essential stores were closed
down and circulation limited, online courses became mandatory for universities
but schools remained open.

  * •

November 28: beginning of ease lockdown, with less restrictions on the
circulation and reopening of stores;

  * •

December 15: end of lockdown but curfew from 9PM to 6AM declared all over the
national territory.

##  3 Time modeling for citywide bike sharing use

For each city, BSS use is measured every 10 minutes over the course of the
years 2019 and 2020 yielding a sample of size $365\times 144=52560$ for 2019
and $52704$ for the leap year 2020. The data consist of the raw number of
trips over the whole city initiated in each 10-minute segment, without any
pre-processing.

Temporal bike usage is modeled as a multiplicative function of time and amount
of rain fall. The time component accounts for the time of the day (among the
144 10 min segments per day), the type of day (working or weekend day), the
week of the year (1 to 53) and holidays. Rain variable is divided into three
levels according to the duration of rain fall within the hour: Low (less than
20 min), Medium (between 21 and 40 min) and High (more than 41 min).

The proposed model, in the spirit of [HXLZ21], is as follows

$BSS\approx f(\text{time, type of day})\times g(\text{week})\times
h(\text{holidays})\times\ell(\text{precipitation}).$ (1)

The functions $f,g,h$ and $\ell$ are estimated non-parametrically via a linear
model on the logarithm of $BSS$, with all variables treated as factors. Since
the data are measured every 10 minutes, the estimation of the first function
$f$ accounts for 144 parameters associated to the typical BSS use distribution
during a working day (one for every 10 minute segment) and 144 parameters for
the distribution over a weekend day. The remaining parameters correspond to
the weekly evolution along the year (53 values) represented by the function
$g$, an additional parameter adjusting for the 11 holidays of the year and 3
parameters for the three levels of rain fall. Accounting for identifiability,
we reach a total of $343$ parameters for $52217$ (resp. $52361$) degrees of
freedom for 2019 (resp. 2020) in both Toulouse and Lyon. The $R^{2}$ in the
linear model varies between $0.97$ and $0.99$ in the four scenarios (Toulouse
and Lyon in 2019 and 2020). Nearly all parameters are highly significant
(p-value < $10^{-16}$) with at most three exceptions corresponding to night
values with very low use. The amount of rain fall is the only non-temporal
variable and, while it is highly significant in the model, it only accounts
for less than $0.5\%$ of explained variance in each case.

Let us first notice that holidays induces a mean decrease of $25.65\%$ in BSS
use. Similarly, the weather is responsible for a $23.1\%$ decrease in BSS use
for a medium level of rain fall and for a $26.2\%$ in case of a high level,
the two coefficients being highly significant. Nevertheless, the overall
periodic time components (time of the day, type of day and week) accounts for
more than $96\%$ of explained variance, compared to less than $1\%$ for
holidays and rain fall.

Figure 2: Mean BSS use distribution over the course of a working-day and
weekend-day in Lyon and Toulouse in 2019 and 2020 provided by the model.
Curves are normalized so that the total sum of use over the year is equal to 1
for each curve.

Figure 2 are the daily components for our 4 models. Since the $R^{2}$ is
nearly 1 for all 4 models, these curves can be considered as representative of
the patterns of daily-BSS use corrected by weather and holidays biases.
Because there is some variability depending on the period of the year (see
Figure 4), we did not normalize these curves by the number of annual trips and
only the shapes of the curves should be considered. The distribution of daily
BSS uses is extremely similar in 2019 and 2020 for working-days with a
correlation of 0.989 in Lyon and 0.985 in Toulouse. The four visible traffic
peaks occur at the same time in Toulouse and Lyon both in 2019 and 2020,
namely around 8:45 AM, 12:15 PM, 1:45 PM and 6:15 PM. The cities show similar
differences in behavior in 2020, with a small decrease in the 8:45 AM peak
compensated by a higher and more spread-out use during late morning and the
beginning of the afternoon. In contrast, the last three peaks almost perfectly
overlap in 2019 and 2020. The decrease of BSS-use in the morning rush during
the pandemic situation was observed in previous studies but often more
pronounced [HXLZ21, BMC22, KKH21, LZHA20]. The morning peak completely
disappeared during the pandemic situation, which is not what we observed even
when focusing on different relevant periods of the year (Figure 4). The
relative increase in BSS use we observed around noon was also observed in some
of these papers but a normalization of the data would be necessary to
highlight them. What is surprising, however, is the absence of two peaks
around noon in all previous studies. It may reflect different habits, or just
the fact that these studies consider data with a one-hour accuracy, whose
effect is to aggregate the two peaks. The use at night greatly decreased in
2020 in both Lyon and Toulouse, which can easily be explained by the curfews
imposed by the French government.

On weekend-days, rhythms in Lyon and Toulouse are very similar with more
variability in 2020 between the two peaks and a drop in usage around 1:30 PM.
The presence of two peaks was observed in [SS21] but not in [LZHA20], [GY19]
or [BMC22] where continuous increase in usage from noon to the end of the
afternoon is probably due to data aggregation. The afternoon peak occurs
slightly later compared to the highest congestion hours in these cities,
situated around 5:45 PM.

We also observe a peak of traffic around 0:30AM in 2019, on weekends,
especially for Lyon. In our Public Transport data for Lyon, it coincides with
a peak of underground exits (preceded by a high number of entries just before
and around midnight). It illustrates the importance of BSS/underground inter-
modality, which is mainly asymmetric (BSS followed by the underground, or the
other way round depending on the time of the day).

Figure 3: (Top) Weekly evolution of BSS use over the course of 2019 and 2020
in Lyon and Toulouse provided by the model. Curves are normalized so that the
total sum of use over the year is equal to the observed value for each curve.
The lockdown periods in 2020 are represented by gray areas (17 march to 11 may
and 30 October to 15 December). (Bottom) Weekly evolution of road traffic (FCD
data) and Public-Transport entries, plotted from the raw data.

Figure 3 (top) shows the weekly use of shared bikes for both years and both
towns. These two plots are the weekly components of the model. As for the
daily component, it differs from the raw data in that weather and holidays
effects are corrected, even if they account for little in the explained
variance of the data. For a year without pandemic (2019), Toulouse and Lyon
curves have the same profile, characterized by a low number of trips during
summer holidays followed by a higher number between mid-September and mid-
October. The difference between the two extremes varies from nearly one to two
in both towns. The end of the year, when many people take vacations, is a
second period of low use in both towns. These simple observations show that
shared bikes are largely used for working activities, which is reinforced by
the magnitude of the morning and evening peaks in Figure 2.

We have tested a model accounting for different weekly behaviors for working-
days and weekend-days, i.e. with a component $g(\text{week, type of day})$
instead of $g(\text{week})$ in Eq. (1) and the additional parameters were not
statistically significant. This indicates that any possible weekly evolution
difference between working-days and weekend-days is insignificant to explain
weekly BSS use along the year.

In 2020, rhythms are still similar for both towns but differences exist
concerning the pandemic effect. If the number of trips is largely reduced
during the lockdowns, the ratio between Lyon and Toulouse remains the same as
outside the lockdown with approximately twice as many trips in Lyon. Less
strict than the first one (schools remained open and many sectors were able to
continue their activity, even if universities switched to online courses), the
effect of the $2^{nd}$ lockdown is lower. An interesting period is the one
after the first lockdown. Very quickly, in 2 weeks for Lyon and 4 for
Toulouse, the number of bike trips exceeds the 2019 levels. This phenomenon is
much more important in Lyon where the number of uses even increased by a
quarter for some weeks from one year to the other. The jump in BSS usage
observed right after the first lockdown, particularly in the city of Lyon,
appears to be consistent with previous results for western cities ([BSH+20,
Tok20, WN21]). The difference in the amount of time required to get back to
the level of the year 2019 after the $1^{st}$ lockdown in the two towns is due
to the decrease of BSS use in 2019 in Lyon in the period between mid-May and
mid-June. It corresponds to a special event when, within 2 weeks, a large part
of the BSS-bike fleet was stolen. Without this decrease, the amount of time
required to get back to the level of 2019 would have been similar in both
towns and approximately equal to 1 month. This is similar to what [CGXJ20] was
observed in Beijing but twice shorter than in Brooklyn [WN21] and in Chicago
[HXLZ21].

Figure 3 (bottom) allows a comparison with two other types of urban transport,
private cars and Public-Transport. Please note that for FCD data, all the
urban territory is under consideration, including urban highways around the
cities. The first observation is that patterns are very similar in both towns
and neither Public-Transport nor road-traffic recovered in 2020 their 2019
levels. The summer period is clearly less impacted in both towns. Public
transport’s resilience to the pandemic was particularly poor since it remained
largely underused until autumn 2020, with patterns for the weeks following the
first lockdown which are similar to those during the second lockdown. In both
towns, the decrease is about 40%, as compared to 2019, which is similar to
observations in Shenzhen showing a decrease of 34% [RRMZ21]. With a latency of
one month as for BSS, road-traffic volumes (as represented by the observed
fleet of vehicles in the available FCD data) return to stable levels (outside
summer) better than those of Public-Transport and reach around 85% of the 2019
levels for both towns. The order highlighted here regarding the impact of the
pandemic on the three modes of transportation is consistent with previous
findings [MiBSRK22]. Please note that the impact of COVID-19 on these three
mobility types in Lyon is very close to what has been observed in Zürich
[BMC22].

(a) Lyon

(b) Toulouse

Figure 4: Comparison of the average BSS use during a working-day in Lyon and
Toulouse for the two lockdown periods and the period right after the first
lockdown.

In spite of the high quality of fit, Figure 3 shows high variations of bike
usage in 2020 depending on the considered period of the year. So, it is worth
looking at how BSS traffic bike is distributed over one typical day for three
periods: the first and the second lockdown, and the weeks just following the
first lockdown (see Figure 4). The graphs on the left of Figure 4 show that
the heavy restrictions of the first lockdown in France almost completely
obliterated BSS use. Nevertheless, three of the four peaks can still be
perceived and occur at the exact same times, around 8:45 AM and 6:15 PM.

The weeks following the lockdown reveal different BSS uses in the two towns.
In Lyon, the slight morning peak decrease is more than balanced by a late
morning and early-afternoon spread-out. It is also worthwhile noticing that,
right after lockdown, the evening peak recovered its 2019 level which shows
how resilient BSS usage is. In Toulouse, the observed pattern is different,
with a volume of usage which does not get back to its pre-pandemic level. The
almost-halved morning peak is strongly impacted and this is not balanced by
the rest of the day with the three other peaks also largely diminished while
inter-peak periods recovered their 2019 level. Under the hypothesis that
working from home was equally distributed in Lyon and Toulouse, it suggests
that BSS is more resilient in Lyon than in Toulouse for commuting. Comparing
these patterns with those of Zurich and Basel [BMC22] would put Zurich close
to Lyon and Basel close to Toulouse. This is consistent with the fact that BSS
is slightly more established in Zurich and Lyon than in Basel and Toulouse.

Finally, the impact of the second lockdown was much less noticeable than the
first one with a similar day pattern as usual and only a moderate decrease in
volume. Lyon and Toulouse show a very similar pattern. It is worth noticing
that, with or without COVID, the morning peak for working-days is, for this
period of the year, preceded by a smaller one (around 7:50 AM in Lyon and 8 AM
in Toulouse), and this phenomenon is more obvious in Lyon. This small peak,
somehow present in the March-May period in 2019, reveals a bimodal
distribution which probably deserves being further studied.

Figure 5: Comparison of trips duration over the different time periods for
Lyon and Toulouse merged data. First ($Q_{1}$), second ($Q_{2}$, median) and
third ($Q_{3}$) quartiles are plotted. The boundaries of the whiskers are the
$10^{th}$ and $90^{th}$ percentiles.

Consistent with previous findings [LZHA20, TL20, HXLZ21, RRMZ21], trip
duration increased during the pandemic situation, as pictured in Figure 5.
However, this overall increase masks opposite situations between working and
weekend days, which have been little documented so far. Distributions are
highly skewed due to the presence of a few long (more than $2$ hours) trips,
not visible in the boxplots. An analysis of variance has been conducted on
trip duration trimmed of values below one minute and above two hours in order
to handle the outliers and skewness. It shows that the differences in trip
duration between 2019 and 2020 are highly significant for working-days on the
first four periods but becomes less visible in the second lockdown (p-value
$\approx 0.011$).

Weekends display the opposite. While distribution shifts toward longer trips
for working-days, it shifts toward shorter trips for weekend-days. Long
weekend trips tend to disappear in 2020, as opposed to longer commutes. These
observations constitute a major difference when compared to Zurich for which
[LZHA20] reported an increase in duration also during weekends with a
completely different behavior. However, our results are in line with [TL20]
data for New-York, readable in some of their figures and tables.

##  4 Main spatio-temporal patterns of hourly BSS use

In this section, we used Exploratory Factor Analysis (EFA) [Jol02] to
investigate the spatio-temporal effects of the pandemic on BSS usage. EFA is
an unsupervised data compression technique that allows extracting from data a
small number of latent factors that meaningfully summarize the covariance
matrix of the observed features (i.e., variables).

We chose to extract two distinct EFA models from the available datasets, one
for Toulouse and one for Lyon, each covering the two years 2019 and 2020. In
both models, the observed variables correspond to the $p=287$ (resp. $p=430$)
dock-stations in Toulouse (resp. Lyon), for which we observe the number of
trips which begin at a given station in a given one-hour slot. Therefore, for
each station, we have a total of $q=17544=24\times(365+366)$ observations.
Each one-hour time slot represents a population sample (i.e., an individual)
on which all variables are observed. Unlike our previous analysis in which we
distinguished between working-days, weekend-days or periods of the year on the
basis of prior knowledge of relevant factors, we now assume that we do not
have such preliminary knowledge, and thus aim to learn directly from the data
the factors that can best explain existing similar temporal patterns between
the different stations.

In the fundamental equation of the EFA model, observed variables are expressed
as linear combinations of a given (fixed) number of (unknown) common factors
plus an additive term that can be considered as noise, or, in EFA terminology,
as the uniqueness associated to each observed variable. Based on such
principles, EFA fundamentally differs from Principal Component Analysis
[DWD16] in the sense that factors are constructed by assuming that the
covariance of the observed variables could be decomposed into two parts, i.e.,
a common one and a unique one, while in PCA all variance in the observed
variables is analyzed and used for the determination of the components. In
other words, PCA aims to find components that allows reproducing (exactly) the
observed variance as linear combinations of the identified components, while
in EFA focuses on the interpretation of the relationships among the variables
and on identifying the latent factors which explain such relationships as
noisily observed in the data covariance matrix.

Following the usual EFA terminology, we call (common) factors the set of K
latent “variables” which makes up the construction of the observed variables
and factor loading matrix the set of numerical relationships describing how
much each factor explains each (observed) variable. The fundamental equation
of factor analysis is thus the following:

$\mathbf{X=\Lambda F+U}$

where ${\bf X}$ is the $p$-vector of observed variables, ${\bf F}$ is the
$K$-vector of unknown factors, $\boldsymbol{\Lambda}$ is the unknown $p\times
K$ matrix of factor loadings and ${\bf U}$ is the unknown $p$-vector of error
variables (uniqueness) associated to each of the $p$ observed variable.

Factor loadings can be interpreted as correlations between factors and
observed variables (the stations in our case) and range from $-1$ to $1$.
Loadings close to $-1$ or $1$ indicate that the factor strongly influences
(negatively or positively, respectively) the observations (time dynamics in
our case) of the station. Loadings close to $0$ indicate that the factor has
little influence on the station. For each loading and for both towns, we can
draw a map where each dock-station is geographically located with a circle
whose color is function of the loading value. These maps will be called
“loading maps”.

Each entry of ${\bf U}$ is called a unique factor. Since each unique factor is
specific to one variable, the error terms are independent and
$\mathrm{Cov}({\bf U})$ is a diagonal matrix.

As part of the EFA methodology, it is possible to estimate (e.g., via
regression analysis) factor scores, i.e., the $q$-sized vector of coefficients
for each of the K factors over the sampled population. Factor scores can have
negative and positive values and are usually standardized. In our case study,
the scores for a given factor represent the evolution of that factor over the
one-hour time slots related to the two years 2019 and 2020, and thus allow
visualizing the temporal dynamics of BSS usage for the set of dock stations
highly represented by the considered factor (i.e., stations exhibiting high
loadings on that factor).

More details on the EFA model, the solving procedure to compute factor
loadings and uniqueness, as well as approaches to select the number of $K$
factors and to estimate factor scores can be found in the well-established
literature on the subject [Mul09, CL13, Jör78].

The following analysis has been performed using the departure data, but a
similar one on the arrivals has been conducted, producing identical
conclusions. Therefore, only the data from departure are presented to avoid
redundancy. As previously discussed in relation to factor scores, time series
have been centered and scaled during the factor analysis process, which means
that the comparisons between stations is based on their pace and not their
intensity.

###  4.1 Technical choices

Factor extraction was performed using the Minimum Residuals (MINRES) approach
[HJ66]. The working principle of MINRES is to minimize the sum of off-diagonal
squared residuals of the correlation matrices of ${\bf X}$ (the observed one
and that reproduced from the identified common factors). Unlike maximum
likelihood estimation [Law40], MINRES does not rely on any assumption about
the distribution of observed variables and can produce valid solutions even
when applied to singular matrices [Jör78, BM03].

An important design choice concerns the number of common factors that EFA
should target. We rely on Parallel Analysis (PA) [Hor65], which uses the
eigenvalues of the observations’ correlation matrix as rough estimates of the
actual common factors. Specifically, PA compares such eigenvalues with those
obtained from a random correlation matrix for which no factors are assumed.
The presence of common factors shall induce large eigenvalues: the number of
factors is set to the lowest rank above which all data eigenvalues are larger
than those from the uncorrelated variables. In our case study PA yielded 23
(resp. 27) factors for Toulouse (resp. Lyon). They account for 41.5% (resp.
53.6%) of the total variance.

Once a set of factors is extracted, it is usual to perform a rotation in order
to produce a more interpretable and simplified solution [Abd03]. Varimax
rotation over BSS data worked best in that respect. It is an orthogonal
rotation (which thus keeps factors uncorrelated) which maximizes the sum of
the variance of the squared loadings.

In the following, factors are ordered in decreasing order of the variance they
explain. Regarding the variance explained by the 22 and 26 common factors
respectively, the first 3 factors for Toulouse and first 4 factors for Lyon
account for 82% of this variance in each case. For both Toulouse and Lyon, two
more factors are of interest by their particular spatio-temporal distribution.
Lower-ranked factors are either restricted to a handful of stations or
concentrated in a very small time period (e.g., a particular day). Therefore,
in the following we will consider only 5 factors for Toulouse and 6 for Lyon.
The $3^{rd}$ factor in Lyon and the $4^{th}$ factor in Toulouse, which are
representative of the new bike stations installed by end of 2019 / beginning
of 2020, will not be discussed here as they are deemed to be irrelevant to the
scope of the paper.

###  4.2 Factors for Lyon vs Factors for Toulouse

In the loading maps, only stations with loadings greater than .2 are plotted.

####  4.2.1 The morning peak

(a) Hourly representation of the $1^{st}$ factor scores - Lyon (left) and
Toulouse (right)

(b) $1^{st}$ factor Loading Map - Lyon (c) $1^{st}$ factor Loading Map -
Toulouse

Figure 6: The EFA factors representing the morning peaks in Lyon and Toulouse.

The first factor points out a dominant morning peak for both Lyon and Toulouse
for working-days in 2019 and 2020, which completely disappears during the
first lockdown. For both towns, it is the only factor that shows significant
scores for this morning peak and for a large number of stations. This factor
is mainly associated with departures from residential areas. This phenomenon
is particularly observable in the city of Toulouse where residential areas are
more clearly separated from business districts and activity centers. Loadings
for the city of Lyon appear to be lower in the most touristic zones, such as
the “Presqu’Ile”, and in La Doua university district. This can be explained by
considering that such locations have very particular traffic patterns at this
time of the day. It is worth noticing that, in the city of Lyon, the first
factor also shows low but significant positive scores for the end-of-afternoon
peak, which is not the case for Toulouse. This can be again explained in terms
of the urban fabric associated to the corresponding areas of the city of Lyon,
which are characterized by a more mixed residential-work land use than in
Toulouse. As a consequence, dock stations in these zones show some non-
negligible activity (bike trip departures) also at the end of the afternoon,
probably due to people leaving their work places at the end of the day.

####  4.2.2 The end-of-afternoon peak

(a) Hourly representation of the $4^{th}$ factor scores for Lyon (left) and
$2^{nd}$ factor scores for Toulouse (right)

(b) $4^{th}$ factor Loading Map - Lyon (c) $2^{nd}$ factor Loading Map -
Toulouse

Figure 7: The EFA factors representing the end-of-afternoon peak in Lyon and
Toulouse.

Factor 4 for Lyon and factor 2 for Toulouse appear to be representative of the
end-of-afternoon peak particularly related to work places during the 5
working-days. Several differences in this end-of-the-afternoon peak can be
highlighted between the two towns. First of all, as one of the effects of the
COVID-19 pandemic, the end-of-afternoon traffic peak in 2020 took longer to
get back to pre-pandemic levels in Toulouse than in Lyon. This is probably
partly due to the important role that university student-related usage play in
this factor for Toulouse (the loading map show that dock-stations near the
universities are particularly active) and the fact that a lot of them left the
city of Toulouse to go back to their home cities and benefit from online
courses. Secondly, we can notice that this end-of-afternoon factor is more
concentrated, both in time and space, for the city of Lyon. Stations concerned
by factor 4 for Lyon are in fact located in the main work places of the city,
while the whole city-center (plus the university campuses) is concerned by
factor 2 in Toulouse. This can be explained again in terms of the different
fabric of the two cities and the fact that the end-of-afternoon peak related
to business activities is already partially represented by factor one,
together with the morning peak, for stations belonging to areas with a more
mixed residential-work behaviors.

Finally, among all the factors, these factors are the only ones that count
positively for working-days (days with a positive score sum) and negatively
for weekend-days (days with a negative score sum). Figure 8 shows that these
two factors have pretty different time profiles. It is difficult to explain
such differences in more detail without further study, but comparisons with
university holidays appear to confirm the fact that student usage plays a
fundamental role in this Toulouse BSS factor profile. In this respect, the
performed EFA analysis does not bring out a specific factor for student
profile of BSS usage in Toulouse, but it detects one such factor for Lyon
(Figure 9). The loading map corresponding to factor 5 for Lyon is in fact
perfectly associated to university campuses, and university holidays or online
courses restriction period due to the COVID pandemic.

(a) Lyon ($4^{th}$ factor)

(b) Toulouse ($2^{nd}$ factor)

Figure 8: Daily sum of all station scores over the $4^{th}$ factor for Lyon
and $2^{nd}$ factor for Toulouse.

(a) Hourly representation of the factor scores

(b) Daily representation of the factor scores (c) Loading Map

Figure 9: The $5^{th}$ EFA factor representing university student BSS usage in
Lyon.

####  4.2.3 The late-evening and night-time factor

(a) Daily and hourly representation of the $2^{nd}$ factor scores for Lyon
(left) and $3^{rd}$ factor scores for Toulouse (right)

(b) $2^{nd}$ factor loading map for Lyon (top) and $3^{rd}$ factor loading map
for Toulouse (bottom)

Figure 10: The EFA factors representing the late-evening and night-time BSS
usage in Lyon and Toulouse.

The daily representation of the scores highlights that this factor is
predominant on Saturdays, while loading maps pinpoint bike stations close to
areas with high concentration of bars, restaurants and other leisure
activities (e.g., Hotel de Ville, Bellecour, Berges du Rhone, and La
Guillotiere in Lyon, Place Saint Georges, Place du Capitole, Place Esquirol
and Place Saint-Pierre in Toulouse). Our results for Lyon, for example, are
consistent with those of Nicolas Chausson, who studied urban nighttime
centralities [Cha19]. Interestingly, the curfew imposed between 9PM and 6AM
starting from mid-October is clearly visible on the hourly representations for
both towns. Particularly for these last two weeks of October, we observe a
peak of traffic between 8PM and 9PM, right before the curfew, probably due to
people using bikes to go back home.

####  4.2.4 The parks and stadiums

(a) Daily and hourly representation of the $6^{th}$ factor scores for Lyon
(left) and $5^{th}$ factor scores for Toulouse (right)

(b) $6^{th}$ factor loading map for Lyon (top) and $5^{th}$ factor loading map
for Toulouse (bottom)

Figure 11: The EFA factors relative to parks and stadiums BSS in Lyon and
Toulouse. In the loading maps, only stations with loadings greater than .2 are
plotted.

The $6^{th}$ factor in Lyon and the $5^{th}$ in Toulouse are representative of
BSS usage near parks and stadiums (see Figure 11): Ile Barbe, Saint-Rambert,
Tête d’Or, Mulatière and Gerlan in Lyon, Sept Deniers, Jardins Raymond VI,
Prairie des Filtres, Jardin des Plantes and les Argoulets in Toulouse. Scores
are high during weekends. It can be noted that on the first weekend after the
end of the first lockdown (16 and 17 May 2020), bike traffic gets back to pre-
pandemic levels near parks and, in an unusual way, remains high during the
following two weeks (particularly in Lyon), even though this period did not
correspond to vacation time and schools had reopened.

##  5 Conclusion

In this paper, we investigate the evolution of Bike-Sharing System (BSS) usage
for two French cities, Toulouse and Lyon, during the COVID-19 pandemic
situation.

We first compare the evolution of the number of bike rentals for both towns
and along the two years 2019 and 2020, with a 10-minutes time granularity. In
this analysis, data from all the dock-stations have been aggregated, thus
ignoring any spatial considerations. The results show that these time series
can be clearly explained as the product of two main effects: the annual
tendency and daily evolution. Our multiplicative regression model shows very
good fitting scores for both towns, taking into account school-holidays and
rain fall. Secondly, we use Exploratory Factor Analysis (EFA) to derive
spatio-temporal patterns in BSS usage over the two years. In the data, dock-
station spatial granularity is preserved, thus permitting to observe and
interpret, via the retrieved latent factors, the dynamics at the level of
dock-stations instead of that of the entire network as in the first study. EFA
highlights five and six latent variables, globally explaining around 50% of
the variance for Toulouse and Lyon respectively. The additional latent
variable available for Lyon shows a clear student-related BSS usage, which is
merged with another factor in the case of Toulouse.

Among the most relevant changes in the hourly evolution of BSS usage between
2019 (without COVID) and 2020 (with COVID), the global network level analysis
shows a proportional increase of BSS use during late morning and early
afternoon for working days, in both towns. Nevertheless, the four daily peaks
remain located at the same time in 2019 and 2020 in both towns. Concerning BSS
traffic volume, morning peaks appear as the most impacted by the pandemic,
particularly in Toulouse. The BSS traffic profile in Lyon is definitely more
than resilient, it displays a boost of activity between the two lockdowns
(from mid-May to end of October), which Toulouse does not. Road traffic and
Public-Transport have been much more impacted than BSS. None of them appears
to get back to pre-COVID levels. After the first lockdown, with a similar
1-month delay and until the second lockdown, with the exception of summer
holidays, road-traffic stabilizes at approximately 85% of the 2019 values for
both towns. The traffic flow of public transport has the same dynamic in both
towns, recovering a maximum of 60% of its value after the first lockdown and
75% of its value after summer holidays. This is consistent with previous
findings in other towns. We found that trip duration significantly increased
during the pandemic period for working-days but not for weekend-days. These
findings are relevant and original with respect to other analyses performed on
other towns. Concerning weekend BSS traffic, we also observe that the midday
peaks are 15-minute later in Toulouse than in Lyon, in 2019 and 2020.

Concerning the spatio-temporal analysis performed with EFA, both towns display
a morning peak mostly associated with departures from residential areas. This
is particularly noticeable in the city of Toulouse, where residential areas
appear to be more separate from business ones. An end-of-afternoon peak of BSS
traffic is clearly detected by EFA with respect to areas with high density of
business and activity centers in the city of Lyon, while it covers a large
portion of the whole city center of Toulouse. This BSS usage profile appears
to take longer to get back to pre-pandemic levels in Toulouse than in Lyon,
even though this phenomenon may be partially explained for Toulouse by student
activity (and the related summer holiday break) associated to dock stations
included in this pattern. For Lyon, a specific factor is associated to student
activity, clearly showing the prolonged effect of lockdown, restrictions and
holiday breaks which affected student-related bike usages.

Interestingly enough, the EFA analysis clearly identifies a BSS usage pattern
in the proximity of parks and stadiums for both cities. After the first
lockdown, BSS traffic near these zones appears to be relatively higher in 2020
than in 2019, during weekends and, surprisingly, during the two weeks
following the end of the lockdown despite the fact that it was not a vacation
period. This could indicate the population’s need to reconnect with social and
nature activities after lockdown stay-at-home restrictions.

Our results globally show that BSS appeared as a resilient mobility
alternative during the pandemic situation. It probably indicates an increased
preference towards cycling in general. Without surveys, it is however
difficult to know which part of the road-traffic decrease can be put down to
some new cycling usage, to smart working, or other mobility choices. For this
reason, as future research is to shed light on this question, we aim to
complete this study by coupling it with an analysis of surveys that are being
currently collected in the two analyzed cities. The findings of this paper
could help transport operators and public authorities better identify and
understand cycling behavioral changes in order to respond even more
efficiently to an emergency situation, such as the one created by the
pandemic, to be prepared for future crises as well as to understand the
current opportunities in terms of bicycle use and to set up the conditions for
the perpetuation of new cycling practices towards a more resilient and
efficient mobility.

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# On The Power of Subtle Expressive Cues in the Perception of Human Affects

Ezgi Dede
College of Engineering
Koc University, Istanbul, Turkey
<EMAIL_ADDRESS>
Kamile Asli Agilonu
College of Engineering
Koc University, Istanbul, Turkey
<EMAIL_ADDRESS>
Ergun Akleman
Visual Computing & Computational Media, School of PVFA
Computer Science and Engineering, College of Engineering
Texas A&M University, College Station, TX, 77831
<EMAIL_ADDRESS>
Metin Sezgin
College of Engineering
Koc University, Istanbul, Turkey
<EMAIL_ADDRESS>
Joint with Computer Science and Engineering Department.

###### Abstract

In this study, we introduce a sketch-based method for testing how subtle
expressive cues influence the perception of affect in illustrations of human
figures. We specifically study the impact of human posture and gaze direction,
implicitly specified through nose orientation, on perceived emotions and mood.
Through a series of user studies using sketchy illustrations of a running
figure, where a professional illustrator manipulated gaze direction through
adjustments on the nose orientation, we found that this simple change resulted
in a diverse range of perceived affects, spanning from fear to concern and
wonder. These findings shed light on the importance of fine details in
defining context for context-aware system designs and underscore the
importance of recognizing and expressing affect. Understanding minor
expressive cues is crucial to developing emotionally intelligent systems
capable of expressing affect.

##  1 Introduction and Motivation

Understanding how to perceive human affects such as feeling, emotion,
attachment, or mood from affect displays is critical for a wide range of
applications, from affective computing and context-aware systems to visual
storytelling.

Figure 1: Expressions such as anger, happiness, sadness, pride, and fear only
with posture without any facial expression.

Affective displays can be any type of expressive cues, such as facial, vocal,
or gestural behavior, that serves as an indicator of affect VandenBos (2007).
Unfortunately, in the scientific literature, only facial, vocal, and gestural
expressions are studied Ekman and Oster (1979); Ekman and Keltner (1997);
Ekman (1999); Russell et al. (2003). However, expressions can go beyond
facial, vocal, and gestural expressions. As shown in Figure 1 even body
posture can be sufficient to convey human affects Liu et al. (2012); Akleman
et al. (2015). Using standard scientific processes, it is hard to classify and
categorize such unusual and subtle expressions. In this work, we have
developed a methodology to evaluate such unusual and subtle expressions. Using
this methodology, we have demonstrated that the perception of human affects
can be significantly changed even by subtle expressive cues of affective
displays, such as gaze direction.

###  1.1 Context & Motivation

The main problem with understanding human affects is that expressive cues of
affective displays can be subtle and context-dependent. These cues can come
from a wide range of sensory input. To understand human affects, it is
important to consider all types of sensory input, even if a cue appears to be
relatively minor. It is also important to process these cues in their context
to recognize the underlying human affect. Small details such as the addition
of a few droplets have already been shown to change and exaggerate the
expression of human affects Akleman and Celik (2020) (see Figure 2).
Therefore, every cue from a small detail to overall body posture can be
crucial in understanding human affects by providing a rich contextual
expression.

Figure 2: Droplets help to change and exaggerate perceived expressions Akleman
and Celik (2020).

Unfortunately, the interpretation of subtle cues is not yet well understood in
the scientific literature. Expert actors, comic book artists, cartoonists, and
animators know how to create subtle and context-aware expressive cues
effectively. However, the knowledge of these artists is gained through
apprenticeship training obtained by interacting with other experts. Only a
handful of artists formally describe their methods Johnston and Thomas (1981);
McCloud and Martin (1993); Blair (1995); McCloud (2006); Eisner (2008a, b);
Celik (2011). User study procedures that are common in the scientific
community are too complex to discover the impact of a wide variety of subtle
context-aware cues. Simple procedures are needed to discover the effects of
subtle cues. For this goal, we have developed a middle ground by combining
expert knowledge with very simple user studies to quickly test information
provided by experts. We develop user studies with extremely simple sketch-
based illustrations. This simplifies the creation of the data set. On the
other hand, we work with expert cartoonists to create the data set. Therefore,
we can be sure that the set is in good condition for user studies.

Figure 3: This example shows that props such as droplets can be used to
significantly increase the intensity of perceived expressions. Droplets near
the eyes create a sad expression, droplets on the forehead give the impression
of fatigue, and droplets under the nose give the impression of sickness even
in a neutral face. For more examples see Akleman and Celik (2020).

###  1.2 Basis and Rationale

Understanding subtle expressions of human affects in their context is crucial
in human interactions. Individuals are expected to behave within certain norms
required by the context, such as gatherings or work environments. As the
context also depends on the culture, we can see problematic interactions
between people originating from different cultures. A person coming from a
culture that has strict boundaries of personal space would feel uncomfortable
in an environment where people do not acknowledge such boundaries. Thus, it is
important for any computational agent working with humans to analyze and
understand the context in which they are working. This raises the question:
What is the context? In ubiquitous computing, a formal description is given as
"physical activity, location, and the psychophysiology and effective state of
a person" Bulling and Roggen (2011).

In the field of human-computer interaction, context-aware systems are
frequently used because they offer easy use to users when they adjust their
functionalities to the given context. An example would be an intelligent
system designed to receive voice commands during emergencies. For example, in
the event of a house fire, it is expected that the caller will speak louder
and faster than usual. If the system is not designed to adapt to this
situation and understand the user, the consequences of this failure would be
critical.

###  1.3 Contributions

In this work, we develop a formal evaluation methodology to evaluate the
impact of subtle expressive cues. Our methodology is based on methodical
sketches drawn by expert illustrators. Using these sketches, we study the
effect of a particular expressive cue in detail. Using user studies, we can
validate or reject a hypothesis on the effect of a particular subtle
expressive cue.

In this work, we also provide a proof of concept for the method. using our
methodology, we have demonstrated that a relatively subtle change in a drawing
can make a significant change in the context created by the person’s effective
state. As a result, we argue that context-aware systems should be designed to
detect and interpret subtle expressive cues, since small elements can create a
completely different context.

(a) Set-0: A wire person (see Figure 9 for the full set).

(b) Set-1: A wire person with a hat (see Figure 10 for a complete set).

(c) Set-2: A solid person slightly behind viewpoint (see Figures 5 and 6 for
the full set).

(d) Set-3: A solid person with side view (see Figure 12 for the complete set).

Figure 4: An artist created sets of sketchy illustrations to test the validity
of our hypothesis. These are some examples of four sets that provide
simplified visual depictions of running people.

To run the user study, an expert illustrator initially created four different
sets of illustrations with different levels of visual detail (see Figure 4 for
two samples from each set). For this study, by discussing with the
illustrator, we chose to use the solid drawings shown in the figures of the
sets 4(c) and 4(d) because they provided less ambiguity and their body
components were proportionally more accurate and robust. They also had eyes,
which reduced the ambiguity of the head of the figure. Finally, we chose to
use the side view shown in Figure 4(c) and added the gaze direction to further
reduce the ambiguity.

The complete set of data we used for our study is shown in Figures 5 and 6. We
focus on investigating the effect of the gaze direction of a running man on
the effective context perceived by the viewer. We have shown these images to
independent volunteers and asked them to describe the event that took place in
the scene and the emotion associated with the figure. From the results, we
have observed that the change in the direction of the nose and eyes creates
different contexts and emotions even though the rest of the body is the same.
We have also considered an additional illustration set, adding gaze direction
as a second variable to see if that affects the context as well; however, the
limitations of our user study did not allow us to investigate that variable
(see Figures 5 and 6).

##  2 Related Work

One of the main influences in the creation of different expressions in
illustrations comes from Ollie Johnston and Frank Thomas, two of Walt Disney’s
leading teams of animators known as the Nine Old Men Johnston and Thomas
(1981). They also presented the animation principles developed by Disney
Studios to the general public. The animation principles were later adapted to
computer graphics applications by another legendary animator John Lasseter
Lasseter (1998). Another influential animator in understanding and creating
subtle expressions by sharing his vast practical knowledge is Preston Blair
Blair (1995). Cartoonists such as Scott McCloud McCloud and Martin (1993);
McCloud (2006) and Will Eisner Eisner (2008a, b) also contributed greatly to
the understanding of this problem. We can also list Hakan Celik and Ergun
Akleman who showed the importance of some subtle expressive cues Celik (2011);
Akleman and Celik (2020).

Despite relatively few publications on the art side, there is a large body of
literature on the intersection of human-computer interaction and psychology to
analyze and understand affective states Picard (2000, 2003); Tao and Tan
(2005). Harmon et al. classify affective states in three main dimensions:
valence, arousal, and motivational intensity Harmon-Jones et al. (2011, 2013a,
2013b); Gable and Harmon-Jones (2013). We observe that subtle cues can also be
important in analyzing these dimensions effectively.

(a) Facing down and front.

(b) Facing straight front.

(c) Facing up and front.

Figure 5: The illustration set with eye-gaze direction variable.

(a) Facing down and back.

(b) Facing straight back.

(c) Facing up and back.

Figure 6: The illustration set with eye-gaze direction variable.

##  3 Methodology

The key part of our methodology is to create a simple set of sketch-based
illustrations that can be easily shown to participants in the user study. We
have three hypotheses that we want to investigate with the user study:

  1. 1.

Looking behind while running will invoke a context happening out of the
ordinary compared to looking ahead while running illustrations in Question-1.

  2. 2.

Looking behind while running will trigger more negative emotion descriptions
compared to looking ahead while running illustrations in Question 2.

  3. 3.

The intensity of the emotion will increase when the illustrated man is looking
up or down, compared to the man looking straight in Question 3.

For the user study, we collected data using Qualtrics in Turkish. The
volunteer group consists of 54 random individuals from different age groups,
occupations, and gender preferences. The answers to these questions are taken
from the online platform without direct contact with the participants. No
personal data is collected during this process.

It might be worth noting that they all share the same cultural heritage as the
experiment was conducted only by Turkish citizens. Illustrations are presented
in a random order to counter-balance the order effect. For every illustration,
we asked participants to answer three open-ended questions:

  1. 1.

What do you think that is going on in this illustration?

  2. 2.

How does the illustrated man feel at the moment?

  3. 3.

Can you rate the intensity of this emotion from 1 to 10? (1 means "almost
none" and 10 means "overwhelmingly").

We refrain from offering choices to the participants to avoid inducing bias.
We considered the position "facing front" as our baseline and compared our
results from different positions with respect to that position. If a user
identified an emotion but did not include the intensity, we did not include
that entry in our result analysis. Users who did not complete all the
questions but answered all the questions related to a single illustration were
included in the results of that illustration.

##  4 Results

To analyze our results, we have used one of the methods of qualitative
analysis called Grounded Theory. We counted the number of participants who
have used "running" and "fleeing" while describing the illustrated figure. A
chi-square analysis of X2(2) = 13,64, p<0.01 showed significant results for
using the word ’running’ to describe a man facing the front compared to
describing a man facing the back (see Table 1). However, the results were not
significant for the "fleeing" word count (see Table 2).

To describe the man facing down and front, 21 descriptions made out of 54 in
total gave details about the reasons for looking to that specific position.
Similarly, 19 descriptions made for the man facing up and front explained the
reasons for that direction. On the other hand, only five descriptions out of
54 descriptions made for the facing-front illustration tried to explain the
reasons for looking that way.

The intensity comparison showed that the back conditions produce significantly
higher intensity for emotions of fear, concern, curiosity, and excitation
compared to the front. Furthermore, facing up yielded high intensity scores
for emotions such as happiness, curiosity, and excitation, while facing down
yielded high scores for fear, concern, ambition, and anxiety. Lastly, the
difference in the intensity of emotions between facing up and facing ahead is
relatively high; people tend to rate emotions more intensely when the
character is facing up.

(a) This table shows the number of participants who used the word "running" to
describe the illustrated man.

(b) This table shows the number of participants who used the word "to flee" to
describe the illustrated man.

##  5 Discussion

The immediate result to report is the change of words while describing the
action when the eye and nose orientations change. Tables 1 and 2 show the
usage of the words "running" and "fleeing", respectively. We see that the word
running is used more often when the figure is facing the front, whereas the
word "fleeing" is used when the figure is facing backwards.

Some participants reported that they have focused on the absence of some
features in addition to the features existing in the illustrations. For
example, one participant reported: "I had thought the guy was running a
marathon, but then I realized that there was no sweat drawn. Thus, I have
changed my answer." Some participants perceived the illustrations as scenes
following each other and responded accordingly. For example, one participant
responded "Somebody threw a rock to the guy" while describing the man facing
up and back, then followed with the description "The rock did not hit the guy,
fell next to him." illustration facing front and down.

(a) Facing Back and Front.

(b) Facing Up and Down.

Figure 8: These two tables show the intensity rating comparison for different
emotions. Table 3 compares the highest emotion ratings for facing back and
front conditions. B: facing back condition intensity rating. F: Facing front
condition intensity rating. Table 4 compares the highest emotion ratings for
facing up and down conditions. U: facing up condition intensity rating. D:
Facing down condition intensity rating. .

We have seen that when the figure is facing down, both the descriptions and
the emotions suggested for those scenes had negative valence. In contrast,
positive valence emotions were attributed to the figure facing up with one
exception of hopelessness. To measure the significance of the associated
emotions, we use an intensity value. We acquire the intensity for each emotion
for that illustration by summing the intensity values assigned by the
participants. It should be noted that a single participant can assign a
maximum of three emotions to a given illustration. We then look at the
difference between the intensity of the same emotion in different poses for
comparison. If an emotion is named in one pose and not in the other, its
intensity value is considered 0 for the figure that lacks the emotion for this
analysis. Table 3 shows the results by comparing the figure facing forward and
facing backward. As we can see from the table, the words "fear, concern,
curiosity, excitation" are more associated with facing backward than forward,
while "thoughtful, tired, happiness" is more associated with facing forward
than backward. Although no conventional methods to express emotions (such as
eyebrows and mouth shape) were used in the illustration, the positioning of
the figure itself was enough to attribute these emotions to the figures. This
means that our internal states that are shaped by cultural and personal
preoccupations are affected by small details in shaping a context. Moreover,
this study shows that creating and describing a context is more complex than
it seems, since it is a compilation of many fine details that may be obscure
initially.

We believe that future studies will benefit from a more detailed analysis of
the context and the elements that create the context. A proper understanding
of the context and its implementation will greatly improve the performance and
user experience in intelligent user interfaces.

##  6 Conclusion and Future Work

In this work, we have developed a method to test hypotheses about the impact
of subtle expressive cues on the perception of human affects. Using this
method, we show that gaze direction in a running person can change perceived
human affects. Although our method can be used to formally demonstrate the
impact of other subtle cues, we think that a formal scientific user study can
be overkill for most of these effects. Our study demonstrates that there is a
need for a series of short publications from animators, illustrators, and
cartoonists to explain the impact of some of these affects using their
artworks as examples.

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Figure 9: Set-0: wire-person running.

Figure 10: Set-1: wire-person running holding a hat.

Figure 11: Set-2: solid-person running, viewed from slightly behind viewpoint.

Figure 12: Set-3: solid-person running, side view.