Document [original]

Arnz European Transport Research Review (2022) 14:44

https://doi.org/10.1186/s12544-022-00568-9

ORIGINAL PAPER

The demand-side mitigation gap inGerman

passenger transport

Marlin Arnz1,2*

Abstract

Deep transport decarbonisation requires not only technological measures, but also large-scale changes towards

sustainable mobility behaviour. Researchers and decision-makers need suitable tools for corresponding strategy

development on a macroscopic scale. Aiming at broad accessibility to such methods, this paper presents an open

source passenger transport model for policy analysis in German medium- to long-distance transport. It discusses

model design and data, limitations, alternative approaches, and its base year results and concludes, that macroscopic

transport modelling is very suitable for policy analysis on national scales. Alternative approaches promise more insight

on smaller scales. As an exemplary case study, the model is applied to ambitious technology projections for the year

2035, showing the ambition gap towards reaching the 1.5 degree-target of the Paris Agreements. Results indicate that

66 million tCO

2eq

per year must be mitigated through further technological substitution or demand-side mitigation

strategies.

Keywords: Transport modelling, Mobility behaviour, Emissions reduction, Discrete choice, Transport decarbonisation,

Network accessibility

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1 Introduction

Fast transport sector decarbonisation is deemed difficult

yet crucial for climate change mitigation in alignment

with the Paris Agreements [24]. Since time until reach-

ing an average global temperature increase of 1.5 degree

Celsius is limited, unleashing full transport mitigation

potential requires not only technological measures, like

fuel switches and new propulsion technologies, but also

large-scale behavioural changes towards sustainable

mobility [64]. These different mitigation strategies are

often referred to as avoid, shift, improve: Avoiding the

need for traffic, shifting traffic to more environmentally

friendly modes, and improving vehicle technologies [40].

Avoid and shift measures are especially effective and low-

cost in the long term [24] and promise high increases of

well-being as co-benefits to (GHG) emissions reduction

[25].

Yet, quantitative analysis of the role of sufficiency

in swift transport mitigation is comparably rare: Only

one third of the measures analysed in national trans-

port mitigation studies address the demand side, which

makes policy strategy comparison uncertain [35]. The

same picture occurs for measures included into national

climate action plans in nationally determined contri-

butions [34], which highlights policy relevance of this

research field. On a global scale, some integrated assess-

ment studies advanced methods for the depiction of

demand-side action [see reviews from 30, 76]. On local

scales, Creutzig [22] finds that transport modelling yields

the most realistic representation of behaviour. Transport

modellers increasingly use their tools for long-term sce-

narios towards emissions mitigation and sustainability

[10]. However, large-scale models are usually proprietary,

making it difficult for new ideas to enter the field [50].

This paper presents quetzal_germany, an open source,

macroscopic passenger transport model for Germany. It

Open Access

European Transpor

esea

h R

*Correspondence: maa@wip.tu-berlin.de

1 Workgroup for Infrastructure Policy, Technical University Berlin, Berlin,

Germany

Full list of author information is available at the end of the article

Page 2 of 13

Arnz European Transport Research Review (2022) 14:44

explores the suitability of macroscopic transport model-

ling for nation-wide analysis of demand-side mitigation

pathways. In the following, Sect. 2 gives a brief over-

view of transport system analysis, corresponding meth-

odological requirements, national transport modelling in

practice, and starting points for open source approaches.

Section 3 presents quetzal_germany’s structure and

method. Its base year results are discussed in Sect.4, fol-

lowed by a critical review of its capabilities and norma-

tive assumptions. Section5 gives an exemplary outlook

into the year 2035 to quantify the ambition gap towards

reaching the 1.5 degree-target of the Paris Agreements.

Section6 concludes.

2 Background ontransport system analysis

2.1 Classification andrequirements ofmodels

Transport system analysis is naturally complex because

it involves a large number of heterogeneous decision-

makers with difficult to predict behaviour on the demand

side, as well as different temporal layers at the supply side

and the built environment. Figure1 outlines short-term

interactions between supply and demand side, as well as

long-term impacts of external effects on decision mak-

ing, land use, and transport supply. Allsop [3] defines two

main purposes of transport analysis: estimating features

and use of existing transport systems that are difficult

to observe; and estimating them in circumstances that

do not yet exist. The first purpose describes the work of

classic transport economists, while the second is particu-

larly interesting for comprehensive emissions mitigation

scenarios.

Transport analysis applications must meet a number of

requirements: On the demand side they need (1) a proba-

bilistic distribution of travel demand over space and time,

(2) variation of demand depending on perceived travel

cost or its benefit, and (3) differences among travellers

in perception of cost or benefit and its variation over

time. Supply-side requirements comprise (4) different

classes of vehicles or modes in the network (private and

public), (5) flow-dependent link cost, and (6) options for

traffic management [3]. Accurate depiction of the trans-

port sector in an energy system context further requires

(7) choice of not making a trip (physically), (8) vehicle

ownership and drive train technologies, (9) private vehi-

cle use patterns, and (10) infrastructure investment deci-

sions [4, 28, 52, 63, 67, 76].

While techno-economic energy models usually fail

to represent behavioural aspects of above [46], trans-

port modelling has been a central tool for simulation

of mobility behaviour since the late 1950s [17]. There

are two major approaches: activity-based (micro-) and

aggregated (macro-) modelling. Micro-modelling is the

younger field of research and utilises agent-based model-

ling techniques with rich sets of dependent variables and

usually involves high spatial and temporal resolutions

[8, 69]. Macro-modelling, on the other hand, follows the

classical four steps of transport modelling (Fig.2) and

simulates travel between aggregated demand zones for

aggregated demand segments (e.g. trip purposes or pop-

ulation groups) at the desired level of detail.

Transport models with national scope are particularly

interesting for the analysis of large-scale policies within

the transport sector and beyond. Even though publica-

tions concerning design of large-scale transport models

are rare in scientific literature, they shed light on many

non-trivial considerations. The Danish [59], Italian [12],

Norwegian [56], Swedish [13], UK [27], and Dutch [42]

national transport models have aggregated designs with

inter-connected demand and supply modules. All of

them are Logit models, based on discrete choice theory.

Fig. 1 A multi-perspective framework for transport analysis [based

on 3, 53]

Fig. 2 Classical four steps of macroscopic transport modelling

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Arnz European Transport Research Review (2022) 14:44

The four latter models have separate modules for short-

and long-distance travel to ensure high levels of detail

and high computational performance. This differentia-

tion further helps to accurately depict the impact of few,

but long trips on the overall traffic system [60]. Similarly,

the German national DEMO model divides passenger

travel into distances at a threshold of 100 km, which

allows simulation of mobility choices at a resolution of

6,561 zones [73].

2.2 Open source modelling

Availability of models and corresponding data is cru-

cial in order to support—and often enable—quantita-

tive analysis of sufficiency measures in transport. Open

source modelling and open data is desirable as it pro-

motes barrier-free co-development of new perspectives

and approaches of complex problem solving. It helps

reducing parallel efforts in maintaining large code bases

and data sets, allowing researchers to collaborate effi-

ciently on shared problems [54]. Additionally, closed

source modelling often lacks options to integrate into

other simulation or optimisation tools, which is deemed

important for thorough decarbonisation pathway analy-

sis [46].

Still, there are no open source transport models on

national scales to the author’s knowledge. In many coun-

tries, an underlying reason can be lack of required data

sources (that are openly licensed). For many practicion-

ers however, required open source software has high

entry barriers, such as a poor overview of appropriate

solutions, steep learning curves, and lack of an estab-

lished community. Until today, proprietary software

largely dominates transport modelling [50]. While there

are many frameworks for micro-modelling approaches,

the only open source software for macroscopic trans-

port modelling is Quetzal [20]. It implements methods

of the classical four-step model and beyond, allows full

demand-supply interaction, spatially explicit network

representation, full flexibility in demand group segmen-

tation, and is highly modular due to its implementation

in Python.

3 Introduction ofquetzal_germany

quetzal_germany is an aggregated transport model (see

Fig.2) for medium- and long-distance passenger travel

within the area of Germany. It is divided into 2225 zones,

simulating traffic in between them. They are defined by

clustering 4605 municipality unions to similar zone sizes.

If computational power is limited, the zoning system

can easily be reduced to 401 NUTS3-level zones. Inner-

zonal traffic is computed from other data sources, mak-

ing local and urban mobility an exogenous element (see

Sect.3.3). The model is developed in Python under use of

the Quetzal open source transport modelling suite [20].

It is openly available on github (see section “Availability

of data and materials”).

Trip generation and distribution (steps one and two

in Fig.2) are currently covered by an exogenous (OD)

matrix from the Federal Transport Infrastructure Plan

2030 (VP2030) [61]. Transport demand of the whole

population, linearly interpolated between 2010 and 2030

to the base year 2017, is divided into twelve demand seg-

ments, corresponding to the national mobility survey

(MiD2017): commuting, business, education, grocery

shopping or medical executions, leisure, and accompa-

nying trips; each trip purpose further divided into car

availability in the household. Mode choice is designed

as a Multinomial Logit model for each of these segments

with land and air transport alternatives. Logit models and

random utility maximisation are by far the most common

and best understood applications in discrete choice anal-

ysis [11, 19, 53]. Section3.2 describes the choice model

specification and its variables in detail.

The network model for Germany with measurable

(LoS) attributes is described in Sect.3.1. Both road and

public transport use the Dijkstra algorithm to find short-

est paths in terms of travel time. Demand-supply equili-

bration is implemented as iterative convergence between

the equilibrium road traffic assignment, using the Frank-

Wolfe algorithm [33], and the Logit modelling step. In a

subsequent step, quetzal_germany calculates emissions

from transport activities as described in Sect.3.4.

3.1 Network model andlevel‑of‑service attributes

quetzal_germany includes a highly detailed network

model based on OpenStreetMap data for (MIT) and

GTFS feeds for (PT). The latter is aggregated to the most

relevant services for inter-zonal travel, using agglomera-

tive clustering and filtering methods, in order to increase

computational performance for the German-wide model.

As a blueprint for regional studies however, the whole

network graph can be selected. There are seven different

network layers for corresponding transport modes:

1 Long-distance rail transport: ICE, IC and EC rail ser-

vices

2 Short/medium-distance rail transport: Local and

regional rail services

3 Local public transport: Bus, ferry, tram and under-

ground services

4 Coach transport: Connections based on FlixBus’ net-

work coverage

5 Air transport: Connections between 22 major air-

ports

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Arnz European Transport Research Review (2022) 14:44

6 Road: Motorways, A and B roads, as well as intercon-

necting links

7 Non-motorised transport: Straight-line connections

between zone centroids with distances up to 40 km

All relevant PT interconnections are realised through

footpaths between stops of different layers. Network

access/egress links connect each layer to sources and

sinks of transport demand in the population centroid

of each zone. As measures of LoS, every network link is

equipped with two attributes: travel time (Eq. (1)) and

monetary travel cost (Eq. (5); Table2).

In-vehicle time

Tiv

results from the network graphs. Road

network average speeds are calculated from OpenStreet-

Map speed limits and conversion factors from [48]. PT

link duration stems from real GTFS schedule data. Wait-

ing time

Twait

applies as zero for car transport and as the

average waiting time at PT stops based on vehicle head-

ways of the respective route. Entering an airplane costs

45 minutes including security checks, luggage handling,

boarding, and longer walking distances within airports.

Delay times of any kind are currently neglected. Walk-

ing time

Twalk

accrues for PT intermodal transfers (at

5 km/h) or cycling connections between centroids (at

17km/h).

Tae

is the average access/egress time and repre-

sents a measure of accessibility. It is constant five minutes

for MIT, depicting access, starting, and parking, while PT

accessibility depends on the corresponding zone’s and

network’s characteristics. Expression (2) calculates PT

Tae

z,j

for mode j and zone z, inspired by a two-step floating

catchment technique presented in [49]:

The mean of weighted distance

dm,n

over all PT stops (i.e.

nodes) n in

Nz,j

is again, weighted by share

ηm,uz

of PT

access/egress mode m in

M={walk, bicycle, car}

. Values

for

ηm,uz

depend on the zone’s urbanisation degree

and

can be found together with speed variable

αm

in Table1.

Distance measure

dm,n

is based on the geodesic distance

Dn,c

from node n to population cell c (at a resolution of

100 ×100

m).

dm,n

is weighted by the number of PT vehi-

cles that depart from this stop between 6 a.m. and 6 p.m.

during the week

and by the population weight measure

m,n

(1)

TT =Tiv +Twait +Tae +Twalk

(2)

Tae

z,j=

m∈M

ηm,uz·dm,n·αm∀n∈Nz,

(3)

m,n=

c∈z



n′∈Nz,j

n′

·D

n′,c



n′′ ∈N

z,j

sn′′

Each access/egress mode has a catchment area defined

dmax,m

, wherein the cell population

is counted and

linearly weighted by its distance to node n. This double

weighting makes population counts close to a node more

relevant than distant ones, or, from the perspective of PT

users, closer nodes more attractive. It also reduces the

impact of distance thresholds choice for access/egress

modes. As a result,

dm,n

yields realistic average distances

relative to population density and service frequency of

stops. Access/egress mode parameters can be varied in

scenario settings as an approximation to inner-zonal

mobility choices.

Travel cost TC is composed of distance-specific cost

in EUR/km, in-vehicle time specific cost

in EUR/h, fix

cost

cfix

in EUR per trip, and a split factor f, used for car

occupancy rates or average shares of PT subscriptions in

the population. Sunk costs, like car ownership cost or PT

subscriptions, are not included. Empirical evidence fre-

quently shows, that individuals usually do not account

them in daily mode choice [e.g. 5]. Table2 summarises all

cost function parameters for the base year except for local

PT. Pricing schemes are very diverse within Germany so

that the following assumptions apply: Unimodal bus trips

cost 7 EUR. They reduce to 5 EUR, if origin or destina-

tion is a city, because cities are centers of price zoning

systems and there is a higher share of subscriptions in the

population. If bus transport occurs on the first or last leg

of a multimodal trip, half these cost accrue, respectively.

Travellers decide upon their route and mode based on

a set of shortest paths between their origin and destina-

tion. The Dijkstra algorithm computes shortest paths

for car and bicycle transport, and for every PT mode

(4)

m,n=cDn,c·Pc·



Dn,c

dmax,m





cPc·



Dn,c

max,



∀c∈Dn,c≤dmax,

(5)

=D·cd+T

·ct+cfix

Table 1 Values for access/egress link parametrisation

values are derived from the calibration data set

Variable Walk Bicycle Car

0.948 0.017 0.035

0.899 0.034 0.067

0.883 0.026 0.091

in km/h 5 17 30

dmax

in km 0.4 10 30

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Arnz European Transport Research Review (2022) 14:44

combination available. The main leg’s transport mode

represents the path’s main mode, which is the decision

variable in the mode choice model.

3.2 Mode choice model specification andcalibration

Scope, explanatory power, and policy analysis suit-

ability of the demand-side model depend on the attrib-

utes included and how they apply for different demand

groups. Witte etal. [75] show that travel time and price

are the most frequently used LoS attributes across mul-

tiple disciplines, while others, such as car availabil-

ity or income, have a higher significance. All national

transport models shown in Sect.2 use time and price

as mode choice variables, and so does quetzal_ger-

many. Moreover, PT mode accessibility and frequency

is included through

Tae

in TT, while demand segmenta-

tion includes car availability. Other individual or social

attributes are neglected due to limited data availability.

For distance-dependent cost factors, many transport

studies find non-linear marginal utilities, i.e. decreas-

ing cost sensitivity over time [26]. Modellers com-

monly encounter this issue by so-called “cost-damping”

mechanisms like the Box-Cox transformation [16] to

generate realistic elasticities of demand [58]. It is also

common practice to aggregate time and price into a

generalised cost term GC, using exogenous value of

time (retrieved by mode, purpose and distance from

[7]), in order to decrease model complexity [53]. Given

the available mode choice variables, four different util-

ity formulations V for alternative j, with (ASCs) and

marginal utility parameters

, were tested:

1 Box-Cox transformation of GC with

ˆτ

fitted to the

calibration data:

j=ASCj+βGC

ˆτ

j−

ˆτ

2 log-power transformation of GC:

ASCj+

·log



j3

3 log-power spline of GC from [57] with knot points

corresponding to a mean GC at distances of 20km

and 60km:

=ASC



β,GC

j

4 log-power spline of TT with knot points at 1h

and 3h plus linear perception of TC:

ASCj+

F

,TT

j

+β

·TC

The German mobility survey MiD2017 is a revealed pref-

erence, repeated, and cross-sectional survey with the

same zonal resolution as quetzal_germany. Out of 417,094

inner-German trips with observed origin, destination,

mode, and purpose, 134,637 inter-zonal trips serve as

calibration data set. Prices are calculated using the same

assumptions as described in Sect.3.1, because MiD2017

does not report them. OD distance comes from the net-

work model’s shortest paths, because 17% of stated dis-

tances don’t fit the survey’s routed distances. Travel times

from the shortest paths are mapped to the observations

so that the mode combination and route with a travel

time closest to the stated time applies. The choice set is

defined as

M={rail, road PT, air, MIT, non-motorised}

Corresponding modes of the network model are aggre-

gated because responds in MiD2017 do not differentiate

among short-distance and long-distance rail or road PT

accurately.

All of above’s models can be estimated with this data set

using Maximum Likelihood Estimation in the Biogeme

software [14]. Due to the aggregation of the choice set, a

hierarchical model always collapses into a Multinomial

Logit model. In terms of final log-Likelihood, the Box-Cox

Table 2 Monetary cost function components by mode of transport in 2017

Mode CdCtCfix fMin Max

Rail short 0.233 0 1.47 2 5 50

Linear regression of DB price list 2nd class; subscription shares from calibration data set

Rail long 0.053 7.33 15.56 1 19 139

Linear regression with 56 OD-specific prices from DB website in Jan. 2021; 30% savings tariff

Coach 0.057 0 0 1 5 60

Average coach prices in Germany

Airplane 0 0 OD-specific 1 50 -

Economy prices from Sept. 2020 where available; 50 EUR elsewhere

MIT 0.114 0 0 1.5 - -

Average fuel cost for 2017’s new car models with mileage of 15,000 km/a; average car occupancy in Germany

Non-motorised 0 0 0 1 - -

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Arnz European Transport Research Review (2022) 14:44

transformation performs worst, followed with similar log-

Likelihoods by the log-power transformation and the GC-

log-power spline. The log-power spline of TT with linear

perception of TC performs best with a difference in final

log-Likelihood of 65 (which is reasonable). The linear-in-the-

parameters model does not produce significant results at all.

These results imply, that the difference in perception of time

and price cannot be captured by exogenous values of time

sufficiently. Hence, the mode choice model is specified as

for demand segment i with a log-power spline function

as proposed in [57]:

where

q

is a binary parameter such that

q(x)

⇔

x∈



−1,c

q

and zero elsewhere. The spline has a

number of

Q=3

knot points

with

c0=0

and

cQ=∞

defining the cost intervals at which different log-power

expressions operate. The Rich spline function con-

forms with random utility theory for

β<0

(see [57] for

proof). Iterative adjustment of knot points

and

yields

approximated optimal knot points for each segment’s

model.

All

values were found significant on a 1% confidence

interval (see section “Availability of data and materials”).

Estimation results show that travel price has no impact on

mode decisions for business trips, whereas price sensitivity

for commuting trips is double the average. Moreover, com-

muting and business trips have a larger time sensitivity on

longer distances (higher knot points), while education and

shopping trips become less sensitive earlier.

3.3 Inner‑zonal travel

Aggregated transport models cannot depict inner-zonal

travel by design. quetzal_germany’s zoning system explains

86.7% of total traffic endogenously, while local mobility

is approached as follows: Inner-zonal trip volumes come

from VP2030, segmented by the same demand segments

as above. MiD2017 data yields trip distances as means by

segment, mode, and the zone’s urbanisation degree, which

are relevant for (pkm) calculation. Travel time and prices

are calculated with the same formulas and assumptions as

(6)

j=ASCi

j+F



βi

t,TTj



+βi

c·TC

(7)

(β,x)=β



q=1

q(x)θqln(x)Q−q+1+αq(β)



θq=Q

Q−q+1



r=2

ln(cr−1)∀q=2, ...,Q

q(β)=αq−1(β)+(q−1)!β

Q−1lncq−1Q−q+2

q−2



r=1

ln(cr

)

for inter-zonal travel in order to allow subsequent transport

system evaluations.

3.4 Emissions calculation

GHG emissions are a relevant indicator for transport sys-

tem sustainability. Direct and indirect driving emissions

(well-to-wheel) can be calculated in a post-processing step,

whereas a full life-cycle analysis goes beyond the scope of

transport modelling. Calculation methods differ between

private and public transport: MIT emissions directly

depend on transport demand (formula8); PT emissions

depend on transport supply, which reacts to transport

demand only with delay.

Total MIT emissions are the product of vehicle kilome-

tres—as demand segment i-specific pkm times occu-

pation rates o from MiD2017—and distance-specific

emission factors. The latter is the weighted mean over

drive-train technologies d. In 2017, diesel cars have the

largest share (

γdiesel =

) 0.66 with real driving emissions of

173.6 gCO

2eq

/km; gasoline cars have a share of 0.33 and

emgasoline =

187.6 gCO

2eq

/km; the rest is dominated by

natural gas, which has emissions of 104 gCO

2eq

/km (data

from TREMOD based on HBEFA; see [2]).

Classic PT modes (i.e. no shared or pooled systems)

are scheduled services corresponding to prior demand

analysis or political decisions. Small mode share changes

might lead to increased vehicle loads under constant

emissions, whereas large-scale changes require adapta-

tion in the supply system. Most transport models do not

consider vehicle loads endogenously and assume propor-

tional increase of capacities [37]. So does quetzal_ger-

many, as it is designed for long-term scenarios. Hence, it

calculates GHG emissions using 2017 pkm-specific val-

ues from TREMOD (well-to-wheel; see [2]). This method

diverts from official numbers in rail transport, where

supply-chain emissions of electrified rail transport are

omitted to prevent double counting.

4 Base year results anddiscussion

4.1 Validation ofinter‑zonal mode shares

All the described modelling ambitions aim at a realis-

tic depiction of German passenger transport in the base

year 2017. But there is no data set available for valida-

tion of absolute inter-zonal model results except VP2030,

which is already used as input data. Hence, quetzal_ger-

many’s mode shares can be validated with relative figures

from MiD2017 by demand segments or all together (see

Fig.3. In this context, mode shares always refer to the

trip’s main mode. Summed over all demand segments,

(8)

MIT =



pkmioi·



γdem

Page 7 of 13

Arnz European Transport Research Review (2022) 14:44

quetzal_germany’s modal splits vary only slightly from

MiD2017 data (see Table3).

In quetzal_germany’s results, air travel is underes-

timated even though it accounts only for a very small

share of inner-German traffic. In the education segment,

rail transport takes over a big share from road PT; in all

other segments a small share of MIT. These inaccuracies

largely stem from unrealistic depiction of pricing systems

in rail transport. In quetzal_germany, prices mostly rely

on linear regression and crude assumptions (see Table2),

while real pricing mechanisms are fairly complex. Again,

a major barrier is the lack of open data for PT and air

prices. Another reason for higher rail shares lies in

the network connection: Travellers can choose freely

any major rail stop within origin or destination zone,

respectively, all having the same accessibility. The result

is lower average trip cost. Non-motorised travel, on the

other hand, is slightly underestimated, because trips are

assumed to happen between zone’s geometric centroids.

In reality however, people who are located close to the

destination zone’s border set out for most of these trips.

Except these inaccuracies, the mode choice model per-

forms well. Since there is no measured data for the entire

German medium- to long-distance passenger transport

system, above figures cannot validate quetzal_germany’s

results with certainty. Still, they suggest a realistic repre-

sentation of the 2017 transport system.

4.2 Total traffic andemissions

quetzal_germany accounts for inter-zonal traffic through

endogenous simulation (86.7% of total pkm), as well as

inner-zonal traffic through exogenous calculations (see

Sect.3.3). This allows subsequent computation of indi-

cators for policy effects, such as total pkm and GHG

emissions.

A relevant indicator for validity of results is the yearly

mileage of an average private vehicle: quetzal_germany

yields 15,618 km, which comes close to the official 14,290

km [2]. Table4 compares pkm results to figures from ViZ.

Total pkm of MIT might be overestimated in quetzal_

germany for three reasons: travellers are assumed to start

and end their journey at the zone’s geometric centroid,

which might not represent residential structures in real-

ity; inadequate representation of air travel (see above);

and inner-zonal travel is overestimated. This mainly

explains higher total pkm, too. Rail pkm divide between

long- and short-distance services. quetzal_germany

yields 13 and 76 bn. pkm respectively, while the German

Fig. 3 Modal splits for inter-zonal travel without air transport (left) and traffic distribution of car (red) and public transport (blue) (right)

Page 8 of 13

Arnz European Transport Research Review (2022) 14:44

national transport emissions model calculates 40 and 55

bn. pkm, respectively ([2]; figures are more reliable than

those of ViZ). Besides aforementioned pricing inaccura-

cies in long-distance rail transport, the difference in pkm

is explained by inaccurate network distances: quetzal_

germany uses air-distances between stations, while real

distances depend on the rail network’s curvature.

Table 5 shows, that MIT’s emissions are overesti-

mated to roughly the same extend as its pkm. While air

transport results are not adequately modelled in quet-

zal_germany, road PT is. The difference of rail transport

emissions is caused by different accounting methods, as

described in Sect.3.4.

4.3 Evaluation oftransport system analysis requirements

Requirements for transport system analysis, as col-

lected in Sect.2, make a tool more or less suitable for

its purpose. On the demand-side, a major limitation to

endogenous explanatory power of quetzal_germany is

the outplacement of choices related to physical mobil-

ity and trip destination. Currently, this is covered by

results from the German national modelling study,

which also covers inner-zonal travel volumes. Hence,

variation of demand on trip cost is limited to mode

choice between zones. Here, quetzal_germany differen-

tiates between travellers by trip purpose and car avail-

ability, which allows more detailed policy analysis and

evaluation of traffic flows on specific routes or means

of transport. However, trip cost is limited to travel time

and monetary cost. These are, in a macroscopic setting,

the most significant influence levers, while other LoS

attributes like service frequency and reliability trans-

late easily into time or willingness-to-pay. Still, fur-

ther research should look into utility formulations with

more LoS or idividual attributes.

The supply-side, i.e. the network model, has great spa-

tial detail and covers all modes of transport spatially

explicit. The reduction of temporal complexity by using

a PT headway model instead of minutely resolved itin-

eraries increases computational performance and allows

analysis of comprehensive PT-supply policies, which do

not have to be specified in regional detail. It makes the

implementation of a time-of-day choice redundant,

which is a common element of demand models and their

reaction to traffic. However, reduced temporal complex-

ity also diminishes the impact of traffic situations on

transport demand (i.e. supply-demand equilibration):

Road link capacities, which are usually critical during

rush hours, are rescaled and applied to yearly aggre-

gates. Further research should investigate time-expanded

demand modelling or appropriate computation of aggre-

gate road capacities based on OpenStreetMap data.

Depiction of passenger transport in an energy system

context requires more features than quetzal_germany—

and transport modelling in general—offers. Vehicle

ownership, drive train technologies, and infrastructure

investment are exogenous assumptions, that require

thorough consideration. quetzal_germany depicts indi-

vidual every-day mobility choices, which are influenced

by above’s factors. A feature not represented are individ-

ual car driving styles. Agent-based modelling approaches

can depict corresponding energy demand of MIT more

advanced, even though its applicability and data availabil-

ity on national scales are uncertain.

What is more, the open source model quetzal_germany

serves as a blueprint for other regions, where adminis-

trative borders, population density, and PT schedules

are openly available (applies for all EU countries). The

demand model further requires a mobility survey, which

is available in sufficient detail in most high-income

Table 3 Modal shares by main mode, segmented by trip purpose in percent

Validation data in italic font

The upper value represents quetzal_germany’s results, the lower is the survey average from MiD2017

Main mode Commuting Education Buy/execute Business Leisure Accompany All

MIT 84.6 43.9 91.8 90.2 86.5 96.8 86.1

88.0 40.4 94.2 91.5 90.1 98.2 89.2

Rail 11.8 26.8 5.3 7.5 9.0 2.3 9.3

8.2 21.4 3.1 6.4 5.8 1.0 6.0

Road PT 3.5 28.4 2.4 2.0 3.8 0.8 4.2

3.5 36.9 2.1 1.6 3.2 0.6 4.1

Air 0.00 0.00 0.00 0.10 0.02 0.00 0.02

0.00 0.00 0.01 0.26 0.05 0.00 0.03

Non-motor. 0.1 0.9 0.5 0.2 0.7 0.1 0.4

0.3 1.3 0.5 0.2 0.9 0.1 0.6

Segment share 26.6 4.3 25.7 5.7 31.4 6.3 100.0

Page 9 of 13

Arnz European Transport Research Review (2022) 14:44

countries. At least in Europe, demand model structure

adaption is not required because of the implemented

cost damping mechanism and similar mobility behaviour

across countries [32]. Due to its open design and full doc-

umentation, quetzal_germany may contribute towards

opening up macroscopic transport modelling and the

investigation of demand-side mitigation strategies in pas-

senger transport across Germany and beyond.

4.4 Discussion ofalternative methods

In general, micro-simulation is an attractive alternative

approach, because it can better capture population het-

erogeneity and other externalities of transport than GHG

emissions. Moreover, it would endogenise quetzal_ger-

many’s workaround for inner-zonal travel. However, data

requirements and computing times of these models tend

to be enormous, which drastically reduces their applica-

bility to large scales [71].

Yet, both approaches rest upon the same method:

Discrete choice modelling. It is based on random utility

theory, which draws from micro-economic utility maxi-

misation and rational choice, adding a probabilistic error

component. Random utility theory is the most elaborate

theoretical basis for analysis of discrete choice problems

[53]. It shows great flexibility with a simple mathematical

formulation at low computational complexity. Yet, it has

normative assumptions and limitations, which modellers

must reflect on.

In high-income countries, it is obvious and well-

researched, that mobility behaviour often deviates from

rational choice. An extensive review of reviews by [41]

supports that argument, finding strong correlations

between non-rational factors and low-carbon mode

adoption in urban contexts. Empirical evidence shows,

that the Theory of Planned Behaviour [1] or the Norm

Activation Model [62] perform well in describing pat-

terns of more sustainable mode choice [39]. Moreo-

ver, behavioural economics exhibit concepts, which can

enhance our understanding of mobility decisions and

corresponding sustainability-directed policies [6, 51].

Witte etal. [75] argue that Kaufmann’s mobility concept

[43] is the most promising framework for bridging eco-

nomic, social, cultural, and political aspects in mobility

research and build upon their own multi-disciplinary

framework. Finally, Creutzig [23] argues, that the liberal

world view connected to utility maximisation theory is

ill-suited to cope with global challenges we face today.

Logit modelling, however, has advanced in recent

decades. Mixed Logit models are state-of-the-art [21,

66], acknowledging taste variations within aggregated

demand groups and allowing for the inclusion of individ-

ual and social attributes. Another advancement are latent

choice models, capable of including individual attitudes

of mobility choices. Bahamonde-Birke etal. [9] extend

this further by differentiating between perceptions and

attitudes in order to “represent the decision making pro-

cess and the way in which the different variables take part

in it as accurately as possible”. However, Vij and Walker

[68] show, that most latent choice models have the same

explanatory power as the corresponding multinomial

logit model formulation. And still, they are based on

rational choice theory.

In practice, demand model formulations crucially

depend on data availability (surveys and socio-economic

details), while the price for data gathering strongly

increases with the size of the model region and its het-

erogeneity. Hence, large model regions often come with

rather simple Logit model specifications. This can well

Table 4 Total traffic (billion pkm) by mode for ViZ and quetzal_germany results (the upper value shows modelled results, the lower

value shows data generated from VP2030 and MiD2017)

Column NM comprises non-motorised trips

MIT Rail Road PT Air NM All

quetzal_germany Inter-zonal 985.3 75.8 37.1 1.5 0.0 1100

Inner-zonal 92.6 13.2 34.3 0.0 28.6 169

ViZ Total 950.4 95.7 81.4 10.4 55.3 1195

Table 5 Driving emissions (million tCO2eq) by mode from quetzal_germany results (the upper value shows modelled results, the lower

value shows data from VP2030 and MiD2017) and values retrieved from [15]

MIT Rail Road PT Air All

quetzal_germany Inter-zonal 116.5 3.8 2.7 0.3 119.2

Inner-zonal 10.9 0.7 2.5 0.0 18.3

BMU Total 98.2 1.0 NaN 1.9 NaN

Page 10 of 13

Arnz European Transport Research Review (2022) 14:44

be sufficient, when the level of detail in simulated decar-

bonisation strategy measures fits. As an example: While

at small model regions, individual perceptions within a

neighborhood might contribute great insight for policy

advise, national-level policies, like fuel taxation, do not

require more advanced model attributes than monetary

cost. Within the limitations of data availability, quet-

zal_germany can depict a broad set of transport policies

through price mechanisms and transport supply system

changes (travel time and network accessibility), seg-

mented by useful demand groups.

5 German passenger transport towardstheParis

Agreements

Germany can contribute to limiting global temperature

increase to 1.5 degree Celsius by becoming climate neu-

tral by 2035 across all sectors [see 65]. With current pol-

icy and technology pathways, transport emissions would

not be lower than 154 million tCO2eq in 2030 [38]. This

projection clearly fails the climate neutrality goal, even

though projections become more optimistic over time

due to the implementation of new policies and faster

technology deployment than expected. In quetzal_ger-

many’s base year 2017, passenger transport had a share

of 65 % of the transport sector’s GHG emissions [15].

This section’s outlook shows the GHG emissions gap that

would appear in 2035 without demand-side strategies for

passenger transport, i.e. no behavioural change in trans-

port demand except its linear increase proportional to

population growth.

5.1 Supply‑side development

In general and across all modes, transport supply remains

constant relative to transport demand: Capacities get

expanded proportional to traffic volume increase so that

congestion remains at 2017 levels. Future pricing follows

assumptions from VP2030 in order to be consistent with

trip generation and distribution, which is consistent with

[2] (see Table6). According to the authors, these projec-

tions exhibit a clear political intention to make transport

more environmentally friendly. Only aviation ticket prices

increase due to higher cost of synthetic fuels [see 18].

5.2 Technological development

Private vehicle technology development is heavily dis-

cussed in German society, industry, science, and politics.

Will Germany continue to be diesel-driven or switch to

EVs? Common techno-economic approaches to this

question [such as 36] neglect socio-technical aspects like

co-evolution, niche-regime interactions, and behavioural

change [see 47]. In a thorough robustness and uncer-

tainty analysis, Wanitschke [70] shows that battery EVs

are likely to claim a significant share of vehicle sales in the

medium term. However, production capacities will cap

their market penetration at least until 2030. After dia-

logues with automobile manufacturers, Windt and Arn-

hold [72] calculate a maximum stock of 14.8 million EVs

in 2030 (including plug-in hybrid EVs; corresponding to

32% of 2017’s private vehicle stock). Drees etal. [29] pro-

ject 15.1 million EVs in their progressive scenario, which

is in line with production capacities and assumptions of

this outlook. Future EV charging cost are highly contro-

versial, yet crucial for its competitiveness [70]. For sake

of simplicity, all EVs are assumed to be operated at cost of

battery-driven vehicles with today’s home charging price

of 0.3 EUR/kWh. The remaining vehicles are assumed to

have the same drive train shares and operating cost as in

2017, while efficiencies of combustion engines continue

to increase with 1.5% p.a. as a mean across fuel types

[61]. Car ownership rates are assumed to stay at 2017 lev-

els, as influencing variables (i.e. socio-economic house-

hold variables and PT coverage) do not change.

A deep-decarbonisation scenario as ambitious as this,

requires fossil-free electricity generation by 2035, which

yields zero driving emissions for all electric drive trains.

The German rail operator “Deutsche Bahn” already

announced climate neutrality in 2038, even earlier in pas-

senger transport. Hence, rail transport is assumed emis-

sions free, here. The same assumption applies to road

PT, though mainly driven by the European Union’s clean

vehicle directive. Air transport technology displays the

least robust pathways and faces high technological bar-

riers towards full decarbonisation. Hence, 50% of its fuels

are assumed to be climate neutral (i.e. biofuels or syn-

thetic fuels), while technologies remain unchanged.

5.3 Emissions in2035

Above’s assumptions yield modal shares very similar to

those of 2017. Higher vehicle efficiencies of private cars

decrease driving cost per kilometre, which produces a

mode shift of 0.5% towards MIT on medium to long dis-

tances, taking equally from rail and road PT. Total traffic

Table 6 Yearly user cost increase between 2017 and 2035,

following assumptions from [2, 61]

Price

development

[% p.a.]

MIT, combustion engines 0.5

MIT parking cost 2.0

Road PT 1.0

Rail 0.5

Air 0.5

Page 11 of 13

Arnz European Transport Research Review (2022) 14:44

increases to 1,407 billion pkm (7% more than in 2017).

Through technological development, passenger transport

emissions decrease to 65.5 mio. tCO

2eq

, which is a reduc-

tion of 45% compared to 2017, but still far from climate

neutrality.

Several national research projects have investigated

pathways for (passenger) transport decarbonisation in

the past: Transport and Environment analyses three

explorative bottom-up scenarios until 2040, of which the

reference scenario yields 45% emissions reduction com-

pared to 2010, mainly through improve strategies [31,

74]. With similar assumptions and within the same time

horizon, the GreenLate scenario in the RESCUE study

achieves a 46% emissions reduction [55]. The technol-

ogy pathway report of the Ariadne study concludes that

a technology shift alone does not contribute enough to

short-term (2030) decarbonisation goals and further

demand-side mitigation measures are needed [45].

Full decarbonisation of the German energy system by

2035 is challenging [see 44] and this exemplary scenario

already includes progressive technological assumptions.

There are natural barriers towards technology deploy-

ment, which are usually considered in energy system

modelling [e.g. 55]. Avoid and shift strategies would sup-

port decarbonisation without challenging technological

boundary conditions such as renewable fuel imports,

electricity generation capacities, production chain ramp-

up, or resource availability.

6 Conclusion andoutlook

Unleashing full transport decarbonisation potential is

crucial for reaching the 1.5 degree-target of the Paris

Agreements. This paper’s outlook for Germany’s passen-

ger transport emissions shows that even in an ambitious

technology scenario, there remains a large emissions gap.

It can be bridged by further technological ambition, i.e.

improve strategies, within uncertain boundary condi-

tions. Avoid and shift strategies, on the other hand, do

not violate these boundary conditions, but affect society’s

well-being. How much they can contribute, and which

effect they would have, remains open.

quetzal_germany, as presented here, can be used to

analyse strategies to fill this knowledge gap. It realistically

depicts mode choice behaviour on medium- to long-

distance travel in Germany and exhibits useful levers for

national policy analysis. Future development ambition

should direct towards endogenous depiction of trip gen-

eration and distribution to make it standalone and aid as

an open source blueprint for other countries or regions.

The discussion of quetzal_germany’s properties shows:

Macroscopic transport modelling is a suitable tool for

large-scale transport demand-side analysis and should

be used to support deep decarbonisation scenarios in

techno-economic modelling.

Abbreviations

ASC: Alternative-specific constant; EV: Electric vehicle; GHG: Greenhouse gas;

LoS: Level-of-service; MiD2017: “Mobility in Germany 2017”; MIT: Motorised

individual transport; OD: Origin destination; pkm: Passenger kilometre; PT:

Public transport; VoT: Value of time; ViZ: “Transport in figures”; VP2030: “Forecast

of transport interconnectivity 2030”.

Acknowledgements

I am very thankful for the support of my supervisors Christian von

Hirschhausen and Philipp Blechinger. I would also like to thank Quentin Chas-

serieau for his ongoing support.

Author contributions

The author read and approved the final manuscript.

Funding

Open Access funding enabled and organized by Projekt DEAL. This work was

funded by the Reiner Lemoine Foundation.

Availability of data and materials

The open source transport model quetzal_germany can be found in form of

Jupyter Notebooks on github: https:// github. com/ marli narnz/ quetz al_ germa

ny; Archived version: https:// zenodo. org/ badge/ lates tdoi/ 34040 6296; License:

MIT; Input data availability: The input data set supporting this repository is

available on Zenodo: https:// doi. org/ 10. 5281/ zenodo. 56596 79. The traffic

assignment step and a new calibration of the mode choice model require

access to data from the Federal Ministry of Transport.

Author details

1 Workgroup for Infrastructure Policy, Technical University Berlin, Berlin,

Germany. 2 Graduate School Energie System Wende, Reiner Lemoine Institut,

Berlin, Germany.

Received: 9 February 2022 Accepted: 20 September 2022

References

1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior

and Human Decision Processes, 50(2), 179–211. https:// doi. org/ 10. 1016/

0749- 5978(91) 90020-t

2. Allekotte, M., Biemann, K., Heidt, C., Colson, M., & Knörr, W. (2020). Aktu-

alisierung der modelle tremod/tremod-mm für die emissionsberichter-

stattung 2020 (berichtsperiode 1990–2018). Tech. Rep. TEXTE 116/2020,

Umweltbundesamt.

3. Allsop, R. E. (2008). Transport networks and their use: How real can mod-

elling get? Philosophical Transactions of the Royal Society A: Mathematical,

Physical and Engineering Sciences, 366(1872), 1879–1892. https:// doi. org/

10. 1098/ rsta. 2008. 0013.

4. Anable, J., Brand, C., Tran, M., & Eyre, N. (2012). Modelling transport energy

demand: A socio-technical approach. Energy Policy, 41, 125–138. https://

doi. org/ 10. 1016/j. enpol. 2010. 08. 020

5. Andor, M. A., Gerster, A., Gillingham, K. T., & Horvath, M. (2020). Running

a car costs much more than people think: Stalling the uptake of green

travel. Nature, 580, 453–455.

6. Avineri, E. (2012). On the use and potential of behavioural economics

from the perspective of transport and climate change. Journal of Trans-

port Geography, 24, 512–521. https:// doi. org/ 10. 1016/j. jtran geo. 2012. 03.

003

7. Axhausen, K., Ehreke, I., Glemser, A., Hess, S., Jodden, C., Nagel, K., Sauer, A.,

& Weis, C. (2015). Ermittlung von bewertungsansätzen für reisezeiten und

zuverlässigkeit auf der basis eines modells für modale verlagerungen im

nicht-gewerblichen und gewerblichen personenverkehr für die bundes-

verkehrswegeplanung. Tech. rep., TNS Infratest and IVT, ETH Zürich.

Page 12 of 13

Arnz European Transport Research Review (2022) 14:44

8. Axhausen, K. W., & Gärling, T. (1992). Activity-based approaches to travel

analysis: Conceptual frameworks, models, and research problems. Trans-

port Reviews, 12(4), 323–341. https:// doi. org/ 10. 1080/ 01441 64920 87168

26.

9. Bahamonde-Birke, F. J., Kunert, U., Link, H., & Ortúzar, J. (2015). About

attitudes and perceptions: Finding the proper way to consider latent vari-

ables in discrete choice models. Transportation, 44(3), 475–493. https://

doi. org/ 10. 1007/ s11116- 015- 9663-5.

10. Banister, D., & Hickman, R. (2013). Transport futures: Thinking the unthink-

able. Transport Policy, 29, 283–293. https:// doi. org/ 10. 1016/j. tranp ol. 2012.

07. 005

11. Ben-Akiva, M., & Lerman, S. R. (1985). Discrete choice analysis: Theory and

application to travel demand. MIT Press.

12. Beria, P., Bertolin, A., Tolentino, S., Debernardi, A., Ferrara, E., & Filippini, G.

(2019). A model-based evaluation of national transport policies. In XXI

SIET conference, Bologna, Italy.

13. Beser, M., & Algers, S. (2002). SAMPERS: The new Swedish national travel

demand forecasting tool. In National transport models (pp. 101–118).

Springer. https:// doi. org/ 10. 1007/ 978-3- 662- 04853-5_9

14. Bierlaire, M. (2020). A short introduction to PandasBiogeme. Ecole Poly-

technique Fédérale de Lausanne, report transp-or 200605 edn.

15. BMU. (2019). Klimaschutz in zahlen. Tech. rep., Bundesministerium für

Umwelt, Naturschutz und nukleare Sicherheit.

16. Box, GEP., Cox, DR. (1964). An analysis of transformations. Journal of the

Royal Statistical Society Series B (Methodological) 26(2):211–252, http://

www. jstor. org/ stable/ 29844 18

17. Boyce, D., & Williams, H. (2015). Forecasting urban travel: Past, present and

future. Edward Elgar Publishing.

18. Cabrera, E., & de Sousa, J. M. M. (2022). Use of sustainable fuels in aviation:

A review. Energies, 15(7), 2440. https:// doi. org/ 10. 3390/ en150 72440.

19. Cascetta, E. (2001). Transportation systems engineering: Theory and meth-

ods. Springer. https:// doi. org/ 10. 1007/ 978-1- 4757- 6873-2.

20. Chasserieau, Q., & Goix, R. (2019). Design and use of quetzal, an open-

source python library for transport modelling. In 47th European transport

conference, Dublin.

21. Cherchi, E., & Ortúzar, J. (2007). Empirical identification in the mixed

logit model: Analysing the effect of data richness. Networks and Spatial

Economics, 8(2–3), 109–124. https:// doi. org/ 10. 1007/ s11067- 007- 9045-4

22. Creutzig, F. (2015). Evolving narratives of low-carbon futures in transpor-

tation. Transport Reviews, 36(3), 341–360. https:// doi. org/ 10. 1080/ 01441

647. 2015. 10792 77

23. Creutzig, F. (2020). Limits to liberalism: Considerations for the anthropo-

cene. Ecological Economics, 177(106), 763. https:// doi. org/ 10. 1016/j. ecole

con. 2020. 106763

24. Creutzig, F., Jochem, P., Edelenbosch, O. Y., Mattauch, L., Vuuren, D. P.,

McCollum, D., & Minx, J. (2015). Transport: A roadblock to climate change

mitigation? Science, 350(6263), 911–912. https:// doi. org/ 10. 1126/ scien ce.

aac80 33.

25. Creutzig, F., Niamir, L., Bai, X., Callaghan, M., Cullen, J., Díaz-José, J.,

Figueroa, M., Grubler, A., Lamb, WF., Leip, A., Masanet, E., Mata, É., Mat-

tauch, L., Minx, JC., Mirasgedis, S., Mulugetta, Y., Nugroho, SB., Pathak,

M., Perkins, P., Roy, J., de la Rue du Can, S., Saheb, Y., Some, S., Steg, L.,

Steinberger, J., & Ürge Vorsatz, D. (2021). Demand-side solutions to cli-

mate change mitigation consistent with high levels of well-being. Nature

Climate Change. https:// doi. org/ 10. 1038/ s41558- 021- 01219-y

26. Daly, A. (2010). Cost damping in travel demand models. Tech. rep., RAND

Europe, http:// www. rand. org/ pubs/ techn ical _ repor ts/ TR717. html

27. DfT. (2020). Overview of model structure and update to 2015. resreport

70006059, Department for Transport, London, UK.

28. Dodds, P. E., & McDowall, W. (2014). Methodologies for representing the

road transport sector in energy system models. International Journal of

Hydrogen Energy, 39(5), 2345–2358. https:// doi. org/ 10. 1016/j. ijhyd ene.

2013. 11. 021

29. Drees, T., Medert, H., Meinecke, M., & Halici, C. (2021). Netzentwicklung-

splan strom 2035—version 2021, zweiter entwurf. Tech. rep., 50Hertz,

Amprion, TenneT, TransnetBW

30. Edelenbosch, O., McCollum, D., van Vuuren, D., Bertram, C., Carrara, S.,

Daly, H., Fujimori, S., Kitous, A., Kyle, P., Broin, E. Ó., Karkatsoulis, P., & Sano,

F. (2017). Decomposing passenger transport futures: Comparing results

of global integrated assessment models. Transportation Research Part

D: Transport and Environment, 55, 281–293. https:// doi. org/ 10. 1016/j. trd.

2016. 07. 003

31. Ehrenberger, S., Seum, S., Pregger, T., Simon, S., Knitschky, G., & Kugler, U.

(2021). Land transport development in three integrated scenarios for Ger-

many: Technology options, energy demand and emissions. Transporta-

tion Research Part D: Transport and Environment, 90(102), 669. https:// doi.

org/ 10. 1016/j. trd. 2020. 102669.

32. Fiorello, D., Martino, A., Zani, L., Christidis, P., & Navajas-Cawood, E. (2016).

Mobility data across the EU 28 member states: Results from an extensive

CAWI survey. Transportation Research Procedia, 14, 1104–1113. https:// doi.

org/ 10. 1016/j. trpro. 2016. 05. 181.

33. Frank, M., & Wolfe, P. (1956). An algorithm for quadratic programming.

Naval Research Logistics Quarterly, 3(1–2), 95–110. https:// doi. org/ 10. 1002/

nav. 38000 30109

34. Gota, S., Huizenga, C., Peet, K., & Kaar, G. (2016). Nationally-determined

contributions (ndcs) offer opportunities for ambitious action on transport

and climate change. Tech. rep., Partnership on Sustainable Low Carbon

Transport.

35. Gota, S., Huizenga, C., Peet, K., Medimorec, N., & Bakker, S. (2018). Decar-

bonising transport to achieve Paris agreement targets. Energy Efficiency,

12(2), 363–386. https:// doi. org/ 10. 1007/ s12053- 018- 9671-3.

36. Grube, T., Kraus, S., Reul, J., & Stolten, D. (2021). Passenger car cost

development through 2050. Transportation Research Part D: Transport and

Environment, 101(103), 110. https:// doi. org/ 10. 1016/j. trd. 2021. 103110

37. Hellekes, J., & Winkler, C. (2021). Incorporating passenger load in public

transport systems and its implementation in nationwide models. Procedia

Computer Science, 184, 115–122. https:// doi. org/ 10. 1016/j. procs. 2021. 03.

022

38. Hendzlik, M., Lange, M., Burger, A., Dziekan, K., Fechter, A., Frey, K., Lambre-

cht, M., Mönch, L., & Schmied, M. (2019). Kein grund zur lücke - so erreicht

deutschland seine klimaschutzziele im verkehrssektor für das jahr 2030.

Tech. rep., Umweltbundesamt, Dessau-Roßlau.

39. Hoffmann, C., Abraham, C., White, M. P., Ball, S., & Skippon, S. M. (2017).

What cognitive mechanisms predict travel mode choice? A systematic

review with meta-analysis. Transport Reviews, 37(5), 631–652. https:// doi.

org/ 10. 1080/ 01441 647. 2017. 12858 19.

40. IEA. (2013). Policy pathway: A tale of renewed cities. Tech. rep., Interna-

tional Energy Agency, Paris.

41. Javaid, A., Creutzig, F., & Bamberg, S. (2020). Determinants of low-carbon

transport mode adoption: Systematic review of reviews. Environmental

Research Letters, 15(10), 103–002. https:// doi. org/ 10. 1088/ 1748- 9326/

aba032.

42. Joksimovic, D., & van Grol, R. (2016). Consolidated calibration of Dutch

national and four regional models: Recent developments. In European

transport conference 2016, Barcelona, Spain.

43. Kaufmann, V. (2002). Re-thinking mobility contemporary sociology.

Ashgate, Farnham. http:// infos cience. epfl. ch/ record/ 104262

44. Kobiela, G., Samadi, S., Kurwan, J., Tönjes, A., Fischedick, M., Koska, T.,

Lechtenböhmer, S., März, S., & Schüwer, D. (2020). Co

-neutral bis 2035:

Eckpunkte eines deutschen beitrags zur einhaltung der 1,5-grad-celsius-

grenze; diskussionsbeitrag für fridays for future deutschland. Tech. rep.,

Wuppertal Institut für Klima, Umwelt, Energie, https:// doi. org/ 10. 48506/

OPUS- 7606

45. Koller, F., Winkler, C., Liedtke, G., Österle, I., Mocanu, T., Seibert, D., Özcan

Deniz, Matteis, T., Bergfeld, M., Sehn, V., Kattelmann, F., Haun, M., & Dirna-

ichner, A. (2021). Deutschland auf dem weg zur klimaneutralität 2045

- szenarien und pfade im modellvergleich - verkehr. Tech. rep., Potsdam

Institute for Climate Impact Research, Potsdam. https:// doi. org/ 10. 48485/

pik. 2021. 006

46. Krumm, A., Süsser, D., & Blechinger, P. (2022). Modelling social aspects of

the energy transition: What is the current representation of social factors

in energy models? Energy, 239(121), 706. https:// doi. org/ 10. 1016/j. energy.

2021. 121706

47. Köhler, J., Turnheim, B., & Hodson, M. (2020). Low carbon transitions

pathways in mobility: Applying the MLP in a combined case study and

simulation bridging analysis of passenger transport in the Netherlands.

Technological Forecasting and Social Change, 151(119), 314. https:// doi.

org/ 10. 1016/j. techf ore. 2018. 06. 003.

48. Lange, M., Hendzlik, M., & Schmied, M. (2020). Klimaschutz durch tem-

polimit - wirkung eines generellen tempolimits auf bundesautobahnen

Page 13 of 13

Arnz European Transport Research Review (2022) 14:44

auf die treibhausgasemissionen. Tech. rep., Umweltbundesamt,

Dessau-Roßlau.

49. Langford, M., Fry, R., & Higgs, G. (2012). Measuring transit system acces-

sibility using a modified two-step floating catchment technique. Interna-

tional Journal of Geographical Information Science, 26(2), 193–214. https://

doi. org/ 10. 1080/ 13658 816. 2011. 574140

50. Lovelace, R., Parkin, J., & Cohen, T. (2020). Open access transport models:

A leverage point in sustainable transport planning. Transport Policy, 97,

47–54. https:// doi. org/ 10. 1016/j. tranp ol. 2020. 06. 015

51. Mattauch, L., Ridgway, M., & Creutzig, F. (2016). Happy or liberal? Making

sense of behavior in transport policy design. Transportation Research Part

D: Transport and Environment, 45, 64–83. https:// doi. org/ 10. 1016/j. trd.

2015. 08. 006.

52. McCollum, D. L., Wilson, C., Pettifor, H., Ramea, K., Krey, V., Riahi, K., Bertram,

C., Lin, Z., Edelenbosch, O. Y., & Fujisawa, S. (2017). Improving the behav-

ioral realism of global integrated assessment models: An application to

consumers’ vehicle choices. Transportation Research Part D: Transport and

Environment, 55, 322–342. https:// doi. org/ 10. 1016/j. trd. 2016. 04. 003

53. Ortúzar, J., & Willumsen, L. G. (2011). Modelling transport. Wiley. https:// doi.

org/ 10. 1002/ 97811 19993 308.

54. Pfenninger, S., Hirth, L., Schlecht, I., Schmid, E., Wiese, F., Brown, T., Davis,

C., Gidden, M., Heinrichs, H., Heuberger, C., Hilpert, S., Krien, U., Matke, C.,

Nebel, A., Morrison, R., Müller, B., Pleßmann, G., Reeg, M., Richstein, J. C.,

… Wingenbach, C. (2018). Opening the black box of energy modelling:

Strategies and lessons learned. Energy Strategy Reviews, 19, 63–71. https://

doi. org/ 10. 1016/j. esr. 2017. 12. 002

55. Purr, K., Günther, J., Lehmann, H., & Nuss, P. (2019). Wege in eine ressour-

censchonende treibhausgasneutralität. resreport 36, Umweltbundesamt,

Dessau-Roßlau.

56. Rekdal, J. (2006). Evaluation of the Norwegian long distance transport

model (ntm5). resreport 0609, Work group for transport analysis in the

National Transport Plan in Norway, Molde, Norway.

57. Rich, J. (2020). A spline function class suitable for demand models. Econo-

metrics and Statistics, 14, 24–37. https:// doi. org/ 10. 1016/j. ecosta. 2018. 02.

002

58. Rich, J., & Mabit, S. L. (2015). Cost damping and functional form in

transport models. Transportation, 43(5), 889–912. https:// doi. org/ 10. 1007/

s11116- 015- 9628-8

59. Rich, J., & OvergaardHansen, C. (2016). The danish national passenger

model–model specification and results. European Journal of Transport and

Infrastructure Research, 16(4), 573–599. https:// doi. org/ 10. 18757/ EJTIR.

2016. 16.4. 3159.

60. Rohr, C., Fox, J., Daly, A., Patruni, B., Patil, S., & Tsang, F. (2013). Modeling

long-distance travel in great Britain. Transportation Research Record:

Journal of the Transportation Research Board, 2344(1), 144–151. https:// doi.

org/ 10. 3141/ 2344- 16.

61. Schubert, M., Kluth, T., Nebauer, G., Ratzenberger, R., Kotzagiorgis, S., Butz,

B., Schneider, W., & Leible, M. (2014). Verkehrsverflechtungsprognose 2030

- los 3: Erstellung der prognose der deutschlandweiten verkehrsverflech-

tungen unter berücksichtigung des luftverkehrs. techreport, Intraplan

Consult and BVU Beratergruppe Verkehr+Umwelt.

62. Schwartz, S. H. (1977). Normative influences on altruism. Advances in

Experimental Social Psychology, 10, 221–279. https:// doi. org/ 10. 1016/

s0065- 2601(08) 60358-5

63. Schäfer, A. (2012). Introducing behavioral change in transportation into

energy/ economy/ environment models. resreport WPS 6234, World

Bank, Washington, DC. http:// docum ents. world bank. org/ curat ed/ en/

34552 14683 23329 438/ Intro ducing- behav ioral- change- in- trans porta tion-

into- energy- econo my- envir onment- models

64. Sims, R., Schaeffer, R., Creutzig, F., Cruz-úñez, X., D’Agosto, M., Dimitriu,

D., Meza, M., Fulton, L., Kobayashi, S., Lah, O., Mckinnon, A., Newman, P.,

Ouyang, M., Schauer, J., Sperling, D., & Tiwari, G. (2014). Climate change

2014: Mitigation of climate change. Contribution of working group III to

the fifth assessment report of the intergovernmental panel on climate

change. Cambridge University Press, Cambridge, United Kingdom and

New York, NY, USA, chap Transport. https:// doi. org/ 10. 1017/ CBO97 81107

415416. 005

65. SRU. (2020). Umweltgutachten 2020: Für eine entschlossene umwelt-

politik in deutschland und europa. Tech. rep., Sachverständigenrat für

Umweltfragen, https:// www. umwel trat. de/ Share dDocs/ Downl oads/ DE/

01_ Umwel tguta chten/ 2016_ 2020/ 2020_ Umwel tguta chten_ Entsc hloss

ene_ Umwel tpoli tik. html

66. Train, K. (2002). Discrete choice methods with simulation. Cambridge

University Press.

67. Venturini, G., Tattini, J., Mulholland, E., & Gallachóir, B. Ó. (2018). Improve-

ments in the representation of behavior in integrated energy and

transport models. International Journal of Sustainable Transportation, 13(4),

294–313. https:// doi. org/ 10. 1080/ 15568 318. 2018. 14662 20

68. Vij, A., & Walker, J. L. (2016). How, when and why integrated choice and

latent variable models are latently useful. Transportation Research Part B:

Methodological, 90, 192–217. https:// doi. org/ 10. 1016/j. trb. 2016. 04. 021

69. Vovsha, P., Freedman, J., Livshits, V., & Sun, W. (2011). Design features of

activity-based models in practice. Transportation Research Record: Journal

of the Transportation Research Board, 2254(1), 19–27. https:// doi. org/ 10.

3141/ 2254- 03

70. Wanitschke, A. (2021). Are battery electric vehicles the future? An uncer-

tainty and robustness comparison with hydrogen fuel cell and internal

combustion engine vehicles in Germany. Shaker, Dueren

71. Wegener, M. (2011). From macro to micro-how much micro is too much?

Transport Reviews, 31(2), 161–177. https:// doi. org/ 10. 1080/ 01441 647. 2010.

532883

72. Windt, A., & Arnhold, O. (2020). Ladeinfrastruktur nach 2025/2030: Szenar-

ien für den markthochlauf. Tech. rep., Nationale Leistelle Ladeinfrastruktur.

73. Winkler, C., & Mocanu, T. (2017). Methodology and application of a Ger-

man national passenger transport model for future transport scenarios. In

45th European transport conference, Barcelona.

74. Winkler, C., & Mocanu, T. (2020). Impact of political measures on passen-

ger and freight transport demand in Germany. Transportation Research

Part D: Transport and Environment, 87(102), 476. https:// doi. org/ 10. 1016/j.

trd. 2020. 102476.

75. Witte, A. D., Hollevoet, J., Dobruszkes, F., Hubert, M., & Macharis, C. (2013).

Linking modal choice to motility: A comprehensive review. Transportation

Research Part A: Policy and Practice, 49, 329–341. https:// doi. org/ 10. 1016/j.

tra. 2013. 01. 009

76. Yeh, S., Mishra, G. S., Fulton, L., Kyle, P., McCollum, D. L., Miller, J., Cazzola, P.,

& Teter, J. (2017). Detailed assessment of global transport-energy models’

structures and projections. Transportation Research Part D: Transport and

Environment, 55, 294–309. https:// doi. org/ 10. 1016/j. trd. 2016. 11. 001

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