Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

Abstract

The growth of ride-hailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters. Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for RH. The model results indicate RH services' popularity among those aged 18-39, larger households and households with fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.

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Transportation Research Record
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DOI: 10.1177/0361198121989726
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Exploring Preferences for Transportation
Modes in the City of Munich after the
Recent Incorporation of Ride-Hailing
Companies
Maged Shoman
1
and Ana Tsui Moreno
1
Abstract
The growth of ride-hailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite
widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses
the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from
RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters.
Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH
service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and
cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for
RH. The model results indicate RH services’ popularity among those aged 18–39, larger households and households with
fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services
on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model
using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when
choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels
and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.
The past few years have witnessed a significant growth
of gig-companies, known as transportation network
companies (TNCs), operating on-demand and app-
based, prearranged services, which are also referred to as
ride-hailing (RH), ride-sourcing and ride-matching (1).
The original term used for such services was the new
online enabled transportation service (NOETS). RH ser-
vices are offered through an online platform that con-
nects customers demanding a ride to drivers offering a
ride supply using their own vehicles. The GPS capability
of the online platform allows the driver to determine the
passenger’s pickup location and keeps the passenger
updated about the driver’s location and arrival time.
In Germany, Uber began its operations during early
2013 in Berlin, the capital city. Berlin’s taxi companies
put up a ferocious resistance, including legal action.
Meanwhile, Uber kept proceeding with its plans to
expand into operations in Hamburg, Cologne, Stuttgart
and Dusseldorf (2). However, after several claims and
legal suits filed against Uber citing them as an unfair
market player, and after the service being banned in sev-
eral cities, Uber representatives gave up on the German
market. Berlin and Munich are its only operational
grounds now, with limited services, enabling it to operate
while complying with the German laws. Besides Uber,
other ride-sharing services such as Cabify and BlaBlacar
operate in Munich and most German cities today.
Previous studies on RH services reflect their impact on
urban mobility through measures such as modal shares.
A literature review of the available work reflected the
struggle to acquire data from RH companies about their
vehicles, drivers and passengers. Several authors tried dif-
ferent approaches to acquire adequate and reasonable
RH data using surveys and interviews. An innovative
approach to dissect the market was adopted by (3), in
which one of the authors personally drove for Uber and
Lyft. Studies in major American cities found that RH
companies are responsible for a 6% reduction in transit
1
Department of Civil, Geo and Environmental Engineering, Technical
University of Munich, Munich, Germany
Corresponding Author:
Maged Shoman, magedshoman@gmail.com

use (4). In the same work the authors found that if Uber
or Lyft services did not exist, around 55% of ride-hailing
trips would not take place or would be made by walking,
biking or transit. Another survey found 21% using RH
for commuting but a larger proportion using transit for
that purpose, with RH popularity higher in late evenings
and night and lower in the morning and evening rush (5).
(1) found that if RH did not exist in San Francisco, 33%
of its users would switch to public transit.
Despite the consensus that RH services are more effi-
cient than their fellow modes, literature on the topic is
quite limited. The available research stresses the impor-
tance of data to study the impacts of such rapidly grow-
ing services. To fill the gap in academic literature and aid
in studying the impacts of RH services, we designed a
survey to understand how travelers in the city of Munich
value their time when using RH. Studying the influence
of RH using the value of time (VOT) addresses willing-
ness to pay by different income groups, which was a lim-
itation of several published pieces of research.
The objective of this paper is to estimate the impact of
RH services on other transportation modes based on
how users respond to changes in auto, metro and RH
travel time and cost in an online survey.
The paper is structured as follows. A literature review
presents the research related to acceptance of and willing-
ness to use RH in different countries and the impacts of
RH services on other transportation modes. This is fol-
lowed by a section on methodology, covering the design
of the survey and how its results are incorporated into
the mode choice of an existing travel demand model.
Analysis and results are added after the methodology.
Finally, conclusions are presented in the last section.
Literature Review
Once RH companies were accepted into the market and
demand for such services started increasing, studies
revealed that the RH users mostly use it for social trips
and other similar purposes, but rarely use it for work
purposes, compared with transit (5). In New York, RH
services are commonly used for activities such as social
activities, shopping, entertainment, and so forth (6). In
other studies, RH has satisfied commuter demands for
rapid response during unpredictable weather conditions,
using surge pricing to increase its supply (7). (4) found
that RH services provide an unprecedented level of con-
venience and the survey responses showed that 37%
would choose RH because of the struggle to find park-
ing, and 33% would do so to avoid drunk driving.
As RH companies gained popularity, their services
grew very fast in a short time. A 2019 paper showed that
Uber had generated 31 million trips and 52 million pas-
sengers since 2013 (8). It was not long until RH
companies affected taxi ridership in ways that harmed
the taxi industry, with taxi ridership decreasing in most
of the studies, specifically by 65% in San Francisco from
2012 to 2014 (8), an 8% decrease in taxi rides per hour in
New York (7), an 18% drop in passenger trip numbers
per day in Beijing from 2012 to 2015 (9), an 18% drop in
Toronto (10) and a 15% decrease in Dubai after RH
companies’ entrance into the market (8). When compar-
ing RH vehicles to traditional taxis, research by (11)in
five cities in the US found that RH vehicles have a higher
efficiency rate in relation to trips made compared with
taxis. (12) found that the higher capacity of RH vehicles
leads to a significant decrease in congestion and exhaust
emissions. The effect of RH’s entrance into the market
on other modes of transport is noteworthy. The analysis
of the changes in mode shares after the introduction of
RH services in Toronto from 2011 to 2016 shows that
RH changed from 0% to 24.1%, taxi from 22.8% to
5.2%, transit from 16.3% to 20.3%, auto from 44.6% to
21.4% and active modes from 16.3% to 29.1% (10). In
the same study, RH (Uber) was found to cost less than
using a taxi (taxi fare: $7.20 for a 6km trip) but more
expensive than transit (transit fare: $3.25).
Seeking to determine RH users’ market segment, (10)
found that in Toronto, people mostly using TNCs are
between 20 and 39 years old with only 2% of users aged
60 or over. The majority of the trips took place between
late night and 5 a.m. The same study found that house-
holds with higher earnings use RH (54%) more than do
low income groups (2.6%). (13) also found similar results
for the market segment demand, which is explained by
the common use of technology among younger age
groups. A recent study using revealed preference (RP)
surveys to model the demand of users using rewards
incentives found that energy savings are valued more
highly than cost savings and that the acceptance of
reward system use is more common among lower income
segments (14). Money and convenience were considered
more important by RH users in China than risks such as
privacy and security of using the service (15).
To investigate the main determinants of riders’
choices, (16) used a stated preference (SP) survey to con-
duct a feasibility study of an interurban RH service on a
commuter sample in Salerno metropolitan area. (17) also
used an SP survey on commuters in Chicago to deter-
mine the influence of trip parameters such as time and
cost on demand. The use of SP surveys is common in
studies with different transportation modes and was uti-
lized by (18) to study the influence of level-of-service on
the use of autonomous and RH vehicles.
Key findings from a report published by the
Transportation Research Board in 2018 focused on
understanding the interaction between different trans-
portation modes, mainly RH services in five regions
2Transportation Research Record 00(0)

(Chicago, Los Angeles, Nashville, Seattle, and
Washington D.C.) (19):
Highest use of RH occurs during late hours of the
day and weekends.
Top concerns for users who would shift from tran-
sit to RH were transit travel and waiting times.
RH is used by all income groups.
The use of RH is affected by the decrease in auto
ownership with frequent users reporting no autos
per household.
Uber had a complementary effect in transit cities with
low transit ridership and a substitution effect in cities
with high transit. This is because of Uber’s ability to pro-
vide additional flexibility when transit supply was insuffi-
cient (20). The effects of RH services have been studied
from different viewpoints with simulation results present-
ing potentials, but also limitations, of such services (21,
22), with the main issue being efficiently supplying the
service on demand at the right time. Many RH studies
also had limitations with respect to estimating the will-
ingness of commuters to share a ride.
Methodology
SP Online Survey
The SP survey was designed to be carried out through an
online survey. The purpose of the survey was to deter-
mine travelers’ preferred mode for a given trip, varying
mode-specific attributes and the trip purpose. Auto, tran-
sit and RH were the alternatives in the choice set. A 20-
question survey was created in Limesurvey (limesur-
vey.org). The survey was designed in four parts.
(1) Sociodemographic profile. Individuals provided
their gender, age, occupation, residence area
type, residence period in Munich, distance to
nearest transit stop, auto license ownership, and
the number of people, workers, children and
autos in a household.
(2) Used mode of transport on an average day.
Individuals were asked about the most common
transportation mode used for different trip
purposes.
(3) Willingness to use RH services for HBW (home-
based work) trips. Individuals stated their prefer-
ence from a choice set of three transportation
modes (RH, auto and transit) across three sce-
narios varying cost and time.
(4) Willingness to use RH services for HBO (home-
based other) trips. Individuals stated their prefer-
ence from a choice set of three transportation
modes (RH, auto and transit) across three
scenarios varying cost and time. Cost and time
values were exactly like the HBW scenarios.
The key objective of the scenarios presented was to
acquire a confident measure of the demand for RH and
the respondent’s VOT in comparison with mutually
exclusive alternatives—private auto and public transport.
For the design of the base scenario, we used an 8km trip
from Gundermannstraße-15b to Augustenstraße-118 in
Munich city. Since the most common form of RH ser-
vices in Munich is Uber, UberApp (uber.com) was used
to estimate the total travel time and cost. The same data
was adopted from Google Maps for auto and Mu
¨nchner
Verkehrs- und Tarifverbund (MVV) for public transport.
Variables added for consideration included walking time
to vehicle, waiting time, in-vehicle time, parking search
time, walking time to destination, travel cost and parking
cost. The only distiction between the presented scenarios
is in using the RH service for a shared ride. To consider
the changes in a shared ride, total travel time was
increased by 10% and 20% and the cost was reduced by
half across the two additional scenarios.
The average response time to the survey was around
5 min. The survey was offered for two weeks from March
11 to 25, 2019, in both German and English, available
online to travelers in the city of Munich. The survey link
was emailed to several students at different universities,
employees at different companies and Facebook groups.
Additionally, flyers were printed in German and English
with the survey barcode for easier access via smartphones
and were distributed around the city center.
The raw total sample of responses was 800. However,
not all respondent completed the survey or provided their
sociodemographic attributes. The final sample was reduced
to 500. Around 90% of the respondents took the survey in
English, and respondents’ gender was evenly distributed.
Of English respondents, 90% were under 39 years old and
only 37% owned more than one auto. Conversely, 82% of
the German respondents were younger than 39 years old
but 70% owned more than one auto.
To provide realistic results applicable to travelers in
Munich, the survey responses were weighted to match
the census data for the city of Munich. Iterative propor-
tional fitting (IPF) was used. IPF is the most widely used
mature deterministic method of allocating individuals by
calculating a series of noninteger weights that reflect how
representative each individual is of each constraint (23).
Specifically, age, gender and household size distributions
were used as control totals. The extension mlogit in R
was used (24).
Incorporating the Results in a Travel Demand Model
To examine the impact of travelers’ willingness to use
RH services, we incorporated the results of the survey
Shoman and Moreno 3

into the mode choice model of the travel demand model
MITO (Microscopic Transportation Orchestrator) (25).
In this section, we will describe MITO and how its mode
choice model was modified to incorporate a new trans-
port mode: RH.
MITO Travel Demand Model. MITO uses microsimulation
to simulate each household and person individually. It
follows a disaggregated four step model with the con-
straint of travel time budget.
A synthetic population is generated using iterative
proportional updating (IPU) at three geographical levels:
county, municipality and borough (26). Households and
persons are used as input to MITO. First, the number of
trips is generated for each household. Trip generation
uses sample enumeration to select the number of trips
made for every household type for each purpose.
Household types were defined inductively by testing
67 million possible household type definitions for each
purpose (27). Second, a destination for every trip is
assigned. The destination for mandatory trips, such as
work and education trips, is already defined by the syn-
thetic population. The destination of discretionary trips
depends on the remaining travel time budget for the
household. Based on Zahavi’s theory of constant travel
time budgets (6), longer trips to work will lead to shorter
discretionary trips. The travel time budget is not a hard
constraint for an individual household, but is rather used
to influence the probabilities of choosing different desti-
nations so that travel time remains constant over time.
The mode choice model is built as a nested logit
model. It includes an auto nest (auto driver and auto
passenger), a transit nest (bus, metro, train), bicycle and
walking. RH was not included in the choice set because
it had not yet been introduced into the German market
at the time of the National Household Travel Survey
(MiD2008, mobilitaet-in-deutschland.de).
The time-of-day choice model selects a preferred arri-
val time for trips and calculates the trip departure time
based on the expected travel time by the selected mode.
The trip assignment is simulated in the multiagent travel-
based model, MATSim (28).
Mode Choice Model with RH. The mode choice of MITO
did not include RH in the choice set. To provide a new
mode choice model with RH, there were two approaches:
(1) estimate a new model based on the responses to the
SP online survey and calibrate the alternative-specific
constants to the modal split resulting from the responses,
or (2) extend the current model using an incremental
logit approach (29), which is able to estimate the modal
shares of RH based on the current modal shares and the
changes in the service characteristics with respect to the
existing set of alternatives. While the first option would
provide more accurate results for RH and the modes
included in the online survey, we would be losing the
richness of the German household travel survey’s large
sample size (176,000 versus 500 trips), modes of trans-
port covered (7 modes versus 3 modes) and range of trips
included (across the region versus one trip in the city; six
trip purposes versus two trip purposes; range of distance,
etc.), as well as the nested structure of the MITO model.
Alternatively, the incremental logit approach tries to
reflect how users react differently to changes in a refer-
ence mode and RH travel time and cost, assuming that
there are no changes in the utility of other variables or
the alternative-specific constant. After careful consider-
ation, we opted to apply an incremental logit model to
incorporate the new service into the calibrated nested
logit model of MITO.
The application of the model requires the econometric
estimation results of a reference mode and the difference
between the new mode and the reference mode. Figure 1
presents the nested model structure with the current
modes in blue and the new mode to be incorporated in
orange.
The structure includes RH in the transit nest since the
majority of auto/ride-sharing service users would substi-
tute such a service for public transport or auto as found
by (4). It is argued that the nature of RH is as a public
transport mode in the mobility system. Like the metro
mode, RH is designed to travel faster than buses and pro-
vides a point-to-point travel option.
To define the changes in utilities for RH, we use the
results from the SP survey. We assume that travel time,
travel cost and VOT are the only variables that are differ-
ent for the travelers when they make the choice between
metro and RH. All other sociodemographic attributes,
area attributes and the alternative-specific constant
Figure 1. Nested model structure (current modes in blue, new
ride-hailing mode [RH] in orange).
4Transportation Research Record 00(0)

remain unchanged for the new mode, following the incre-
mental logit model approach.
To incorporate the different sensitivity of RH users to
travel time by transit, we modify the RH time coefficient
by a factor equal to the ratio between the travel time
coefficient of RH and that of transit. The VOT for RH
users is also incorporated to calculate the generalized
cost of this mode.
The utility of RH is defined by
U0
RH ¼Umetro +bgCmetro gCRH gCmetro
ðÞð1Þ
gCRH ¼dtimeRH
dtimeMetro
timeRH +distanceRH farePerKmRH
VOTRH
ð2Þ
where
U#
RH
is the utility of RH;
U
metro
is the utility of metro (reference mode);
b
gCmetro
is the coefficient for generalized cost of
metro;
gC
RH
is the generalized cost of RH;
gC
metro
is the generalized cost of metro;
d
timeRH
is the coefficient of time for RH from the SP
survey;
d
timeMetro
is the coefficient of time for transit from the
SP survey;
time
RH
is the total travel time by RH;
distance
RH
is the total distance by RH;
farePerKm
RH
is the fare per km by RH; and
VOT
RH
is the value of time for RH.
After obtaining the utility for RH, the probability of
selecting RH is defined by
P0
RH =
exp U0
RHUmetro
nc

exp UbusUmetro
ðÞ
nc

+ exp UtrainUmetro
ðÞ
nc

+1
3P0
Transit
ð3Þ
P0
Transit =
exp U0Transit
ðÞ
PMexp UM
ðÞ
exp U0Transit
ðÞ
PMexp UM
ðÞ
+1PTransit
½
ð4Þ
where
P#
RH
is the probability of selecting RH;
U
mode
are the utilities by transit mode (metro, bus,
train);
nc is the nesting coefficient;
P#
transit
is the probability of selecting transit modes;
and
U
M
are the utilities by mode (walk, bicycle, auto nest).
Application and Sensitivity Analysis
The study area is the Munich metropolitan region. It is
located in southern Germany and includes five core
cities: Munich, Augsburg, Ingolstadt, Landshut and
Rosenheim and their suburbs. The study area is delim-
ited to municipalities that have a commuter flow higher
than 25% to any of the core cities (30). Travel demand
was generated for all the Munich metropolitan region;
however, the RH service area only included the city of
Munich city. This assumption was in line with current
RH services in the region.
A total of 20 RH scenarios were simulated with differ-
ent RH travel times and RH costs. Further, we also
simulated one scenario without RH to compare how
modal splits vary after the introduction of RH. RH
travel time was assumed to equal the travel time by auto
in the base scenario. We generated scenarios in which, as
congestion worsens, travel time increases proportionally
by a factor. Further, waiting times also increase and were
added to the in-vehicle travel time to obtain the total
travel time. For travel costs, we used the base fare of
e1.5/km, equal to the fare that Uber has in the city of
Munich. We assumed a reduction of the base fare to
e0.75/km when carpooling services were offered. Also,
we increased the base fare to e3.0/km and e6.0/km to
consider surge pricing. The RH service characteristics
varied as follows:
Travel time (four levels): 1.0 (base); 1.1; 1.2; and
1.5 times the base travel time. They were associ-
ated with waiting times of 0, 4, 8, and 18min,
respectively.
Travel cost (four levels): 0.75; 1.50 (base); 3.00;
6.00 in e/km.
Only RH service characteristics varied across scenarios.
Therefore, we maintained the same service characteristics
for transit and auto.
Analysis and Results
SP Online Survey
Preliminary Analysis. The first step was to check whether the
responses from the survey should be weighted to match
the general population characteristics in Munich. The
most recent census data available for Germany is from
2011 (www.zensus2011.de). As shown in Table 1, the
demographics of the survey respondents were different
than those observed in the census data: persons between
25 and 39 years old were overrepresented, while persons
older than 50 years old were highly underrepresented. To
balance the sample, we applied IPF to gender, age and
household size. The correlation after IPF was 0.998. Table
1 presents the share of each group in the census data and
in the unweighted and weighted survey, together with the
weights. Higher weights were given to groups that were
underrepresented, such as respondents older than 50 (with
Shoman and Moreno 5

the maximum weight of 10.41), while smaller weights were
given to groups that were overrepresented, such as persons
between 25 and 29 (with the minimum weight of 0.29).
Introducing weighted survey data could result in reduced
accuracy of the models. Furthermore, the up-weightings
for persons older than 50 were significant: they exagger-
ated by 10 times the collected responses. However, intro-
ducing the weights reduces the bias in the estimation
results caused by overrepresented groups. To better con-
trol for the impact of weighting our survey responses, we
estimated both the unweighted and the weighted models
and compared the results. Survey respondents who
selected options outside of the variable categories pre-
sented in Table 1 were very few and therefore were
excluded from the study.
Table 2 presents the mode share distribution across
the three survey scenarios based on the survey responses.
As the scenarios are the same in both sets, RH seems to
have had a higher preference when traveling on trips with
a purpose other (HBO) than work (HBW). Scenario 2,
with the shorter time and least cost, had the highest
modal share in both sets, with 8.04% for HBO purposes
and 4.66% for HBW purposes.
Mode Choice Model Estimation. A multinomial logit model
(MNL) was used to estimate a mode choice model from
the survey responses. The gmnl package in RStudio (19)
was used to build the two models for each purpose:
HBW and HBO. The wide format dataset included indi-
vidual and transport characteristics as designed in the
survey, removing the highly correlated variables.
Weighted and unweighted responses were used to control
for the impact of the weighting procedure on the sensitiv-
ity of the model. The signs of the coefficients for
weighted and unweighted data remain equal (e.g., per-
sons in households with an auto are less likely to use
RH), although the magnitudes differ. Given that the
weights were introduced to minimize the bias of the sur-
vey respondents in age and household size distributions,
it was likely that the greater differences were in those
variables. The unweighted coefficients were larger for
young adults because they were overrepresented in the
sample and their responses were disproportionally affect-
ing the results compared with the population. After
weighting the responses, the coefficient for young adults
was reduced, correcting for this imbalance. Looking
at the two sets of cross-tabulations (weighted and
unweighted), the estimates still look reasonable and the
differences could be explained by the weighting method.
HBW and HBO models had a Mcfadden R
2
value of
0.30 and 0.17, respectively, which is acceptable for the
number of scenarios. For HBW, gender, employment,
auto license, living area type and residence period in
Table 1. Weighted Survey and Population Statistics
Group share
Variable Census data Unweighted survey Weighted survey Weights
Gender
Male 48.92% 45.00% 49.00% 1.09
Female 51.08% 55.00% 51.00% 0.93
Age
\18 0.00% 0.00% 0.00% 0.00
18–24 12.87% 19.52% 14.23% 0.73
25–29 7.88% 30.88% 9.09% 0.29
30–39 15.99% 37.45% 16.56% 0.44
40–49 20.08% 8.17% 18.68% 2.29
.50 43.18% 3.98% 41.44% 10.41
Household size
1 39.12% 33.80% 39.52% 1.17
2 30.86% 34.80% 31.22% 0.90
3 13.75% 14.20% 13.36% 0.94
4 or more 16.27% 17.20% 15.90% 0.92
Table 2. Survey Modal Split across Scenarios, by Trip Purpose
Scenario number
Trip
purpose RH Auto
Public
transport
Scenario 1
(TT, C)
HBW 1.28 18.57 80.15
HBO 2.09 40.33 57.58
Scenario 2
(1.1TT, 0.5C)
HBW 4.66 19.19 76.15
HBO 8.04 36.73 55.23
Scenario 3
(1.2TT, 0.5C)
HBW 3.84 24.67 71.49
HBO 6.39 38.28 55.33
Note: C = cost ; HBW = home-based work; HBO = home-based other;
RH = ride-hailing; TT = travel time.
6Transportation Research Record 00(0)

Munich were not statistically significant. The number of
household autos and the interest in using RH were statis-
tically significant and they increased the likelihood of
selecting RH. RH use seemed to be more common with
the younger age spectrum (18–39 years old). Bigger
households and households located far from a transit
station were also more likely to use RH services. For
HBO, the model showed a similar behavior to the HBW
model except that the distance to a transit station was
not significant.
These results of RH being common with a younger
group agree with the findings of (10) and (13). The higher
use of RH services by households with fewer autos and
all income groups supports the key findings in the TCRP
report (19).
Based on the coefficients of time and cost, VOT for
RH was derived. We distinguished two different income
groups: \e1,500 and .e1,500. VOT was calculated by
time and cost coefficients for RH using Equation 5 (31).
Value of time VOTðÞ¼
btime
bcost 60
e
hr
 ð5Þ
where
btime is the coefficient of time using RH; and
bcost is the coefficient of cost for an income group.
Table 3 presents the results for the estimated VOT
across income groups for HBW and HBO trips.
Higher income groups being willing to use RH services
more than the lower income groups for both trip purposes
agrees with the findings of (10) in Toronto. The VOT for
various modes, purposes and income groups used in
MITO is presented in Table 4. For the lower income
group, the VOT for HBW and HBO of RH is higher than
transit and any other mode. In other words, lower income
group users of other modes are likely to switch to RH.
For the higher income group, the VOT for HBW and
HBO of RH is between auto driver and auto passenger.
This means that higher income group users would be will-
ing to pay more for eliminating the task of driving.
RH Service Scenarios
Figure 2 presents the comparison of mode split with and
without ride-hailing (RH) in the base scenario (travel
time as auto travel time, no waiting time and e3.0/km
fare) at the following trip purposes: home-based work
(HBW), home-based education (HBE), home-based shop
(HBS), home-based other (HBO), non-home-based work
(NHBW), and non-home-based other (NHBO).
For HBW, HBS, HBO, NHBW and NHBO trips, the
share for RH was less than 7% and a large proportion of
the RH share was gained from public transit. The results
agree with the survey model estimates, in which RH rep-
resented 8.04% of HBO trips and 4.66% of HBW trips.
HBE trips had an RH share of around 18%, of which the
majority share was gained from public transport (10%)
and the active transportation modes (7%). These findings
show RH trips mostly substituting transit services and
active modes which is quite opposite to the RH effect in
the Toronto study by (10) where RH complemented tran-
sit and active transport modes.
Figures 3–6 present the share of RH across various
scenarios for the different trip purposes. In each figure,
travel time is equal to one level (1, 1.1, 1.2 and 1.5) and
travel cost varies. Figure 3 presents the RH share at TT
= 1 or the least congested level and various costs.
Compared with other trip purposes, HBW did not seem
Table 3. Value of Time across Income Groups, by Purpose
Income group HBW HBO
\e1,500/month 13.48 10.47
.e1,500/month 15.92 18.35
Note: HBO = home-based other; HBW = home-based work.
Table 4. Microscopic Transportation Orchestrator Value of
Time across Income Groups, by Mode and Purpose
Income
group Auto driver Auto passenger Transit
HBW HBO HBW HBO HBW HBO
\e1,500 4.63 4.44 7.01 4.30 8.94 5.06
e1,500–
e5,600
8.94 6.11 13.56 8.31 17.30 9.78
.e5,600 12.15 8.63 18.43 11.30 23.50 13.29
Note: HBO = home-based other; HBW = home-based work.
Figure 2. Modal split by purpose, with and without ride-hailing
(RH), in the base scenario, showing home-based work (HBW),
home-based education (HBE), home-based shop (HBS), home-
based other (HBO), non-home-based work (NHBW), and non-
home-based other (NHBO).
Shoman and Moreno 7

to be very affected by the cost changes. The maximum
variation (between lowest and highest cost) of HBW was
less than 0.6% in comparison with 3% for other pur-
poses. The trip purposes with highest sensitivity to cost
were NHBW and NHBO.
At TT = 1.1, the share of RH (Figure 4) across all
purposes was reduced compared with the base travel time
scenarios, as expected. The variation of RH share with
increasing cost was also reduced. Variation with cost fol-
lows the same pattern as in the base travel time scenario
but with lower reduction. Similar conclusions may be
obtained from the travel time increase by 20% (Figure 5).
The results for the highest travel time varied signifi-
cantly from the previous scenarios (Figure 6). The share
across all purposes was reduced, with the highest reduc-
tion being in the share of HBE, almost reduced to half
compared with congestion at TT = 1.2 (from a maximum
of 11.4% to a maximum of 6.4%). Further, this reduction
was lower for other purposes: for HBO, the maximum
varied from 6.2% to 4.6%, or for HBS from 4.1% to
3.6%. This indicates that HBE trips were more sensitive
to travel time than other trip purposes. No HBW trips
used RH when costs were higher than e0.75/km.
The largest share of RH trips for all trip purposes
happened at the least congested level (TT = 1) and low-
est cost level (e0.75/km) and the smallest share of RH
trips happened at the most congested level (TT = 1.5)
and highest cost level (e6/km). RH share for all purposes
reflected a consistent behavior across scenarios when it
came to changes in RH share with increasing cost. It is
noticeable that the total RH trips share at TT = 1 and
e3/km is equal to the RH share at TT = 1.1 and e1.5/
km. There may be other combinations that balance costs
and travel time, producing similar modal share for RH.
Conclusion
The rapid growth and expansion of RH services with wide-
spread claims about their benefits to other transportation
modes, despite the lack of open data and limited research
available on the topic, increases the importance of innova-
tive research methodologies to gather representative data.
Using Munich city as a case study, this project predicts the
impact of RH on modal share based on the user’s willing-
ness to pay for such services. Despite a potential bias in
responses, the results provided a preliminary understanding
of the willingness to use RH services and the preferences for
other similar transportation modes.
Figure 3. Ride-hailing (RH) share (%) versus cost (e/km) at
travel time (TT) = 1, showing home-based work (HBW), home-
based education (HBE), home-based shop (HBS), home-based
other (HBO), non-home-based work (NHBW), and non-home-
based other (NHBO).
Figure 4. Ride-hailing (RH) share (%) versus cost (e/km) at
travel time (TT) = 1.1, showing home-based work (HBW), home-
based education (HBE), home-based shop (HBS), home-based
other (HBO), non-home-based work (NHBW), and non-home-
based other (NHBO).
Figure 5. Ride-hailing (RH) share (%) versus cost (e/km) at
travel time (TT) = 1.2, showing home-based work (HBW), home-
based education (HBE), home-based shop (HBS), home-based
other (HBO), non-home-based work (NHBW), and non-home-
based other (NHBO).
8Transportation Research Record 00(0)

Given that the number of survey responses was lim-
ited for estimating a mode choice to be applied to the
entire study area and with all modes available in the
choice set, we used the incremental logit approach to
incorporate a new transport mode into an existing nested
logit model calibrated for the study area. The survey
responses were used for the assumptions on how RH
utility may differ from the utility of the base mode, which
was metro. Specifically, we derived the difference in
travel time perception between RH and metro and the
VOT for RH for two income categories. The results for
the base scenario were similar to the survey responses,
with estimated modal shares for RH of 8.04% for HBO
and 4.66% for HBW.
Furthermore, we carried out a sensitivity analysis of
RH modal share for different travel time and travel cost
scenarios. The results indicate that RH ridership was
more sensitive to travel time than travel cost: an increase
from e1.5/km to e3.0/km reduced the ridership by 10%,
but a 10% increase in travel time reduced the ridership
by 13% for HBO and HBS trips. HBE trips were more
sensitive to travel time than other trip purposes, and
HBW trips were hardly ever made by RH (less than
1% in the most favorable scenario). This study can
help the local region and policy makers understand the
impacts of RH when making policy decisions and engi-
neering developments. The share of RH trips over vari-
ous congestion levels and costs allows decision makers
to understand how demand behavior reacts against
both variables.
There are some limitations with regards to the survey
scenario designed, in which transit and auto scenarios
remained constant and only one specific route was
designed. The overrepresentation of some population
segments was corrected by using weighted responses,
although a higher representation of all population seg-
ments would be desirable. The VOT estimations were
also produced with a simple MNL model in which some
important attributes were not significant. Increasing the
sample size from the survey, the representation of differ-
ent population segments and the number of routes could
improve the results. Further, the routes could be adapted
to each respondent’s commute trip and most common
shopping or leisure trip, by origin, destination and time
of day. For future work, the modeling of induced
demand and choices between single occupancy RH vehi-
cle and multiple occupancy (pooled ride) will be
explored. Furthermore, once ride-hailing is more com-
mon in our study area, we will reconduct an RP survey
with a redesign of the scenarios. This approach would
better reflect the actual experience of RH users, similar
to the study done by (14). The new survey will also be
distributed differently to cover the entire population
more homogeneously.
From the supply side, pickup and drop-off of passen-
gers can be simulated from door-to-door to zone-to-zone
or certain stops. Further, the results from the travel
demand model for mode choice will be incorporated into
a traffic assignment model to estimate the impact of RH
on congestion levels and mobility in the city of Munich.
Acknowledgment
We would like to acknowledge the valuable feedback and com-
ments provided by Rolf Moeckel during the defense of the mas-
ter thesis.
Author Contributions
The authors confirm contribution to the paper as follows: study
conception and design: M. Shoman and A. T. Moreno; data
collection: M. Shoman; analysis and interpretation of results:
M. Shoman; draft manuscript preparation: M. Shoman and A.
Moreno. All authors reviewed the results and approved the
final version of the manuscript.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: The research was completed with the support of the
Technische Universita
¨tMu
¨nchen Institute for Advanced Study,
funded by the German Excellence Initiative and the European
Union Seventh Framework Programme under grant agreement
number 291763.
Figure 6. Ride-hailing (RH) share (%) versus cost (e/km) at
travel time (TT) = 1.5, showing home-based work (HBW), home-
based education (HBE), home-based shop (HBS), home-based
other (HBO), non-home-based work (NHBW), and non-home-
based other (NHBO).
Shoman and Moreno 9

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