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A Level Playing Field for Comparing Air and Rail Travel Times

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Abstract and Figures

Background Shifting travellers from air to rail can reduce environmental impacts and is an important European Union goal. Online travel planning applications allow travellers to easily compare air and rail transport choices, however, they may not accurately consider time travellers spend at the airport or railway station since these depend on buffer times travellers use to protect against delays. Methods This research investigated the actual time spent at airports and railway stations to analyse the accuracy of travel planning applications and help improve the quality of travel time estimates.The research used a travel time recording application to determine the time spent by passengers at airports and railway stations. Data was collected for 312 trips. The research was supplemented by an extensive literature review of dwell times and multimodal travel planning applications. Results The research found that travellers spent an average of 157 minutes at airports and 32 minutes at railway stations. Comparing these results to travel planning application, the information shows that the applications significantly underestimate time spent at airports and slightly underestimate time spent at railway stations.The use of unrealistic airport waiting times in travel planning applications distorts traveller perception in favour of air travel. Conclusion Therefore, railway operators should support the development of improved travel planning applications that better consider waiting times. Improving these applications would be much more cost effective than infrastructure improvements designed to save a few minutes of travel time.
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48
DOI: 10.2174/1874447801913010048, 2019, 13, 48-56
The Open Transportation Journal
Content list available at: https://opentransportationjournal.com
RESEARCH ARTICLE
A Level Playing Field for Comparing Air and Rail Travel Times
Thomas Sauter-Servaes1,*, Thomas Krautscheid2 and Alexander Schober3
1ZHAW Zurich University of Applied Sciences, School of Engineering, Programme Director Transportation systems, Winterthur, Switzerland
2Quotas GmbH, Department Transport & Environment, Head of Department Transport & Environment, Hamburg, Germany
3Alexander Schober, Quotas GmbH, Department Transport & Environment, Consultant, Hamburg, Germany
Abstract:
Background:
Shifting travellers from air to rail can reduce environmental impacts and is an important European Union goal. Online travel planning applications
allow travellers to easily compare air and rail transport choices, however, they may not accurately consider time travellers spend at the airport or
railway station since these depend on buffer times travellers use to protect against delays.
Methods:
This research investigated the actual time spent at airports and railway stations to analyse the accuracy of travel planning applications and help
improve the quality of travel time estimates.The research used a travel time recording application to determine the time spent by passengers at
airports and railway stations. Data was collected for 312 trips. The research was supplemented by an extensive literature review of dwell times and
multimodal travel planning applications.
Results:
The research found that travellers spent an average of 157 minutes at airports and 32 minutes at railway stations. Comparing these results to travel
planning application, the information shows that the applications significantly underestimate time spent at airports and slightly underestimate time
spent at railway stations.The use of unrealistic airport waiting times in travel planning applications distorts traveller perception in favour of air
travel.
Conclusion:
Therefore, railway operators should support the development of improved travel planning applications that better consider waiting times.
Improving these applications would be much more cost effective than infrastructure improvements designed to save a few minutes of travel time.
Keywords: Travel time, High-speed rail, Air transport, Airport waiting time, Travel planning, Rail transport.
Article History Received: February 25, 2019 Revised: April 14, 2019 Accepted: April 23, 2019
1. INTRODUCTION
A growing number of online travel planning applications
provide multimodal door-to-door travel time estimates that
travellers can use to compare travel times by air and rail. These
travel time estimates depend on assumptions made for traveller
waiting time at airports and railway stations. These assumed
waiting times have a significant impact on total travel time and
therefore a traveller’s mode choice decision. The purpose of
this research was to measure the different elements of door-to-
* Address correspondence to this author at the ZHAW Zurich University of
Applied Sciences, School of Engineering, Programme Director Transportation
systems, Winterthur, Switzerland; Tel: +41589347177;
E-mail: thomas.sauter-servaes@zhaw.ch
door travel times by air and rail, and then compare these to data
from several popular online travel planning applications to
determine if these applications underestimate waiting time and
assess how this could impact the mode choice decision
between air and railway travel.
The next section presents an introduction to the effects of
air and rail transport on climate change. It highlights the
importance of competition between the two modes of transport.
Given the high relevance of travel time for mode choice
between these competing travel services, the last section of the
introduction discusses the different segments of door-to-door
travel time. Based on this, the second section presents the
research method for determining real door-to-door travel times
A Level Playing Field for Comparing The Open Transportation Journal, 2019, Volume 13 49
in air and rail transport. The empirical results are described in
Section 3 and are discussed in Section 4, in particular with
regard to the strengths and weaknesses of the chosen research
approach. The last section presents conclusions and recommen-
dations.
1.1. Climate Change Impacts of Air and Railway Travel
Climate change is creating significant social, economic and
environmental problems. Consequently, reducing climate
change is a strategic priority for the European Union (EU) [1].
The European Commission’s goal is to reduce Greenhouse Gas
(GHG) emissions, a major cause for climate change, by
80-95% in 2050 [2], with an intermediate target of a 40%
reduction by 2030 (both relative to 1990 levels) [3].
In the transport sector, global GHG emissions are projected
to double by 2050 without aggressive and sustained policy
intervention according to the UN Intergovernmental Panel on
Climate Change (IPCC) [4]. Consequently, the EC has set a
goal for the transport sector to reduce its share of GHG
emissions by 20% in 2030 compared to 2008 levels, and by
70% in 2050 [5].
One of the key problems faced in reducing transport sector
GHG emissions is the strong growth in air travel [6, 7]. Much
of this growth has been stimulated by the rise of low-cost
carriers. The number of flights in Europe increased by 80%
between 1990 and 2014 and is forecast to grow by a further
45% between 2014 and 2035 [8]. Airbus forecasts an increase
of intra-regional and domestic Revenue Passenger Kilometres
(RPK) of 2.9% per year for the European market between 2016
and 2036 [9]. The International Air Transport Association
(IATA) projects European passenger growth of 2.3% per year
until 2036; this is 550 million passengers more than today and
will result in over 1.5 billion passengers by 2036 [10].
The environmental impacts of air travel in Europe have
increased significantly over the past 25 years. Direct emissions
from aviation account for about 3% of the EU’s total GHG
emissions. However, the impact of aviation on climate change
is almost doubled when the full effects of radiative forcing are
included [11]. If global aviation was a country, it would rank in
the top 10 of emitters. Some studies forecast that global
aviation will generate 22% of total GHG emissions by 2050 if
the sector continues to fall behind efforts in other sectors [12].
Much research has been conducted on developing technical
and operational measures such as CO2 standards for aircraft or
sustainable biofuels, but studies show that these may be
insufficient to significantly reduce aviation emissions in the
next 20 years [8, 13, 14]. In fact, air traffic and GHG emission
growth have outpaced aviation sector efficiency gains for
decades due to the continuous increase in passengers [6].
Given the difficulties in reducing GHG emissions in the
aviation sector, an important EU goal is to support a modal
shift from air travel to rail [15]. Shifting air travel to rail
reduces environmental impacts [16]. According to Eurostat,
47% of European air passengers travelled on intra-European
routes in 2016, another 17% on national routes [17]. Many of
these trips could be shifted to electrically powered railways –
especially to Europe’s High-Speed Rail (HSR) network which
is expected to triple in length by 2030. The carbon footprint of
electrically powered railways depends on the primary energy
used to generate the electricity, but part of the energy is already
generated with renewable sources today (e.g. 100% green
electricity for German ICE fleet) and the European production
of renewable energy is growing [18].
In order to achieve its GHG emissions target, the European
Commission’s goal is that the majority of medium-distance
passenger transport in 2050 be by rail [4]. Unfortunately,
current trends contradict these plans. Between 1995 and 2010,
HSR travel grew significantly faster than air traffic (8.1%
versus 3.1% per year), however, the trend has reversed since
2010. Since then, intra-European air traffic has increased by
3.8% per year in passenger-kilometres, almost twice as fast as
HSR, which grew by 1.5% per year [19].
1.2. Short Review of Air-HSR-Competition
Many studies have explored the relationship between air
transport and HSR [20]. Competition between HSR and air
transport started in 1964 with the opening of the world’s first
high-speed train service Tokaido Shinkansen between Tokyo
and Osaka. In 1981, France opened the first European HSR
line: the Train à Grande Vitesse (TGV) from Paris to Lyon. In
contrast to the Shinkansen, which operated on dedicated tracks,
the TGV was fully compatible with the existing railway
network.
Germany (1991) and Spain (1992) followed France with
their Intercity Express (ICE) and Alta Velocidad Española
(AVE) trains. European HSR trains are capable of reaching
speeds of over 200 km/h on upgraded conventional lines and of
over 250 km/h on new lines designed specifically for high
speeds [21, 22]. The improved quality and speed of HSR
service enabled railways to return to competitiveness in the
long-distance passenger transport market [23].
Research on the interaction between air transport and other
modes initially focused on competition between modes; more
recent research also considers modal complementarity [24].
Sun et al. synthesize and discuss recently published studies on
competition between HSR and air transport [20]. Their meta-
analysis finds that travel time is the most critical factor in
determining the competitiveness between HSR and air
transport. Janic developed one of the earliest models of travel
time competition between the two modes, concluding that HSR
can compete with air transport over a range of distances from
400 to over 2000 km [25]. Givoni found that the introduction
of HSR on routes of around 300 km leads to an almost full
withdrawal of airline service (e.g. between Frankfurt and
Cologne or Brussels and Paris), while on routes of over 1000
km HSR cannot seriously compete with air transport (e.g.
between Tokyo and Fukuoka, 1070 km, the HSR share of the
traffic is only 10%) [23]. In between 300 and 1000 km, HSR is
most competitive against air transport [20, 23, 26]. Research
from Steer Davies Gleave reduces this competitive range to
between 175 and 800 km, where door-to-door travel times of
HSR are shorter than air travel [27].
1.3. Traveller Scheduling Model
Given the importance of travel time on air–rail
competition, it is necessary to develop realistic travel times.
50 The Open Transportation Journal, 2019, Volume 13 Sauter-Servaes et al.
These travel times should consider the entire door-to-door
travel chain. However, most published work focuses only on
the in-vehicle time or selected elements of the entire door-to-
door travel chain. In contrast, door-to-door travel is the
complete chain of process steps including access from origin to
the travel mode, waiting time, in-vehicle travel time, transfers,
and egress from the mode to the final destination. Finally, it is
critical to note that travellers often add buffer times to the
minimum calculated time needed to perform these activities in
order to account for delays or uncertainty. This is particularly
true for air travel given the uncertainty of activities such as
security control and the high penalty for missing a flight.
The in-vehicle travel times and transfer times can be easily
obtained by travel planning and information applications, but it
is much more difficult to determine the access to air-
port/railway station and egress from airport/railway station
times (including time spent in activities at the airport/station).
These two steps are considered in more detail below.
1.3.1. Access to Long-Distance Transport
The scheduling model of Noland and Small [28] has been
widely accepted as a standard tool for analysing the effects of
travel time variability [29]. The model assumes that travellers
make a trade-off between being earlier or later than their
preferred arrival time. Koster et al. extended the model to
account for the specific concerns of air travellers. They found
that the departure time from home chosen by air travellers
depends strongly on the probability of missing a flight, and the
corresponding expected cost [29].
The same cost calculations can be assumed for HSR
travellers. However, the rail travellers plan against the
background of different procedural conditions. (Fig. 1)
illustrates the time versus cost structure for air (black) and rail
travellers (red) using the approach from Koster et al. [29].
As shown in Fig. (1), there are three main differences
between rail and air traveller’s cost structure for accessing the
travel mode. These differences lead to structurally shorter
process and waiting times at railway stations and have a
decisive effect on the perceived time uncertainties and the
resulting time buffers:
Spatial structure: The main railway stations in most
European cities are located closer to city centres and
are better linked to regional transport networks than
airports. This results in shorter minimum travel times
to the station and reduced buffer times due to the
availability of more travel alternatives in case of local
service interruptions.
Process Structure: With a few exceptions (e.g. Euro-
star services), European HSR systems are open-gated
systems. There are no permanent security checks and
luggage does not need to be checked-in separately. The
process for boarding an HSR train generally consists of
walking to the platform and stepping onto the train via
many doors. The process for boarding a short/medium
haul flight consists of a fixed sequence of successive
handling processes (check-in, security control, walking
to the gate, and boarding via one door). Furthermore,
most people are much more familiar with railway
stations and the train boarding process than they are
with airports and the flight boarding process. This
makes it particularly difficult to forecast the time
Fig. (1). Access cost functions of air and rail travellers (based on [29]).
minimum travel
time to the station at
early
the
arrivals
station the station
minimum buffer at
reduced cost
risk by HSR
Time
departure time
from home (AIR)
departure time
from home (HSR) time station/airport
preferred arrival
departure (HSR)
final boarding and final boarding
time (AIR)
flight departure
buffer because
airport service
of
time
at the airport
early arrivals
minimum travel
time to the airport
cost of travel
time, parking
and fuel or
ticket expense
reduced cost
risk by HSR
cost of missing
a flight
Costs
A Level Playing Field for Comparing The Open Transportation Journal, 2019, Volume 13 51
required for process steps such as baggage check-in and
security checks. In summary, the minimum processing time
and the buffer time to account for uncertainty are both higher
for air travel than for railway travel.
Failure Costs: On average, the cost of an airline ticket is
higher than an HSR ticket [30]. Furthermore, a much higher
proportion of airline tickets are not easily changeable or
refundable. These factors make the cost of missing a flight
higher than missing an HSR trip. Finally, given the lower
frequency of flights, even if an airline ticket can be used for a
later flight, the risk of losing time due to a missed flight is
noticeably higher than for a missed train since there is frequent
railway service between many main destinations.
1.3.2. Egress from Long-Distance Transport
The process of exiting the long-distance travel vehicle
(airplane, HSR) and leaving the airport or station also consists
of several steps which differ significantly between the modes.
First, consider the time spent leaving the vehicle itself. On
a typical high-speed train all the passengers on a single wagon
leave via two doors (e.g., 74 passengers on an ICE 3 middle
wagon leave through two doors). In contrast, all the passengers
on an airplane generally must leave the aircraft via a single
door (e.g., the approximately 160 passengers on a typical A320
at an airport using a passenger boarding bridge). When using a
remote position with the following bus transfer, the average
time to actually reach the terminal is even longer. This
difference in time leaving the vehicle is exacerbated by the
lower average capacity utilisation figures in long-distance rail
travel: 53% (German rail) [31] vs. 83% in short-/medium-haul
intra-EU air traffic [32, 33].
Second, many airline travellers also need to pick-up
checked luggage, which adds to the time spent at the airport. In
contrast, railway passengers keep their luggage with them
through the trip. Finally, airports are much larger than railway
stations which increases the travel time spent travelling from
the gate to the baggage claim/exit, and then on to their connec-
ting transport.
1.3.3. Research Question
As the analysis presented above shows, the process times
for access and egress are structurally lower for HSR than air
travel, and furthermore, HSR travel requires lower buffer times
to ensure against delays in access processes (e.g., security). But
how is this structural advantage for HSR reflected in mode
choice decisions? Is it possible for travellers to obtain accurate
door-to-door travel times from travel planning applications?
How do these applications forecast these structural time
differences and buffer times? How do buffer times correspond
to real passenger behaviour? The research was carried out to
help answer these questions.
2. RESEARCH METHODOLOGY
A three-step approach was used to address the research
questions. The first step consisted of analysing the compara-
bility of rail and air travel time information presented on digital
travel information portals. The second step consisted of
examining research on real airport/railway station access and
egress times. The third step consisted of performing and
analysing a survey of real door-to-door travel times for flights
and railway trips in European long-distance travel.
2.1. Analysis of Digital Travel Information Systems
An increasing number of flight and train bookings are
being made independently by travellers without the support of
sales staff from travel providers. Information and booking are
increasingly carried out via large online sales platforms, which
are either intra- or multimodal. The research assessed how
these information systems consider buffer times for long-
distance travel.
The research started by identifying a set of travel planning
application platforms that could be used by travellers making
travel decisions for long distance travel. The core criterion for
selecting these platforms was their multimodal design, thus, in
principle, enabling them to compare travel times and prices
between air and rail transport. The research started with
platforms identified in the market analysis carried out for the
European Commission’s DORA project [34]. These platforms
were: GoEuro, Google Maps, Rome2Rio, Route Rank and
Qixxit. This research added the platforms fromAtoB and
Kayak. All other freely available platforms on the Internet did
not meet the selection criteria.
The research examined each of the platforms to determine
to what extent door-to-door travel time comparisons are
actually possible and whether these comparisons are accurate.
It identified the buffer times currently taken into account at
railway stations and airports in the case of door-to-door
information provided by the platforms. The analysis used the
terminology and segmentation of the DATASET2050 project
[35] to determine what time requirements were displayed (if
any) for the following journey phases:
Door-to-Kerb (D2K): Transfer from the starting
address to the station/airport
Kerb-to-Gate/Platform (K2G/K2P): journey seg-
ment from arrival at the point of access to the main
means of travel (pre-travel dwell time)
Gate/Platform to Gate/Platform (G2G/P2P): Main
travel segment from boarding to alighting, includes the
phases off-block, taxiing-out, take-off, route, landing,
taxiing-in and on-block in air traffic.
Gate/Platform-to-Kerb (G2K/P2K): Journey seg-
ment from gate/platform to connecting means of trans-
port, also includes baggage reclaim and customs in air
traffic (post-travel dwell time)
Kerb-to-Door (K2D): Transfer from the station/
airport to the destination address
2.2. Examining Research on Real Airport/Railway Station
Access and Egress Times
The authors carried out a literature review of previous
research on airport/railway station access and egress time. This
review focused especially on dwell times, the length of stay in
airports or stations. Given the very small amount of dwell time
data in published academic literature, the researchers also used
data from airport and railway station operators in the analysis.
52 The Open Transportation Journal, 2019, Volume 13 Sauter-Servaes et al.
Operators have a high incentive for collecting this dwell time
because it is an important indicator for marketing sales and
advertising space.
2.3. Survey of Real Door-to-Door Travel Times in
European Long-Distance Traffic
The main part of this research was collecting and analysing
door-to-door travel times for medium to long-distance trips by
air and rail. Data were collected by an existing panel organised
by an independent quality and market research institute. The
panel currently consists of more than 4,500 active participants
in private households throughout Germany. As part of this
research, the panel participants were provided with a mobile
telephone application specifically developed for this project to
measure travel times by travel segment. This application was
designed as a hybrid mobile app with the web framework
Ionic. Hybrid mobile apps combine the advantages of native
and web apps. By providing platform-specific libraries, which
allow access to the hardware and software components, they
run on different operating systems (e.g. Android, iOS,
Windows). The user interface was created with HTML and
SASS/CSS, the logic was programmed with JavaScript. The
use of the app was voluntary and was supported by an
incentive bonus system, which is also used with other surveys.
The application provided participants with a simple and
intuitive interface to delimit five stages of their journey in time
while the device simultaneously logged their location via GPS.
Participants simply pressed the app button at the following
points of their trip:
Leave starting address
Arrival at the station/airport
Departure
Arrival at destination station/landing at the destination
airport
Leaving station/airport
Arrival at the destination address
The application then automatically registered the time and
place. Travellers were able to manually correct time-related
data and add comments, but the GPS-data was not accessible.
In addition, the participant entered manually whether the trip
was a private or business trip. (Fig. 2) presents application
screenshots for entering data on a private rail trip and a
visualisation of results for the last two travel stages.
A total of 312 trips were recorded in the period from
March to October 2017. These included 74 air trips and 238
train trips from and to seven German cities: Berlin, Cologne,
Frankfurt, Hamburg, Hannover, Leipzig, Munich. The duration
data for the following five travel stages were generated from
the raw data of the app-supported manual tracking:
Transfer time to airport/station
Time between arrival at the airport/station and start of
the flight/rail travel (dwell time 1)
Flight time / rail travel time
Time between arrival at the destination airport/station
and leaving the airport/station to the final destination
address (dwell time 2)
Transfer to destination
The analysis calculated average stage travel times for
business trips, private trips and all trips.
Given the resources available, the aim was not to conduct
an extensive survey but rather to perform an initial study which
could be used as a basis for a more far-reaching subsequent
study.
Fig. (2). Screenshots of travel survey application.
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A Level Playing Field for Comparing The Open Transportation Journal, 2019, Volume 13 53
3. RESULTS
The results show that most of the travel planning
applications examined in the research do not provide full door-
to-door travel times for both air and rail trips. Furthermore,
those applications that do provide this information clearly
underestimate the length of time spent at the access and egress
points for long-distance transportation (i.e., at airports and
railway stations). An important reason is that lack of sufficient
travel time data for the access and egress stages of the journey.
The travel survey application data collected in this research
showed that air travellers reach the airport on average just
under two hours before departure, showing that waiting time at
the airport is a significant component of total travel time. These
results are discussed in the following sections.
3.1. Travel Information Portals Obscure True Travel
Times
Table 1 summarises the type of travel time information
provided by the seven travel planning applications examined in
this research.
In terms of comparing multimodal trips (i.e., trips that
include local access to the railway station or airport) only
RouteRank and Rome2Rio provide users with total travel times
for both air and rail journeys. RouteRank provides public
transport stop to public transport stop travel times for air and
door-to-door travel times for rail journeys, while Rome2Rio
provides door-to-door travel times for both air and rail
journeys. Google Maps provides door-to-door travel times for
rail trips but does not include local access time in its travel
times for air. All the other applications examined use public
transport station to public transport station data for rail
journeys and airport to airport data for flight alternatives.
Only two of the applications examined consider waiting
time at airports in their travel time estimates. RouteRank
provides for an airport arrival at least 90 minutes before
departure. This is consistent with official recommendations for
intra-European flights. Rome2Rio, on the other hand, provides
for an airport arrival only 60 minutes before departure, even at
large international hubs such as Frankfurt.
RouteRank and Rome2Rio are also the only two appli-
cations that consider time from aircraft arrival at the gate to
departure from the airport in their air travel time estimates.
RouteRank adds a 60 minute transfer time between landing and
onward journey. Rome2Rio includes a transfer time of 10
minutes.
For rail trips all the applications except Kayak include
transfer times to/from the station entrance and platform based
on minimum walking times, but do not include any buffer for
potential delays in reaching the station, nor do they consider
orientation time (e.g., the time needed to determine the correct
platform).
The analysis clearly shows that railway travel is placed at a
disadvantage in all the travel planning applications. First, the
applications provide flight only (off block/on block) travel
times for air travel, but total travel times including local
access/egress for railway travel. Second, the applications that
take airport waiting times into consideration appear to
underestimate these times. Finally, all the applications appear
to underestimate railway station waiting times.
3.2. Poor Data on Passenger Dwell Times at Access and
Egress Points
While it seems likely that the two applications that include
airport waiting time underestimate this time, there is very little
empirical data on airport waiting time in the transport research
literature. Small sample surveys conducted in Australia show
an average airport stay of 111 or 113 minutes before departure
[36, 37].
Table 1. Summary of travel time data provided by travel planning applications (accessed 23 May 2018).
fromAtoB GoEuro Google Maps Kayak Rome2Rio RouteRank Qixxit
D2D search no no yes no yes yes no
Intermodal travel-chains no no no no yes yes no
AIR – Display of Travel Times Per Journey Segment
D2D travel time no no no no yes (yes) no
D2K segment no no no no yes PT stop to Kerb no
K2G segment no no no no Minimum 60 min Minimum 90 min no
G2G segment yes yes yes yes yes yes yes
G2K segment no no no no Minimum 10 min Minimum 60 min no
K2D segment no no no no yes Kerb to PT-stop no
RAIL – Display of Travel Times Per Journey Segment
D2D travel time no no yes no yes yes no
D2K segment PT-stop to Kerb PT-stop to kerb yes no yes yes PT-stop to Kerb
K2P segment Transfer time
without buffer
Transfer time
without buffer
Transfer time
without buffer no Transfer time
without buffer
Transfer time without
buffer
Transfer time
without buffer
P2P segment yes yes yes yes yes yes yes
P2K segment Transfer time
without buffer
Transfer time
without buffer
Transfer time
without buffer no Transfer time
without buffer
Transfer time without
buffer
Transfer time
without buffer
K2D segment Kerb to PT-stop Kerb to PT-stop yes no yes yes Kerb to PT-stop
54 The Open Transportation Journal, 2019, Volume 13 Sauter-Servaes et al.
Table 2. Door-to-door air travel times (in minutes; sample: 74 passengers, 57 private trips, 17 business trips).
Transfer
to Airport
Dwell Time
at the Airport
Flight Time Dwell Time
at the Airport
Transfer
to Destination
Total Travel Time
All trips 57 118 143 39 61 417
Private trips 58 123 154 43 64 442
Business trips 55 101 113 25 56 350
Table 3. Door-to-door rail travel times (in minutes; sample: 238 passengers, 176 private trips, 62 business trips).
Transfer
to Station
Dwell Time
at the Station
Rail travel time Dwell time
at the Station
Transfer
to Destination
Total travel time
All trips 24 20 198 12 21 275
Private trips 23 20 211 12 20 286
Business trips 24 20 163 12 21 240
On the other hand, many airports regularly survey how
long departing air travellers stay at the airport because this time
has a strong impact on income. These surveys are generally
based on questionnaires of departing passengers and not the
result of accurate measurements [38]. The published data
varies considerably in scope and differentiation. For example,
Amsterdam Schiphol expects local boarding passengers to
spend 147 minutes at the airport [39]. Zurich Airport
determined that 54% of local passengers spend more than 90
minutes at the airport [40]. The Airport Commercial Revenue
Study (ACRS), the travel retail industry’s leading study
benchmarking the commercial performance of world airports,
finds that international intra-EU travellers spend an average of
approximately 99 minutes and domestic travellers in Europe
spend approximately 93 minutes at the airport [41, 42].
The data situation for long-distance rail transport is
significantly worse. There are no pan-European or pan-national
studies on the length of stay at the origin and destination
stations. Only studies for selected stations such as Nanjing
South Railway Station [43] or Linz [44] are documented.
However, neither of these studies made a distinction between
long-distance and local travellers.
Most current research (e.g [45, 46].) references the work of
Cokasova from 2003 which determined the stay durations at
the origin and destination of long-distance transportation for
both rail and air travel [47]. The research determined an
average stay duration of 60 minutes at the origin airport and 20
minutes at the destination airport. For rail traffic, it determined
a 10-minute stay at the origin station and assumed the time
required for transferring to local transport at the destination
station to be 0 minutes.
However, the ACRS findings and more recent transport
modelling indicates that Cokasova’s time estimates probably
significantly underestimate actual waiting times. The DATA-
SET2050 project of the Horizon 2020 research programme
modelled door-to-door travel times for air travel between the
200 major European airports. The average stay at the origin
airport before departure was 114 minutes. The model also
showed that it takes the traveller 31 minutes after arrival to get
from the aircraft to the means of local access transport [35].
3.3. Travel Time Analysis Confirms the Importance of the
Dwell Time Factor
The travel time survey application data collected as part of
this study confirms the assumption that actual travel behaviour
leads to significantly longer buffer times for air transport than
previous assumptions in the scientific literature or current
travel information systems.
As shown in Table 2, for all trips the average stay at the
airport before departure was just under 118 minutes and the
average time spent after landing was 39 minutes. These values
remain relatively stable if the sample is reduced to trips with
flight times of less than three hours (113/41 minutes) or even
less than 90 minutes (112/42 minutes). Business travellers have
shorter stays, but these are still well above Cokasova's
assumptions and the calculation bases in the travel planning
information systems. The biggest time difference between
business and private trips is at the destination airport and is
likely due to business travellers carrying only hand luggage
and choosing seats in the front of the aircraft.
In contrast, there are almost no differences in the length of
stay for business and private travellers at railway stations. As
shown in Table 3, passengers spend an average of about 20
minutes before, and 12 minutes after the train journey in the
station. These empirical results also clearly exceed the frequen-
tly quoted assumptions of Cokasova. However, the differences
from assumptions in the travel planning information systems
are significantly less than for air travel.
4. DISCUSSION
This study is the first worldwide to present an app-
supported determination of travel segment duration for all
segments of door-to-door long-distance journeys using the
same methodology for rail and air travel. While travel segment
durations were determined in previous studies, this was usually
done via ex-post surveys rather than using real-time travel-
accompanying surveys supported by software. This helped
improve data quality by reducing errors typical of retrospective
interviews such as false perceptions of time or statements of
social desirability. Against this background, the quality of the
survey results is rated as very high compared to previous time
recordings of travel segments.
A Level Playing Field for Comparing The Open Transportation Journal, 2019, Volume 13 55
However, the meaningfulness of the average dwell time
values identified in this study is limited by the small sample
size and the limitation to analyses of flights within or from/to
Europe. The measured dwell times show a high standard
deviation. This suggests that individual user travel experience
has a strong influence on travel planning and the integration of
time buffers of different lengths into travel planning. In air
travel, the transport of baggage, in particular, is likely to have a
considerable effect on the time spent at the airport. However,
since the baggage carrying feature has not been recorded
separately, it is unfortunately not possible to verify the corres-
ponding theses.
The comparison of the online travel planning applications
with regard to their included travel segments and time require-
ment assumptions has not yet been carried out in this form in
any publication. Here the research shows the clear differences
in travel time calculation methods. However, the development
of these online travel planning tools is highly dynamic. The
research therefore only provides a snapshot of the current
situation. Moreover, the selection of travel planning tools only
allows statements for the European market. However, the lack
of airport-specific differentiation and individual adaptability of
the Kerb-to-Gate and Gate-to-Kerb segments in all tools
evaluated shows the continuing underestimation of the impor-
tance of these travel stages and makes it difficult for these tools
to present a fair comparison of rail versus air travel times for
long distance journeys.
CONCLUSION
The study results show that current travel planning
applications distort actual door-to-door travel times. More
specifically, they significantly underestimate travel times by air
for two reasons. First, they do not include realistic waiting
times at the origin and destination airports. Second, most
applications do not include local access and egress times to
airports but do include them for rail.
The study points to several areas for more research. First,
the finding that waiting times at airports are underestimated is
based on relatively small sample size and therefore a more
extensive data collection and analysis should be performed.
Future research should also consider to what degree passengers
perceive time spent waiting at the airport or railway station
positively. It is possible that people use the time constructively
by shopping, eating or working. Another topic to consider is
how the size of airports and train stations impacts the accuracy
of travel time information. Second, the evaluation of travel
planning applications shows a very strong need for improving
the comparability of multimodal trips.
High-speed rail operators should be particularly interested
in making the door-to-door travel time differences between air
and rail travel clearly visible to travellers since these diffe-
rences help justify the major investments needed for high-
speed rail. In this respect, high-speed rail operators should
support more research on time spent at airports and railway
stations, and the development of improved multimodal travel
time comparison portals that better capture door-to-door travel
times.
Studies have repeatedly confirmed the dominant impor-
tance of travel time for the choice of transport mode. In view of
the fact that process times at airports have been significantly
underestimated in all the internet-based multimodal compari-
son portals evaluated in this research, there is considerable
potential for growth in railway trips by providing users with
more accurate door-to-door travel time comparisons. These
corrections should lead to an improvement in the relative
competitiveness of rail and would help justify the large invest-
ments made in European railway infrastructure.
CONSENT FOR PUBLICATION
Not applicable.
AVAILABILITY OF DATA AND MATERIALS
The data supporting the findings of the article is available
in the figshare repository at https://figshare.com/, reference
number 6400832
(https://doi.org/10.6084/m9.figshare.6400832).
FUNDING
Our research project was funded by the International Union
of Railways (UIC)
CONFLICT OF INTEREST
The authors confirm that this article content has no conflict
of interest.
ACKNOWLEDGEMENTS
The submission builds on the results of the project “Real
Travel Times Scoping Survey 2017. Door-to-Door analysis for
long-distance rail and air travel” that was supported by the
International Union of Railways (UIC). The authors express
their thanks to the UIC Intercity & Highspeed Committee for
all the support. Special thanks to our colleague Andrew Nash
for his valuable feedback and suggestions on previous drafts of
the paper.
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In recent years, renewable energy resources have become more important due to the limited number of regions for production of petroleum-based fuels, which are continuously depleting. The aviation sector in terms of commercial and cargo transportation has an increasing need for conventional, as well as, alternative fuels. Derivatives of petroleum fuels used in aviation have negative impacts on air quality. Factors causing greenhouse gas emissions (GHG) in the aviation sector must be reduced. However, such fuels used in the aviation sector are not sustainable. Biofuels which have the potential to replace petroleum fuels and help with emissions are heavily investigated in developed countries for independency, creating a better environment and sustainability. Biofuels which are already used for ground vehicles could also be implemented in the aviation sector to reduce fuel cost and emissions. Overall, aviation fuels made of sustainable resources would also support social and economic development. Numerous industrial initiatives have emerged to find alternative ways to attain bio-aviation fuels. Therefore, there is an increasing level of research with regards to alternative aviation fuels made of biomass in recent years. It is important to obtain basic feedstocks and to develop biofuel production processes in a cost-effective way. This study examines the necessity and the types of biofuels in the aviation sector. By designing unique fuel systems for air vehicles, it is possible to formulate biofuels which can be used for both air and ground vehicle applications. This type of consensus would help with sustainability and a better environment.
Conference Paper
The rapid development of China urbanization and smart transportation has promoted the optimized environment design research of Chinese railway station, while earlier rapidity construction and changing commerce operation based on original railway station have posed various contradictions of function, business and culture. This paper aims at proposing optimized environment design solutions based on pedestrian simulation as exemplified Nanjing South Railway Station, which is the largest railway junction station in Asia. In this paper, simulation experiment of passenger traffic is implemented to diagnose environmental problems, which is based on passengers’ behavioral patterns. Then, five kinds of optimized design solutions are proposed pointedly according to quantized conclusion of the simulation. In addition, a special ‘Optimized Simulation Sample’ System (a.k.a. OSS) of passenger traffic is established, for evaluating whether expected effects have been realized by using the framework of AnyLogic 7.0. In the end, the optimized environment designing is implemented based on pedestrian simulation. In this way, the existing environment problems of railway station can be identified precisely and optimized design solutions can be more effective.
Article
The inauguration of the Shinkansen high‐speed train service between Tokyo and Osaka, Japan, at 210 kph maximum operating speed some 40 years ago marked the comeback of the train as an important passenger mode of transport. Since then high‐speed train (HST) services have been introduced in many countries and are planned in many more, and the train has once more become the dominant mode of transport on many routes. This review summarizes the different elements of HST operation with the aim of characterizing HST operation and putting in context its impact in terms of what it is best designed for and what it can deliver. The review concludes that the HST is best designed to substitute conventional railway services on routes where much higher capacity is required and to reduce travel time, further improving the railway service, also against other modes, therefore leading to mode substitution. However, the high investment in HST infrastructure could not be justified based on its economic development benefits since these are not certain. Finally, the following definition for HST services is suggested: high capacity and frequency railway services achieving an average speed of over 200 kph.