Ride Acceptance Behaviour of Ride-sourcing Drivers
Peyman Ashkrof, Corresponding author
Department of Transport and Planning, Delft University of Technology, Delft, Netherlands
Email: [email protected]
Gonçalo Homem de Almeida Correia
Department of Transport and Planning, Delft University of Technology, Delft, Netherlands
Oded Cats
Department of Transport and Planning, Delft University of Technology, Delft, Netherlands
Bart van Arem
Department of Transport and Planning, Delft University of Technology, Delft, Netherlands
Abstract
The performance of ride-sourcing services such as Uber and Lyft is determined by the collective
choices of individual drivers who are not only chauffeurs but private fleet providers. In such a
context, ride-sourcing drivers are free to decide whether to accept or decline ride requests assigned
by the ride-hailing platform. Drivers’ ride acceptance behaviour can significantly influence system
performance in terms of riders’ waiting time (associated with the level of service), drivers’
occupation rate and idle time (related to drivers’ income), and platform revenue and reputation.
Hence, it is of great importance to identify the underlying determinants of the ride acceptance
behaviour of drivers. To this end, we collected a unique dataset from ride-sourcing drivers working
in the United States and the Netherlands through a cross-sectional stated preference experiment
designed based upon disparate information conveyed to the respondents. Using a choice modelling
approach, we estimated the effects of various existing and hypothetical attributes influencing the
ride acceptance choice. Employment status, experience level with the platform, and working shift
are found to be the key individual-specific determinants. Part-time and beginning drivers who
work on midweek days (Monday-Thursday) have a higher tendency to accept ride offers. Results
also reveal that pickup time, which is the travel time between the driver’s location and the rider’s
waiting spot, has a negative impact on ride acceptance. Moreover, the findings suggest that a
guaranteed tip (i.e., the minimum amount of tip that is indicated upfront by the prospective rider,
a feature that is currently not available) and an additional income due to surge pricing are valued
noticeably higher than trip fare. The provided insights can be used to develop customised matching
and pricing strategies to improve system efficiency.
Keywords: ride-sourcing, ride-hailing, transport network companies, ride acceptance behaviour,
ride-sourcing drivers’ behaviour, shared mobility
2
1. Introduction
Recent technological innovations in the mobility sector have facilitated the emergence of new
modes of transport with ride-sourcing. Offering door-to-door transport services, these two-sided
ride-sourcing platforms match passengers requesting rides through a mobile app with semi-
independent drivers who do not only serve as chauffeurs but also act as private fleet providers.
Ride-sourcing drivers mention benefiting from a considerable degree of flexibility, freedom, and
independence as the most indispensable determinants for them choosing to join the platform, in
one of the most prevalent examples of the gig economy (Ashkrof et al., 2020; Hall and Krueger,
2018). Drivers can freely decide where and when to drive for the platform. These choice
dimensions dynamically impact the supply-demand intensity and limit the control of the central
platform over drivers. Moreover, once ride-sourcing drivers decide to drive and select their
working shift and area, they receive ride requests and can choose whether to accept or decline
them. Drivers’ choice making has far-reaching consequences for the system performance. For
instance, a delayed response due to the low acceptance rate of drivers increases the waiting time
of a rider and thus yielding a lower level of service. No response to a ride request decreases rider
satisfaction and may affect customer retention. In both cases, this can have a direct and indirect
negative impact on drivers’ earnings and the platform profit. Xu et al. (2018) report that
approximately 40% of the ride-hailing requests are aborted and receive no response from drivers,
which carries considerable implications for the system performance.
A successful match between demand and supply is the key objective in ride-sourcing operations
to safeguard the mutual interests of the actors. The rider is transported from the specified pickup
point to the desired location while drivers providing the service earn money, and the platform
making the matching obtains a profit. Notwithstanding, while passengers aim to minimize the trip
costs, waiting and travel time, drivers’ objective is to maximize their earnings and minimize idle
time. The platform itself strives mostly for profit maximization and satisfying its paying customers.
Hence, the matching process is non-trivial due to the need to satisfy contradictory objectives and
choices of the stakeholders. That is why various policies and matching strategies are adopted to
keep the balance between agents’ interests. In such a novel economy, special attention should be
devoted to drivers as service suppliers who make the final decision on ride requests impacting the
rider satisfaction as well as the platform reputation and revenue. Nonetheless, since the entry of
the ride-sourcing business into the market, the relationship between platforms and drivers has been
fragile. Judging by the worldwide strikes and lawsuits filed around the world, an increasing tension
has recently been observed due to the dissatisfaction of drivers with their working conditions
(Hamilton and Hernbroth, 2019). Such dissatisfaction may cause distrust (Rosenblat and Stark,
2015; Wentrup et al., 2019) that can influence drivers’ choices, particularly ride acceptance
behaviour. Therefore, a win-win efficient matching strategy considers the utilities and limitations
of all the parties through the purposeful assignment of ride requests with the nearby drivers with
the highest acceptance probability. To assess this probability, it is crucial to gain a better
understanding of the supply-side behavioural dynamics under different circumstances.
Research devoted to the supply side has hitherto been primarily focused on operational dimensions
such as pricing strategies (Nourinejad and Ramezani, 2019; Xue et al., 2021), relocation guidance
(Zha et al., 2018), matching strategies (Chen et al., 2021; Ke et al., 2021), and estimated travel
time (Wang et al., 2018). In most cases, it is assumed that the fleet is operated by either fully
automated vehicles which are not currently and may not be soon in operation (SAE International,
3
2018) or perfectly compliant rational drivers, whereas the evidence suggests that drivers’
multidimensional and autonomous decisions can significantly influence the system performance.
A growing body of literature in both journalistic and academic formats have attempted to
qualitatively and quantitatively investigate the labour properties of digital on-demand mobility
services. Analysing a sample of around 18,400 taxi drivers working in the United States, Wang
and Smart (2020) argued that the hourly income of taxi drivers has declined since the introduction
of Uber. Leng et al. (2016) concluded that monetary promotion increases drivers’ acceptance rate
and reduces their idle time using the 40-day trip data of 9000 ride-sourcing services collected in
Beijing. Zuniga-Garcia et al. (2020) proposed a framework to measure ride-sourcing driver
productivity (i.e., the profit per unit time) based on the spatial and temporal variation. They found
out that the principal element in ride-sourcing driver productivity is trip distance. Based on the
findings, short trips result in lower productivity even in high-demand areas. Through a nine-month
qualitative study into the Uber driver working experiences, Rosenblat and Stark (2015) reported
that Uber manages the labour force and gains a soft control over drivers using algorithmic labour
logistics such as pricing and information dissemination strategies, which constantly interact with
drivers and shape their behaviour.
Ride-sourcing platforms collect and utilize historical and real-time information of the demand and
supply sides to match ride requests with available drivers. This information is processed and
selectively shared with the platform drivers to keep the balance between match quality (the
attractiveness of a match – for both riders and drivers) and match rate (the number of matches
within a specific time interval) which can conflict (Romanyuk, 2016). Aiming for a high match
rate compels drivers to accept less attractive requests which leads to low match quality. On the
other hand, a low match rate increases the waiting time for passengers and thereby lowering their
satisfaction and loyalty. Moreover, it reduces the occupation rate of drivers, which is affecting
negatively drivers’ income and may contribute to traffic congestion (Beojone and Geroliminis,
2021), as well as decreases the platform revenue and its control over the workforce. Therefore,
maintaining this balance improves system efficiency and the two-sided user experience.
To find such a balance, an in-depth understanding of the behaviour of individual agents is needed.
Despite the extensive literature on various aspects of the demand side, the supply-side behaviour
remains so far largely unknown. Conducting a focus group study with ride-sourcing drivers
working in the Netherlands, Ashkrof et al. (2020) proposed a conceptual framework that
characterises the relationship between tactical (working shift selection) and operational decisions
(ride acceptance and relocations strategies) of drivers and the potentially related factors. They
reported the distinctive behaviour between part-time and full-time drivers, as well as beginning
and experienced drivers. In a closely related paper, Xu et al. (2018), found that ride requests with
economic incentives such as surge pricing are more likely to be accepted by drivers. To the best
of our knowledge, our research is the first study that attempts to comprehensively investigate the
quantitative effects of various existing and hypothetical determinants on drivers’ ride acceptance
behaviour through undertaking a cross-sectional stated preference (SP) survey. The findings can
provide new insights for algorithm developers, platform providers, policymakers, and researchers
working in this field. The focus of this original empirical study is on the unique data collected
from Uber and Lyft drivers working in the US where the ride-sourcing platforms have emerged
and thrived. Moreover, the target group is extended also to drivers working for Uber and ViaVan
(a European shared on-demand transit service) in the Netherlands to tentatively examine the
transferability of the results to the European context. Since the survey has been conducted during
4
the pandemic crisis, we also examine the effects of related views and attitudes on drivers’ ride
acceptance choices.
The remainder of this paper is organised as follows: Section 2 explains the methodologies applied
for the data collection and the data analysis processes. Section 3 focuses on the study results
including the descriptive analysis, the exploratory factor analysis, and the choice modelling
estimation. Lastly, the findings are discussed and the paper is concluded in Section 4.
2. Methodology
2.1. Choice Modelling
Due to the binary decision of accepting or declining a request, the choice modelling approach is
applied to analyse the data at the disaggregated level and estimate the effects of the identified
attributes. This method is based on the probabilistic choice theory that assumes that the decision-
making process has a probabilistic nature (Bierlaire and Lurkin, 2020; Hensher et al., 2005;
McFadden, 1974). Although humans are presumed to be deterministic utility maximizers, the full
specifications of the utilities are unknown to the analyst. This causes stochasticity that is addressed
by the so-called Random Utility Maximisation (RUM) approach capturing the unexplained
variation using random variables. The utility function of alternative
!
for individual
"
is
mathematically formulated as follows:
#
!"
$
%
!"
&
'
!"
Eq. (1)
Where
%
!"
and
'
!"
, which are typically assumed to be two independent and additive contributors
of the utility function, represent the systematic (deterministic) part and the error term (random
parameter), respectively.
%
!"
is assumed to be a linear association of the observed variables
presented in Eq. (2):
%
!"
$
(
)
!#
*+
!#
#
#$%
&,
(
)
!&
*+
!&
&
&$%
&,
(
)
!'
*+
!'
(
'$%
,
Eq. (2)
The first term includes the instrumental variables (
+
!#
-,
that are incorporated in the SP choice sets
such as drivers’ spatiotemporal status, passenger characteristics, and ride attributes. The second
component is associated with the individual-specific attributes
.+
)#
-,
such as socio-demographic
characteristics of the drivers. The third component
.+
)#
-
corresponds to the corona-related
attitudes.
)
!#
,
)
!&
,
)
!'
represent the marginal impacts of the instrumental attributes, individual-
specific factors, and attitudinal variables respectively.
Given that the attitudes of individuals cannot be observed directly, a set of measurable variables
are defined to identify the attitudinal factors and include these latent variables in the deterministic
part of the utility function. The so-called Hybrid Discrete Choice (HDC) model integrates the latent
and explanatory variables either sequentially or simultaneously (Ben-Akiva et al., 2002). To
capture drivers’ attitudes toward the Covid-19 pandemic, the latent variables were initially
identified by conducting an Exploratory Factor Analysis (EFA). Thereafter, a sequential approach
was used to incorporate the factor scores of the latent constructs into the systematic utility.
The second component in the utility function is the error term that captures the unobserved effects
and randomness in choices. This component is constructed based on distributional assumptions on
the joint distribution of the error term vector,
'
"
$
.
'
%"
/0/'
)"
-
*
It is typically assumed that the
5
random variables are independently and identically distributed (IID) under an EV1 (Extreme Value
Type 1) distribution:
1%
(
2/3-
, with
345
.
Based on the RUM method, the probability of alternative
6
to be chosen by individual
"
from the
binary choice set
786/!9
is equal to the probability that the respective utility of alternative
!
%
is
larger than the utility of alternative
!
*
. Eq. (3) represents the probabilistic model:
:
"
.6;
8
!
%
/!
*
9
-$
Pr(U
𝑗
!
n
≥#
U
𝑗
"
n) = Pr(
%
!
!
"
,
+
'
!
!
"
<%
!
"
"
,
+
'
!
"
"
)
= Pr(
'
!
"
"
=
'
!
!
"
>%
!
!
"
=,%
!
"
"
) Eq.
(3)
In other words, Eq. (3) reflects that the probability of choosing alternative
!
%
by individual n is
dependent on the observed attractiveness of alternative
!
%
over alternative
!
*
(
%
!
!
"
=,%
!
"
"
-
and also
the difference in random terms (
'
!
"
"
=
'
!
!
"
).
The software package PandasBiogeme (Bierlaire, 2020) was employed to estimate the choice
models using the Maximum Likelihood Estimation (MLE) approach. The objective of MLE is to
find parameter estimates by maximising the likelihood function which includes the choice
probabilities related to the alternatives chosen in the data. The likelihood function is formulated in
Eq. (4):
?
+,
$,
@ @ @
.:
"-!
-
.
#$%
/
/$%
,
-$%
+
"$%
Eq. (4)
Where
A
is the number of choice tasks shown to individual
"
, ,
:
"-!
represents the choice
probability obtained from the model, and
B
"-!
is a dummy variable that is equal to 1 if alternative
!,
from the choice set
C
is chosen by individual
"
, and 0 otherwise.
2.2. Choice Experiment Design
Central operators apply various information-sharing policies which yield a partial disclosure of
information about ride requests and the characteristics of passengers and drivers. Such policies are
adopted by ride-hailing platforms which leverage on the inherent asymmetry in access to
information, providing drivers with limited information when making work-related decisions.
Specifically, ride acceptance behaviour is affected by such policies that restrain the thorough
assessment of the ride quality (Ashkrof et al., 2020). In both the US and the Netherlands, the
information provided to drivers is remarkably limited. Most notably, trip fare and final destination
are not shown to drivers before ride acceptance. This so-called blind passenger acceptance is meant
to avoid destination-based discrimination (Smart et al., 2015) but at the same time, it can decrease
the income for drivers (Rosenblat and Stark, 2015). Despite such ambiguity, drivers can still
evaluate the attractiveness of incoming requests based on the available information to maximize
the utility of ride acceptance (Ashkrof et al., 2020).
In this study, two scenarios are defined based on the platform information-sharing policy: Baseline
Information Provision (BIP) and Additional Information Provision (AIP). In both scenarios,
drivers are requested to decide whether to accept or decline ride requests based on a finite set of
information provisioned. The BIP scenario mimics the current system operations where a driver
needs to decide on the ride request based upon their current spatiotemporal status, ride attributes,
and passenger characteristics. Then, some additional - currently unavailable - information such as
monetary features about the same request, is added in the AIP scenario giving drivers a second
chance to make a choice. This enables investigating which and to what extent factors impact the
decision of drivers in the existing system setting, as well as examining drivers’ response to the
6
information that is not currently available for them. Moreover, some studies including Morshed et
al. (2021) argue that the covid-19 pandemic has influenced the demand side which can potentially
affect how drivers make choices such as accepting more/fewer rides, changing working shift or
relocation strategies. That is why the attitudes of drivers towards the pandemic are also
investigated in this research. To this end, a cross-sectional SP survey has been designed to collect
the required data for further analysis.
Figure 1 illustrates the information provision set-up in the SP choice experiment. Drivers receive
a ride request associated with certain characteristics and they then indicate their choice to accept
or decline it. This is the BIP scenario that simulates what drivers presently experience and provides
the following set of relevant information:
- Request time: The time when a ride request (ping) pops up.
- Waiting/idle time: The duration between the last drop-off and the incoming ride.
- Previous ride status: Whether the previous ride request has been declined or not.
- Pickup time: Travel time between driver’s current location and rider’s waiting location.
- Type of request: Private or shared rides.
- Rider rating: The average rating of the rider given by drivers.
- Surge pricing: A bonus for drivers offered by the platform when demand (locally) exceeds
supply.
- Driver’s location: The type of built environment where the driver is located.
- Long trip (30+ min): Whether the ride takes more than 30 minutes.
Once drivers make a decision, they are given more information, which is currently unavailable,
about the same ride while the baseline information is still shown. The additional information
in the AIP scenario includes:
- Trip fare: The gross amount of trip fare.
- Guaranteed tip: We hypothesize that passengers can indicate how much they are willing to
tip when requesting a ride and this info can be shared with drivers when a ping pops up.
As soon as the ride request is matched, the specified amount of tip is enforced in case the
trip is successful.
- Congestion: The estimated delay between the pickup point and the destination caused by
traffic congestion.
7
Figure 1: Information provision set-up in the SP choice experiment
In order to generate the experimental design of the SP survey, we identify the alternatives,
attributes, and attribute levels and thereafter the type of design, model specifications, and
experiment size are determined. This process is replicated with the updated input to ensure all the
elements are in line with the research objectives. In the context of the choice dimension taken into
account, Accept and Decline is the binary decision of drivers on ride requests which are considered
as the alternatives and the information shown in each scenario are the attributes. Table 1 shows
the attributes, attribute levels and labels derived by the current system operations, literature,
interview with drivers, and posts on drivers’ forums and then adjusted through a soft launch of the
survey.
Table 1: Attributes, attribute levels and labels
BIP
Attributes
Attribute levels/labels
Request time
Pivoted around the working shift
Type of request
Uber X, Uber Pool
Waiting/idle time (min)
0, 5, 10, 15
The previous request status
Declined, Accepted
Rider rating (stars)
3, 4, 5
Pick-up time (min)
5, 10, 15, 20
8
Driver’s location
City centre, Suburb
Surge price
0, 1.5, 3
Longer than 30 min
Yes, No
AIP
Estimated trip fare
8, 16, 24
Guaranteed tip
0, 1.5, 3
Delay due to traffic congestion (min)
0, 15, 30
Except for request time, the levels and labels of all the variables are specified in the table. UberX
and Uber Pool refer to the private and shared-ride services, respectively. Waiting/idle time ranging
from 0 to 20 minutes in this survey indicates the duration of the driver’s idle status since the last
drop-off. The previous request that has been declined is assumed to play a role in ride acceptance.
The average rating of the riders is always shown to the drivers. Travel time between the location
of driver and rider varies between 5 and 20 minutes in this experiment. The location of the driver
is presumed to be either in the city centre or suburb. Surge pricing is a value that is added to the
trip fare when applicable. If a trip is estimated to be taking longer than 30 minutes, drivers are
notified in advance. Estimated trip fare, guaranteed tip, and the delay due to traffic congestion that
are not currently available in the app are shown in the AIP scenario.
Request time is assumed to be pivoted around the reported working shift of the respondents. This
is because ride-sourcing drivers can freely select their working shift and area thanks to the flexible
labour model. Given that demand and supply intensity significantly varies at different times of the
day as well as days of the week, drivers may have various experiences depending on the selected
working shift. The pivot design ensures that drivers’ can relate to the temporal characteristics of
the experiment by closely resembling the experienced context to improve the response reliability.
This also helps to compare the behaviour of individual drivers on different days of the week and
various time slots such as peak or off-peak hours and the beginning or end of the shift.
To set up an individual-specific experiment, the segmentation procedure is applied. In this
procedure, a set of designs is constructed to segment the population based upon multiple identified
reference points (Rose et al., 2008). In this study, time of day is clustered into five categories:
morning (5-11), midday (11-15), afternoon (15-19), evening (19-23), night (23-5) and also drivers
are assumed to start their shift in one of these categories and work for either 4 hours a day (half a
shift) or 8 hours a day (full shift). Therefore, the working shift in a day is divided into 10 groups
as shown in table 2. Each column indicates a separate working shift that corresponds to a group of
drivers. Accordingly, a library of designs is generated for the request time that has three levels in
each working shift. These levels represent the beginning, the middle, and the end of the shift,
respectively. Ultimately, each respondent is systematically assigned to one of these pre-defined
designs based on their reported working pattern. For example, a driver who starts his/her shift at
16:00 and works for 4 hours in a day is assigned to the Afternoon 4 hours column, hence, the
request time levels for this driver will be 17:00, 19:00, and 21:00.
9
Table 2: Segmentation of the request time based on the working shift of drivers
Morning
(5-11)
Midday
(11-15)
Afternoon
(15-19)
Evening
(19-23)
Night
(23-5)
8h
4h
8h
4h
8h
4h
8h
4h
8h
4h
Request time
8
12
16
8
10
12
13
17
21
13
15
17
17
21
1
17
19
21
21
1
5
21
23
1
2
6
10
2
4
6
To construct the design matrix, the efficient design method is used to generate an efficient
combination of the attribute levels by minimizing the possible standard errors of the parameter
estimates. These standard errors are estimated by calculating the roots of the diagonal of the
asymptotic variance-covariance (AVC) matrix which is obtained from the negative inverse of the
expected second derivative of the loglikelihood function of the discrete choice model as expressed
in Eq. 5 and Eq. 6 (Rose and Bliemer, 2009):
D
%
$,=E1
+
F
0
"
'12((
03
%
!
&
!
03
%
"
&
"
G
H
4%
-
Eq.(5)
Given:
0
"
'12((
03
%
!
&
!
03
%
"
&
"
$
I
( ( (
+
!
!
#
!
-
+
!
"
#
"
-
:
!
!
-
:
!
"
-
,/,,,,,,,,,,,,,,,,,,,,,,,,,,6J,!
%
K,!
*
,
!5/
#$
-5,
#
+
"$%
=
( ( (
+
!
!
#
!
-
+
!
"
#
"
-
:
!
!
-
.L=:
!
"
-
-,/,,,,,,,,,,6J,!
%
$,!
*
,
!5/
#$
-5,
#
+
"$%
Eq.(6)
Where
D
is the AVC matrix,
1
+
denotes the large sample population mean,
)
!#
,
M$L/0/M
!
represents the parameters of alternative
!$L/0/N
.
Then, the so-called D-error which is the determinant of the AVC matrix is used to set up the most
efficient design with the adequately low
O=PQQRQ
(Bliemer and Rose, 2010). Since no prior
information about the parameters was available,
O
6
=PQQRQ
(priors equal to zero) was initially
used to construct the choice sets. A pilot of 50 responses was conducted to obtain the priors. Then,
O
7
=PQQRQ
was applied to minimize the standard error of the estimated parameters and
reconstruct the experiment design accordingly. The following equations present the mathematical
formulation of the
O=PQQRQA
:
,
O
6
=PQQRQ
$
STU.D
%
.V/5--
%89
Eq. (7)
O
7
=PQQRQ
$
STU.D
%
.V/W--
%89
Eq. (8)
Where
V
refers to the choice set design,
M
denotes the number of parameters, and
)
is the best
estimate of parameters derived from the soft launch.
Moreover, two scenarios need to be designed based on the identified framework. In the BIP
experiment, a set of basic information is shown to drivers and then more information is added to
the existing knowledge in the AIP scenario. To implement this strategy, the model averaging
10
method that allows multiple experiments to be evaluated at the same time is used. In this technique,
the estimated AVC matrices are merged into one matrix that can be optimized for an efficiency
measure such as
O=PQQRQ
(Rose and Bliemer, 2009). Therefore, both BIP and AIP models were
designed simultaneously which led to a single design optimized for both models. Eventually, 24
choice sets in 4 blocks were constructed using the NGENE software package (ChoiceMetrics,
2018).
2.3. Questionnaire Structure
An online questionnaire instrument is used to transform the design matrix into meaningful choice
sets that are randomly shown to respondents. Figure 2 displays a screenshot of the experiment
interface which is carefully designed to simulate the ride request arrival process in both BIP (left)
and AIP (right) scenarios.
Furthermore, a set of screening questions is embedded at the beginning of the survey to ensure
respondents are eligible to take part in this survey. The criteria are being older than 18 years old,
Uber/Lyft drivers in the US or Uber/ViaVan drivers in the Netherlands, and also working at least
once a week. After meeting the requirements, respondents are asked about their working pattern
as input for getting assigned to the relevant design. The next part of the questionnaire is the choice
experiment followed by some questions about their working pattern, employment status,
experience, attitudes towards the covid-19 pandemic and their socio-economic characteristics.
Figure 2: Experiment interface in the BIP (left) and AIP (right) scenarios
11
3. Data Collection
As a highly specific target population, recruiting ride-sourcing drivers was a laborious task. A
panel provider was employed to reach out to Uber and Lyft drivers in the US as well as Uber and
ViaVan drivers working in the Netherlands. The data collection process took about three months
from November 2020 to February 2021. Eventually, a sample of 752 and 68 drivers was drawn in
the US and the Netherlands, respectively. After conducting a thorough data quality analysis, 576
responses in the US and 58 cases in the Netherlands were approved for further analysis. Despite
all the efforts, a larger Dutch sample within the designated time frame was not attained due to the
relatively smaller number of active ride-sourcing drivers in the Netherlands. Therefore, the focus
of this study is on the US sample and the Dutch data is mainly used for a brief tentative comparative
analysis.
4. Results
4.1. Descriptive Analysis
The working characteristics of the respondents are shown in Figure 3. Almost half of the drivers
in the US exclusively drive for Uber while only 13% drive solely for Lyft. Multihoming strategy
(i.e., working for several platforms simultaneously) is used by 41% of the respondents in the US.
Uber is more dominant in the Dutch context where 77%, 2%, and 21% drive for Uber, ViaVan and
both, respectively. In both countries, the majority of drivers have working experience of 13-36
months as ride-sourcing driver. Regarding the working days, Monday in the US and Saturday in
the Netherlands are the most popular days to work for our sample. About 70% of the respondents
start their shift in the morning and work for either 8 or 4 hours.
46%
13%
41%
The US
Uber Lyft Both
77%
2%
21%
The Netherlands
Uber ViaVan Both
0%
5%
10%
15%
20%
25%
30%
35%
40%
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
The most common working day
US Netherlands
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Morning-8h
Morning-4h
Midday-8h
Midday-4h
Afternoon-8h
Afternoon-4h
Evening-8h
Evening-4h
Night-8h
Night-4h
Working shift
US Netherlands
0%
10%
20%
30%
40%
Less than 12
months
13-24 25-36 37-48 49-60 More than
60 months
Experience
US Netherlands
Figure 3: Working characteristics of the drivers
12
Figure 4 summarises the sociodemographic characteristics of the respondents including gender,
employment status, and age. Male drivers compose more than 70% of the sample. Around 60% of
the sample consists of the drivers who have other work-related sources of income, from here on
labelled as part-time drivers. The data also demonstrates that the part-time drivers on average work
fewer hours per week than full-time ones do. The average age of the drivers is 36 and 31 years old
in the US and the Netherlands, respectively.
The experience, views and attitudes of drivers towards the Covid-19 pandemic are measured by a
set of statements presented in table 3. A 5-point Likert scale ranging from 1 (strongly disagree) to
5 (strongly agree) was used to capture the opinions of the respondents. The mode (the most chosen
response) for each indicator is calculated to measure the central tendency of the sample in each
country. Most of the drivers stated that they were concerned about the pandemic and getting
infected by passengers and that they also took preventive measures to protect themselves and their
clients. Furthermore, they believed that their job had been negatively affected by the pandemic. In
some cases, the majority of drivers in the US and the Netherlands had different points of view.
Most of the drivers working in the Netherlands neither agreed nor disagreed with changes in
working shift and not driving to the busy areas while the US counterparts indicated their agreement
with these statements. A contrasting viewpoint is observed between two groups of drivers about
the number of incoming requests since the pandemic. The majority of the US drivers stated that
they receive more requests compared to before the pandemic whereas the Dutch sampled drivers
disagreed with that. Moreover, most of the drivers in the Netherlands believed that the pandemic
has changed the way that they work as ride-sourcing driver while the drivers working in the US
had an opposite perception.
Figure 5: Sociodemographic characteristics of the respondents
0%
10%
20%
30%
40%
50%
60%
70%
Part-time Full-time
Employment status
US Netherlands
0%
20%
40%
60%
80%
Male Female
Gender
US Netherlands
0%
10%
20%
30%
40%
50%
60%
70%
18-30 31-40 41-50 51-60 Older than 60
Age
US Netherlands
Figure 4: Sociodemographic characteristics of the respondents
13
Table 3: The indicators measuring the attitudes of drivers towards the Covid-19 pandemic
No.
Statements
US
NL
Mode
Mode
1
I believe that the COVID-19 pandemic has negatively impacted my job as
a driver.
5
5
2
I accept more rides than before the pandemic.
4
4
3
To comply with social distancing measures, I don’t like to have more than
one passenger in my car.
4
3
4
I don’t care about the COVID-19.
1
1
5
I have completely changed my working shift due to the pandemic.
4
3
6
If I end up in a busy area, I don’t wait there because of the risk of getting
infected.
4
3
7
I’m afraid of getting infected by my passengers.
4
4
8
I don’t drive to surge or high demand areas because those areas are more
crowded and the risk of virus transmission is higher.
4
3
9
There is no change in what I had been doing as a driver before the
pandemic.
4
2
10
I take preventive measures such as wearing a face mask, disinfecting my
car, etc. to protect myself and my passengers.
5
5
11
I do care about the COVID-19.
5
4
12
I receive many more rides than before the pandemic.
4
2
4.2. Exploratory Factor Analysis
To investigate the effect of the covid-19 pandemic on ride acceptance behaviour, an Exploratory
Factor Analysis (EFA) was carried out to reduce the number of variables through merging the
highly correlated observed measures (Henson and Roberts, 2006; Spearman, 1904). In order to
ensure that the EFA is applicable, the Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests were
performed (Kaiser, 1974). To keep a balance between parsimony and comprehensiveness, the
Principal Component Analysis (PCA) model was applied (Norris and Lecavalier, 2010) and then
several tests and techniques including the eigenvalues greater than 1, scree plot, and parallel
analysis were deployed to ascertain the minimum number of components. Due to the superiority
of the oblique rotation which takes into account the component interconnections (Flora et al., 2012;
Gaskin and Happell, 2014; Price, 2017), the direct oblimin method was used to independently
rotate the factor axes and situate them near the observed variables. Consequently, two components
summarising the variation of the measures with the factor load greater than 0.5 were identified
using the SPSS software package (Table 4).
14
Table 4: Results of the exploratory factor analysis
Indicators
Components
1
2
I believe that the COVID-19 pandemic has negatively impacted my job as a
driver.
0.659
I accept more rides than before the pandemic.
0.748
There is no change in what I had been doing as a driver before the pandemic.
0.825
I take preventive measures such as wearing a face mask, disinfecting my car,
etc. to protect myself and my passengers.
0.720
I don’t care about the COVID-19. [recoded]
0.696
I receive many more rides than before the pandemic.
0.848
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
Given that accepting more ride requests can largely be offset by receiving many more ride offers,
the first factor is mainly attributed to being positive about the pandemic effects due to having the
impression of no changes (especially negative ones) in working as a driver during this period. On
the other hand, the second component is primarily related to being negative about the pandemic
given the stated concerns and having the perception of its negative effects on their job. Looking
into the factor scores of these two components for each individual, we observed that some drivers
can be associated with being either positive or negative about the pandemic and some of them have
mixed feelings. Due to the relatively small Dutch sample size, the EFA was solely conducted for
the US data.
4.3. Choice Model Estimation and Results
In total, six different models for both BIP and AIP scenarios are estimated for the US data. In each
scenario, three types of models are estimated, comprising of different sets of explanatory attributes:
Primary, Full, and Hybrid model. The primary model includes only the alternative-specific
variables that are provided in the choice experiment. Driver’s sociodemographic characteristics
and working pattern are added to the ride-related attributes in the full model. The hybrid model
incorporates also the experience/attitudinal factors towards the covid-19 pandemic as extracted
from the EFA. This categorisation gives insights into the effects of various sets of variables
depending on the application of interest. For instance, the primary model can be applied when no
information about the drivers’ characteristics and attitudes is available. Furthermore, the
distinction between the AIP and BIP experiments is associated with the additional information
shared with the drivers.
Table 5 summarises the results of the BIP model estimation including the parameter estimates,
their significance value, and the model fitness. ASC_Decline represents the alternative specific
constant incorporated in the utility function of the ride rejection alternative. The negative
significant parameter suggests an unobserved tendency towards ride acceptance. All the other
parameters are included in the utility of accepting the ride.
15
Table 5: The results of the BIP models
Parameters
BIP
Primary
P-value
Full
P-value
Hybrid
P-value
ASC_Decline
-1.810
0.000
-0.417
0.028
-0.374
0.049
B_Pickup time [min]
-0.050
0.000
-
-
-
-
B_Pickup time_Full [min]
-
-
-0.027
0.011
-0.027
0.011
B_Pickup time_Part [min]
-
-
-0.072
0.000
-0.072
0.000
B_Waiting time [min]
-0.017
0.007
-0.018
0.005
-0.018
0.005
B_Peak_hours [1=Peak hours]
-0.560
0.000
-0.368
0.001
-0.375
0.000
B_Weekend/Friday [1=Weekend/Friday]
-0.443
0.000
-0.334
0.000
-0.318
0.000
B_Time1_Loc [1=Beginning of the shift and City centre]
-0.303
0.003
-0.284
0.007
-0.297
0.005
B_Req_Long_Rate_Declined
0.091
0.001
0.102
0.000
0.098
0.001
B_Surge price [USD]
0.101
0.002
0.110
0.001
0.108
0.002
B_Part-time [1=Part-time drivers]
-
-
1.110
0.000
1.120
0.000
B_Beginners [1=Beginners]
-
-
0.353
0.001
0.318
0.004
B_Gender [1=Male]
-
-
0.421
0.000
0.423
0.000
B_Fully satisfied [1=Fully satisfied]
-
-
0.607
0.000
0.613
0.000
B_Education [1=Educated]
-
-
0.080
0.332
0.135
0.109
B_Covid_Positive
-
-
-
-
-0.047
0.255
B_Covid_Negative
-
-
-
-
0.141
0.000
Initial Log-Likelihood
-2395.517
-2395.517
-2395.517
Final Log-Likelihood
-2031.504
-1959.983
-1952.805
Rho-square
0.152
0.182
0.185
As expected, B_Pickup time which refers to the drive time from the driver’s current location to
the pickup point has a negative effect on ride acceptance. This is due to the fact that the pickup
time increases the ride disutility since drivers are not paid while driving without a passenger.
Moreover, given that no information about the trip fare and the ride destination is available in this
scenario, it is not guaranteed that the incurred cost is compensated by the ride. In the full model,
an interaction between the pickup time and the employment status of drivers is found significant.
Part-time drivers who have other sources of income are noticeably more sensitive (almost three
16
times) to the pickup time than full-time drivers who are entirely financially reliant on the job. This
observed reluctance to take a risk may presumably stem from the more constrained working shift
which makes them more conscious of time.
Another temporal component is waiting time which has a marginal negative effect on ride
acceptance. Drivers’ expectations may rise in relation to the time between the last drop-off and the
incoming request. This is because waiting for a request leads to being idle which decreases the
occupation rate and increases drivers’ costs that need to be compensated. Consequently, this result
suggests that drivers might prefer cherry-picking with increased elapsed waiting time.
The drivers working during the evening peak hours (16:00-00:00), weekends and Fridays, when
demand is relatively higher, are more prone to decline ride requests, everything else being equal.
When the frequency of incoming requests rises, drivers become more selective given that a
strategical wait may lead to receiving a more profitable ride. Similarly, there is a tendency towards
ride rejection at the beginning of the shift and in the city centre. These effects may be due to the
expectation of having more opportunities during the remainder of the shift.
B_Req_Long_Rate_Declined suggest that there exists an interaction between request type (Uber
X/Pool), long-distance trips (+30 min), rider rating, and the previously declined ride. The positive
sign implies that the chance of ride acceptance is higher when a private ride (e.g. Uber X) taking
more than 30 minutes is requested by a high-rated passenger while the previous request has been
declined. All these components indicate a favourable ride type, one that is perceived to be
profitable (long ride), less complicated (private ride), trustworthy (high-rated rider), and pressure
reliever (offered after a declined ride).
As can be expected, surge pricing - a spatial-temporal pricing strategy that aims at managing
supply-demand intensity - increases the probability of ride acceptance. When a request is subject
to surge pricing, drivers can earn more money which incentivises them to accept it. Surge pricing
which is the only monetary variable in the BIP experiment can be used to calculate the value of
pickup time by computing the ratio between B_Pickup time and B_surge. Based on the results of
the primary model, the value of pickup time is 0.50 USD/min. This implies that a minute increase
in the pickup time can be offset by an increase of 0.50 USD in the value of surge pricing. According
to the full model, this value for the full-time drivers and part-time drivers is 0.25 USD/min and
0.65 USD/min, respectively.
Among the socioeconomic factors, employment status, satisfaction degree, gender, and experience
level have the highest impact on the ride acceptance behaviour, in descending order. Part-time
drivers are more likely than full-time drivers to accept ride requests. This may be because they
consider this job as an extra income and also their available time is limited. The level of experience
also plays an important role in accepting/declining ride requests. Beginners – drivers with one year
or less experience – accept rides more often. As drivers learn about the system operational
strategies over time, they are better positioned to make more informed decisions. Male drivers as
well as highly satisfied drivers – drivers who rated the system operations with 4.5/5 stars - have a
preference for accepting rides when limited information is provided. In such a blind decision-
making scenario, they may have a higher tendency to trust the platform matching algorithm.
The hybrid model that includes the corona-related factors suggests that drivers who are concerned
about the pandemic and its negative effects on their work experience may have a higher acceptance
rate. This group of drivers, who are prepared and protect their health by adopting preventive
measures, feel the need to protect their business as well since they have the impression of the
17
negative impact of the pandemic on their job. That is why these drivers might be willing to seize
every single opportunity to earn money and compensate for those negative effects. This may lead
to having a higher acceptance rate which can conflict with the match quality and the driver’s
income. These consequences can be the underlying reasons for the negative impression of these
concerned drivers about the pandemic and its impacts. The other parameter, being positive about
the pandemic, is not statistically significant in the BIP model in which the information is more
restrictively shared.
Table 6 presents the results of the AIP scenario in which more information is provided to drivers.
The results show that some alternative-specific factors such as waiting time, and driver’s location,
as well as individual-specific attributes such as working during peak hour, time of day, and gender
are no longer significant. In contrast, several new alternative-specific factors including trip fare,
guaranteed tip and congestion level, as well as the individual-attribute education play an important
role in explaining drivers’ choices. Such changes possibly stem from the importance of monetary
information related to all other attributes. As expected, trip fare and tip have a positive impact on
ride acceptance whereas the level of congestion indicating the delay between the pickup point and
the destination motivates drivers to decline ride requests.
As observed in the BIP models, pickup time increases the disutility of accepting a ride. It should
be noted that the pickup time is more negatively valued compared to the delay associated with
traffic congestion. This is arguably because drivers are paid based on trip distance and travel time,
so traffic congestion is possibly taken into account although not a desired experience. Driver’s
employment status still has significant interaction with pickup time. Part-time drivers are more
sensitive to pickup time due to more constrained working hours. Additionally, the probability of
accepting a ride by a part-time driver is substantially higher than for a full-time driver. Like in the
BIP scenario, the interaction between request type, long ride, rider rating, and the previous
declined ride is still present and leads to higher ride acceptance.
Drivers’ ride acceptance behaviour can be greatly affected if ride-sourcing platforms ask riders in
advance about their minimum willingness to tip and then share this information with drivers when
the request appears. Once the request is accepted by the driver, the specified amount of tip is
automatically secured if the driver successfully picks up the rider. The results of the primary model
suggest that drivers are roughly two times more sensitive to tip and surge price than to trip fare. In
other words, one monetary unit of tip and surge is worth at least two monetary units of trip fare.
This effect stems from tip and surge being considered as an add-on to drivers’ income. Moreover,
no platform service fee is deducted from the tip while trip fare and surge pricing are subject to the
commission fee (which can be about 25%). It also turns out that there is a statistically significant
effect for the interaction between the guaranteed tip and the employment status of drivers. Full-
time drivers are more responsive to tip than their part-time counterparts.
In this experiment, the sensitivity to the pickup time and traffic congestion can be benchmarked
against the three monetary variables. The values of pickup time based on the trip fare, surge
pricing, and the guaranteed tip are 1.36 USD/min, 0.71 USD/min, and 0.59 USD/min, respectively.
The trade-offs for the delay time due to traffic congestion are 0.28 USD/min, 0.15 USD/min, 0.12
USD/min respectively. This suggests that monetary promotions are relatively cheaper pricing
strategies than the trip fare to compensate for the pickup time as well as the delay caused by a
traffic jam.
18
Table 6: The results of the AIP models
Although the education level was not found to be an influential factor in the restricted information-
sharing policy, the results of the AIP models indicate that drivers that attained higher levels of
Parameters
AIP
Primary
P-value
Full
P-value
Hybrid
P-value
ASC_Decline
-1.560
0.000
-0.388
0.116
-0.321
0.191
B_Pickup time [min]
-0.053
0.000
-
-
-
-
B_Pickup time_Full time [min]
-
-
-0.021
0.092
-0.021
0.108
B_Pickup time_Part time [min]
-
-
-0.076
0.000
-0.076
0.000
B_Waiting time [min]
-0.005
0.522
-0.005
0.518
-0.004
0.583
B_Peak_hours [1=Peak hours]
-0.057
0.629
0.027
0.825
0.022
0.860
B_Weekend/Friday [1=Weekend/Friday]
-0.507
0.000
-0.412
0.000
-0.412
0.000
B_Time1_Loc [1=Beginning of the shift and City centre]
-0.135
0.252
-0.137
0.253
-0.155
0.195
B_Req_Long_Rate_Dec
0.086
0.011
0.087
0.011
0.081
0.017
B_Surge price [USD]
0.075
0.048
0.076
0.049
0.069
0.074
B_Trip Fare [USD]
0.039
0.000
0.041
0.000
0.040
0.000
B_Guaranteed tip [USD]
0.090
0.014
-
-
-
-
B_Guaranteed tip_Full time [USD]
-
-
0.208
0.000
0.211
0.000
B_Guaranteed tip_Part time [USD]
-
-
0.021
0.647
0.015
0.743
B_Traffic congestion [min]
-0.011
0.002
-0.011
0.002
-0.012
0.001
B_Part-time [1=Part-time drivers]
-
-
0.981
0.000
1.03
0.000
B_Beginners [1=Beginners]
-
-
0.271
0.023
0.223
0.062
B_Gender [1=Male]
-
-
0.113
0.259
0.124
0.215
B_Fully satisfied [1=Fully satisfied]
-
-
0.190
0.029
0.217
0.012
B_Education [1=Educated]
-
-
0.461
0.000
0.561
0.000
B_Covid_Positive
-
-
-
-
-0.121
0.010
B_Covid_Negative
-
-
-
-
0.178
0.000
Initial Log-Likelihood
-2395.517
-2395.517
-2395.517
Final Log-Likelihood
-1752.026
-1722.981
-1710.417
Rho-square
0.269
0.281
0.286
19
education (i.e. have a college or a higher degree) are more likely to accept rides. Like in the BIP
experiment, beginning and fully satisfied drivers tend to accept more rides. Beginning drivers may
lack sufficient knowledge of the system operations to evaluate the ride quality and fully satisfied
drivers have a higher trust in the system performance.
Regarding the coronavirus pandemic effects, the two identified factors are incorporated into the
AIP model. Unlike the results of the BIP hybrid model, being positive about the pandemic is
statistically significant and has a negative effect on ride acceptance. This component is obtained
from three attitudinal statements about accepting more rides that can be offset with receiving many
more ride requests than before the pandemic, and having the perception of no changes in work
before and during the pandemic. These drivers have the impression of receiving notably more
requests. Although the evidence shows that the total number of requests has declined (Du and
Rakha, 2021), some drivers have stopped working given the more dramatic plunge in demand at
the beginning of the pandemic as well as the high risk of getting infected. This may have decreased
the competition between some groups of drivers and increased their chance to receive ride requests
Therefore, receiving more requests or at least having such an impression makes drivers more
selective and causes more rejection. Conversely, being negative about the pandemic can increase
the chance of acceptance as observed in the BIP scenario. In the AIP scenario, these two
components have opposite values that can offset each other. Therefore, the pandemic may not
significantly influence the ride acceptance behaviour of drivers at the aggregate level of this
scenario.
Due to the relatively small dataset collected in the Netherlands, we could not estimate a statistically
sound separate model for the Dutch sample. Alternatively, the data from both countries were
merged after unifying the attribute units, allowing the analysis of the combined sample and
identifying the possible differences in drivers’ behaviour by specifying dummy variables. Among
the estimated models, the following differences between the two groups of drivers in the AIP-
Primary model were found. Sensitivity to traffic congestion was much higher among the drivers
working in the Netherlands, possibly because the level of congestion is lower in the Netherlands,
according to the traffic index (Traffic Index by Country, 2021). Furthermore, the trip fare was
regarded nearly two times more important in the Netherlands than in the US. There may exist
multiple underlying reasons including the currency, tipping culture (which is less customary in the
Netherlands than in the US), income level, and other economic indices. However, these
observations need to be further investigated with a larger sample size in the Netherlands in order
to draw more conclusive results.
5. Discussion and Conclusions
This research unravels the ride acceptance behaviour of ride-sourcing drivers through a stated
preference experiment performed in the United States and the Netherlands. To the best of our
knowledge, this is the first study attempting to comprehensively estimate the determinants of ride-
sourcing drivers’ ride acceptance behaviour. To this end, a set of potential attributes are identified
based on the current system operations, driver-side app, existing literature, interview with drivers,
and posts on drivers’ forums. Then, two information-sharing policies are defined: Baseline
Information Provision (BIP) and Additional Information Provision (AIP). The former scenario
solely includes the variables currently shown to drivers in the most commonly used system setting
while additional information is provided in the subsequent phase of the experiment. In total, 576
20
and 56 qualified responses were collected in the US and the Netherlands, respectively.
Subsequently, a choice modelling approach is applied to analyse the data. The focus of this study
is on the US data due to the relatively small sample size in the Netherlands.
The monetary variables included in this study are surge pricing, trip fare, and guaranteed tip (i.e.,
the minimum amount of tip that is indicated upfront by the prospective rider). Surge pricing
included in the BIP experiment is the only monetary attribute that is shared with drivers in the
current system setting of the ride-sourcing platforms operating in the target area whereas trip fare
and guaranteed tip are incorporated in the AIP scenario. Results reveal that guaranteed tip is the
most highly valued monetary factor, especially for full-time drivers who are more financially
dependent on the ride-sourcing platforms, followed closely by surge pricing. From the drivers’
perspective, tip and surge pricing as added income are considered about two times more important
than trip fare.
In general, tipping is a pro-social consumer behaviour that is considered as an economically
irrational action of customers and typically targets the low-income service providers (Azar, 2003;
Elliott et al., 2018). Such a social norm has a profound economic impact on the US service industry
(Shierholdz et al., 2017). In the US taxi industry in 2012, tipping comprised around 18% of the
annual taxi revenue which is equal to $445 million (Bloomberg and Yassky, 2014). Currently,
Uber riders can tip after they are dropped off. Analysing 40 million observations of Uber tipping
behaviour in 2017, Chandar et al. (2019) concluded that more than 15% of the trips are tipped
although tips are given privately (no consequences for rider rating) and the chance of having a
match with the same driver is fairly low. They also found out that the average amount of tip is
approximately $0.5 per trip and for those rides that have been tipped, more than $3 is tipped which
is about 26% of the trip fare. In this study, we have introduced a new form of tipping that is
determined in advance. When the ride is matched, the specified amount of tip must be paid and
naturally, the passenger can tip more to reward the service if satisfying.
This feature can be used when a rider highly disvalues waiting time (e.g., being in a hurry) and
intends to persuade nearby drivers to quickly accept the ride. It is effectively a self-determined
discriminatory pricing scheme that allows riders to signal their willingness to pay and thereby
potentially influencing the level of service received. This is in line with the study conducted by
Flath (2012) which suggests that passengers with a strong aversion to waiting would tip taxi drivers
to reduce the time needed to find a taxi. As opposed to trip fare and surge pricing, tipping is not
directly imposed on riders by the platform which makes it less unfavourable from the rider’s
perspective. The results of this study suggest that such a feature can significantly impact drivers’
ride acceptance behaviour. This can also be part of the platform pricing strategy through
developing an algorithm that optimally calculates the trip fare and surge pricing based on the
guaranteed tip determined by riders. This may lead to a higher acceptance rate and level of service
which is beneficial for riders, drivers, and the platform.
Surge pricing is a spatial-temporal pricing strategy that is introduced to address an imbalanced
supply-demand relation. However, surge pricing is one of the most controversial topics in the ride-
sourcing literature given its enormous implications for all stakeholders involved. On one hand, it
is argued that surge pricing is a near-optimal solution that decreases the match failure as well as
system inefficiency through suppressing the excessive demand and also increases the platform
profit (Cachon et al., 2017; Nourinejad and Ramezani, 2019). Using machine learning techniques,
Battifarano et al. (2019) propose that surge pricing can generate more profit if the value is predicted
and disseminated to both riders and drivers in advance. On the other hand, surge pricing may lead
21
to strategic waiting for both riders who seek normal price and drivers looking for higher prices
which results in inefficient performance due to forward-looking behaviour (Ashkrof et al., 2020;
Chen and Hu, 2020; Zhong et al., 2020). The results of this study indicate that surge pricing is an
important determinant of ride acceptance behaviour by ride-sourcing drivers. This is in line with
the findings of Chen et al. (2015). They found that drivers work longer and flexibly adjust their
working shift when surge pricing is present even if they have already hit their daily target. Based
on the findings of this research, surge pricing is the second most important monetary attribute that
can strongly incentivise drivers to accept rides. The value of pickup time for surge pricing is
estimated to be 0.5-0.71 USD/min. This has important consequences for determining the expected
response of drivers to the introduction of surge pricing as a function of their travel time from the
surge location and the surge price level. Unlike the guaranteed tip, no difference in perspectives
of part-time and full-time drivers concerning surge pricing is found.
Nevertheless, employment status is a crucial attribute influencing the choice of drivers. Part-time
drivers, who have other sources of income, show a strong preference for accepting ride offers
compared to their full-time counterparts. This might be because part-time drivers supplement their
revenue from other jobs and also have limited available time restricting their degrees of freedom.
Hence, the opportunity costs of part-time drivers are potentially higher which leads to a higher
acceptance rate (Baron, 2018).
Furthermore, the experience level of drivers with the ride-sourcing platforms and their operational
strategies has been identified as a determinant that influences their choices in various aspects (Chu
et al., 2018; Miranda et al., 2008; Noulas et al., 2019; Rosenblat and Stark, 2015; Wang and Yang,
2019). Based on the findings of this study, beginning drivers who have one year or less of
experience with ride-hailing tend to accept more rides. Lack of sufficient experience and
knowledge to evaluate the characteristics of ride requests and having higher trust in the system
performance might be the underlying reasons for this tendency (Ashkrof et al., 2020). In both BIP
and AIP experiments, pickup time, especially for part-time drivers, has a negative impact on ride
acceptance due to the disutility of driving without a passenger, i.e. unpaid time. Therefore, in order
to have a higher acceptance rate, a new matching algorithm can be developed that can calculate
the response likelihood of nearby drivers and then offer the request to the driver with the highest
probability of acceptance. For instance, less attractive requests can be matched with part-time
beginning drivers. The introduction of such measures should consider their potential acceptance
amongst drivers.
While the small sample collected in the Netherlands, does not allow for estimating a full-fledged
model, it has been observed that drivers working in the Netherlands are more sensitive to the trip
fare as well as traffic congestion. These findings should be further investigated with a larger sample
size from the Netherlands and possibly from other European countries. Another limitation of this
research refers to the inherently typical bias of stated preference surveys in which respondents may
not accurately grasp the choice experiments, especially the AIP scenario that includes several
hypothetical new components. It can be insightful to validate the findings of this study through
analysing a set of revealed preferences data concerning drivers’ behaviour in ride-sourcing
environments or field observation of drivers if possible. Moreover, the insights gained in this study
can be integrated into ride-hailing analysis models (Kucharski and Cats, 2020) and used to assess
the possible effects of driver’s ride acceptance behaviour based on various information-sharing
policies on the ride-sourcing system performance, including efficiency, level-of-service and
profitability. Future research may investigate other aspects of ride-sourcing drivers’ decisions such
22
as registration to the platform at the strategic level, selecting working shift at the tactical level, and
relocation strategies at the operational level.
Acknowledgements
This research was supported by the CriticalMaaS project (grant number 804469), which is financed
by the European Research Council and the Amsterdam Institute for Advanced Metropolitan
Solutions.
References
Ashkrof, P., Correia, G.H. de A., Cats, O., van Arem, B., 2020. Understanding ride-sourcing
drivers’ behaviour and preferences: Insights from focus groups analysis. Res. Transp. Bus.
Manag. 37, 100516. https://doi.org/10.1016/j.rtbm.2020.100516
Azar, O.H., 2003. The implications of tipping for economics and management. Int. J. Soc. Econ.
30, 1084–1094. https://doi.org/10.1108/03068290310492878
Baron, D.P., 2018. Disruptive Entrepreneurship and Dual Purpose Strategies: The Case of Uber.
Strateg. Sci. 3, 439–462. https://doi.org/10.1287/stsc.2018.0059
Battifarano, M., Qian, Z. (Sean), 2019. Predicting real-time surge pricing of ride-sourcing
companies. Transp. Res. Part C Emerg. Technol. 107, 444–462.
https://doi.org/10.1016/j.trc.2019.08.019
Ben-Akiva, M., Walker, J., Bernardino, A.T., Gopinath, D.A., Morikawa, T., Polydoropoulou,
A., 2002. Integration of Choice and Latent Variable Models, In Perpetual Motion.
https://doi.org/10.1016/b978-008044044-6/50022-x
Beojone, C.V., Geroliminis, N., 2021. On the inefficiency of ride-sourcing services towards
urban congestion. Transp. Res. Part C Emerg. Technol. 124, 102890.
https://doi.org/10.1016/j.trc.2020.102890
Bierlaire, M., 2020. A Short Introduction to PandasBiogeme. Ec. Polytech. Fed. Lausanne 13–
43.
Bierlaire, M., Lurkin, V., 2020. Introduction to choice models [WWW Document]. URL
https://www.edx.org/course/introduction-to-discrete-choice-models
Bliemer, M.C.J., Rose, J.M., 2010. Construction of experimental designs for mixed logit models
allowing for correlation across choice observations. Transp. Res. Part B Methodol.
https://doi.org/10.1016/j.trb.2009.12.004
Bloomberg, M.R., Yassky, D., 2014. 2014 Taxicab Factbook. Taxicab Factb. 1–13.
Cachon, G.P., Daniels, K.M., Lobel, R., 2017. The role of surge pricing on a service platform
with self-scheduling capacity. Manuf. Serv. Oper. Manag. 19, 368–384.
https://doi.org/10.1287/msom.2017.0618
Chandar, B., Gneezy, U., List, J.A., Muir, I., 2019. The Drivers of Social Preferences : Evidence
from a Nationwide Tipping Field Experiment.
23
Chen, M.K., Sheldon, M., 2015. Dynamic Pricing in a Labor Market : Surge Pricing and Flexible
Work on the Uber. Ssrn 1–19.
Chen, Xiqun, Chen, Xiaowei, Zheng, H., Xiao, F., 2021. Efficient dispatching for on-demand
ride services : Systematic optimization via Monte-Carlo tree search. Transp. Res. Part C
127, 103156. https://doi.org/10.1016/j.trc.2021.103156
Chen, Y., Hu, M., 2020. Pricing and matching with forward-looking buyers and sellers. Manuf.
Serv. Oper. Manag. 22, 717–734. https://doi.org/10.1287/msom.2018.0769
ChoiceMetrics, 2018. Ngene 1.2 USER MANUAL & REFERENCE GUIDE The Cutting
Edge in Experimental Design End-User License Agreement 241.
Chu, L.Y., Wan, Z., Zhan, D., 2018. Harnessing the Double-Edged Sword via Routing:
Information Provision on Ride-Hailing Platforms. SSRN Electron. J.
https://doi.org/10.2139/ssrn.3266250
Du, J., Rakha, H., 2021. Impact of COVID-19 on Ridehailing and Other Modes of
Transportation [WWW Document]. URL
https://www.morgan.edu/school_of_engineering/research_centers/urban_mobility_and_equi
ty_center/research/new_research/covid_and_ridehailing.html (accessed 5.13.21).
Elliott, D., Tomasini, M., Oliveira, M., Menezes, R., 2018. Tippers and stiffers: An analysis of
tipping behavior in taxi trips. 2017 IEEE SmartWorld Ubiquitous Intell. Comput. Adv.
Trust. Comput. Scalable Comput. Commun. Cloud Big Data Comput. Internet People Smart
City Innov. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - 1–8.
https://doi.org/10.1109/UIC-ATC.2017.8397523
Flath, D., 2012. Why Do We Tip Taxicab Drivers? Japanese Econ. 39, 69–76.
https://doi.org/10.2753/jes1097-203x390304
Flora, D.B., LaBrish, C., Chalmers, R.P., 2012. Old and new ideas for data screening and
assumption testing for exploratory and confirmatory factor analysis. Front. Psychol. 3, 1–
21. https://doi.org/10.3389/fpsyg.2012.00055
Gaskin, C.J., Happell, B., 2014. On exploratory factor analysis: A review of recent evidence, an
assessment of current practice, and recommendations for future use. Int. J. Nurs. Stud. 51,
511–521. https://doi.org/10.1016/j.ijnurstu.2013.10.005
Hall, J. V., Krueger, A.B., 2018. An Analysis of the Labor Market for Uber’s Driver-Partners in
the United States. ILR Rev. 71, 705–732. https://doi.org/10.1177/0019793917717222
Hamilton, I., Hernbroth, M., 2019. Uber drivers across the world are striking about pay,
conditions, and the firm’s “orgy of greed” [WWW Document]. businessinsider. URL
https://www.businessinsider.nl/photos-uber-drivers-go-on-strike-around-the-world-2019-
5?international=true&r=US (accessed 5.6.21).
Hensher, D.A., Rose, J.M., Rose, J.M., Greene, W.H., 2005. Applied Choice Analysis,
Cambridge University Press.
Henson, R.K., Roberts, J.K., 2006. Use of Exploratory Factor Analysis in Published Research.
Educ. Pschological Meas. 66, 11–14. https://doi.org/10.1177/0013164405282485
Kaiser, H.F., 1974. An index of factorial simplicity. Psychometrika 39, 31–36.
24
https://doi.org/10.1007/BF02291575
Ke, J., Zheng, Z., Yang, H., Ye, J., 2021. Data-driven analysis on matching probability, routing
distance and detour distance in ride-pooling services. Transp. Res. Part C Emerg. Technol.
124, 102922. https://doi.org/10.1016/j.trc.2020.102922
Kucharski, R., Cats, O., 2020. Exact matching of attractive shared rides (ExMAS) for system-
wide strategic evaluations. Transp. Res. Part B Methodol. 139, 285–310.
https://doi.org/10.1016/j.trb.2020.06.006
Leng, B., Du, H., Wang, J., Li, L., Xiong, Z., 2016. Analysis of taxi drivers’ behaviors within a
battle between two taxi apps. IEEE Trans. Intell. Transp. Syst. 17, 296–300.
https://doi.org/10.1109/TITS.2015.2461000
McFadden, D., 1974. Conditional Logit Analysis of Qualitative Choice Behaviour, in: Frontiers
of Econometrics.
Miranda, F., Muñoz, J.C., De dios Ortúzar, J., 2008. Identifying transit driver preferences for
work shift structures: An econometric analysis. Transp. Sci. 42, 70–86.
https://doi.org/10.1287/trsc.1070.0199
Morshed, S.A., Khan, S.S., Tanvir, R.B., Nur, S., 2021. Impact of COVID-19 pandemic on ride-
hailing services based on large-scale Twitter data analysis. J. Urban Manag.
https://doi.org/10.1016/j.jum.2021.03.002
Norris, M., Lecavalier, L., 2010. Evaluating the use of exploratory factor analysis in
developmental disability psychological research. J. Autism Dev. Disord. 40, 8–20.
https://doi.org/10.1007/s10803-009-0816-2
Noulas, A., Salnikov, V., Hristova, D., Mascolo, C., Lambiotte, R., 2019. Developing and
deploying a taxi price comparison mobile app in the wild: Insights and challenges. Proc. -
2018 IEEE 5th Int. Conf. Data Sci. Adv. Anal. DSAA 2018 424–430.
https://doi.org/10.1109/DSAA.2018.00055
Nourinejad, M., Ramezani, M., 2019. Ride-Sourcing Modeling and Pricing in Non-Equilibrium
Two-Sided Markets. Transp. Res. Part B 00, 24–26.
https://doi.org/10.1016/j.trb.2019.05.019
Price, L.R., 2017. Psychometric Methods: Theory into Practice [WWW Document]. Meas.
Interdiscip. Res. Perspect. URL https://lccn.loc.gov/2016013346 (accessed 4.14.21).
Romanyuk, G., 2016. Ignorance Is Strength : Improving Performance of Matching Markets by
Limiting Information. Job Mark. Pap. 1–43.
Rose, J.M., Bliemer, M.C.J., 2009. Incorporating model uncertainty into the generation of
efficient stated choice experiments: A model averaging approach, in: International Choice
Modelling Conference, March 30-April 1, Yorkshire U.K.
Rose, J.M., Bliemer, M.C.J., Hensher, D.A., Collins, A.T., 2008. Designing efficient stated
choice experiments in the presence of reference alternatives. Transp. Res. Part B Methodol.
42, 395–406. https://doi.org/10.1016/j.trb.2007.09.002
Rosenblat, A., Stark, L., 2015. Algorithmic Labor and Information Asymmetries: A Case Study
of Uber’s Drivers. Ssrn 10, 3758–3784. https://doi.org/10.2139/ssrn.2686227
25
SAE International, 2018. Surface Vehicle Recommended Practice.
Shierholdz, H., Cooper, D., Wolfe, J., Zipperer, B., 2017. Employers would pocket $5.8 billion
of workers’ tips under Trump administration’s proposed ‘tip stealing’ rule [WWW
Document]. A Rep. by Econ. Policy Institute, Washingt. DC. URL
https://www.epi.org/publication/employers-would-pocket-workers-tips-under-trump-
administrations-proposed-tip-stealing-rule/ (accessed 4.29.21).
Smart, R., Rowe, B., Hawken, A., 2015. Faster and Cheaper: How Ride-Sourcing Fills a Gap in
Low-Income Los Angeles Neighborhoods.
Spearman, C., 1904. “ General Intelligence ,” Objectively Determined and Measured Author ( s
): C . Spearman Source : The American Journal of Psychology , Vol . 15 , No . 2 ( Apr .,
1904 ), pp . 201-292 Published by : University of Illinois Press Stable URL :
http://www.jsto. Am. J. Psychol. 15, 201–292.
Traffic Index by Country, 2021. Traffic Index by Country 2021 [WWW Document]. URL
https://www.numbeo.com/traffic/rankings_by_country.jsp (accessed 5.13.21).
Wang, H., Yang, H., 2019. Ridesourcing systems: A framework and review. Transp. Res. Part B
Methodol. 129, 122–155. https://doi.org/10.1016/j.trb.2019.07.009
Wang, S., Smart, M., 2020. The disruptive effect of ridesourcing services on for-hire vehicle
drivers’ income and employment. Transp. Policy 89, 13–23.
https://doi.org/10.1016/j.tranpol.2020.01.016
Wang, Z., Fu, K., Ye, J., 2018. Learning to estimate the travel time. Proc. ACM SIGKDD Int.
Conf. Knowl. Discov. Data Min. 858–866. https://doi.org/10.1145/3219819.3219900
Wentrup, R., Nakamura, H.R., Ström, P., 2019. Uberization in Paris – the issue of trust between
a digital platform and digital workers. Crit. Perspect. Int. Bus. 15, 20–41.
https://doi.org/10.1108/cpoib-03-2018-0033
Xu, K., Sun, L., Liu, J., Wang, H., 2018. An empirical investigation of taxi driver response
behavior to ride-hailing requests: A spatio-temporal perspective. PLoS One 13, 1–17.
https://doi.org/10.1371/journal.pone.0198605
Xue, Z., Zeng, S., Ma, C., 2021. Economic modeling and analysis of the ride-sourcing market
considering labor supply. Res. Transp. Bus. Manag. 38, 100530.
https://doi.org/10.1016/j.rtbm.2020.100530
Zha, L., Yin, Y., Xu, Z., 2018. Geometric matching and spatial pricing in ride-sourcing markets.
Transp. Res. Part C Emerg. Technol. 92, 58–75. https://doi.org/10.1016/j.trc.2018.04.015
Zhong, Y., Wan, Z., Shen, Z.-J.M., 2020. Queueing Versus Surge Pricing Mechanism:
Efficiency, Equity, and Consumer Welfare. SSRN Electron. J.
https://doi.org/10.2139/ssrn.3699134
Zuniga-Garcia, N., Tec, M., Scott, J.G., Ruiz-Juri, N., Machemehl, R.B., 2020. Evaluation of
ride-sourcing search frictions and driver productivity: A spatial denoising approach. Transp.
Res. Part C Emerg. Technol. 110, 346–367. https://doi.org/10.1016/j.trc.2019.11.021
