HIGH SPEED RAIL DEMAND:EMPIRICAL AND MODELLING EVIDENCES FROM ITALY
Ennio CASCETTA
Department of Transportation Engineering, “Federico II” University of Naples
Pierluigi COPPOLA
Department of Enterprise Engineering, “Tor Vergata” University of Rome

1 INTRODUCTION
High Speed Rail (HSR) represents the future of medium-haul intercity
transport provided that a number of projects have been developing all over theworld, despite the financial and economic crisis. In Europe, in order to achieve
the strategic objectives of social inclusion, cohesion and accessibility, theEuropean Commission launched in 1998 a program to define the standards todevelop an integrated and interoperable HSR network. Indeed, the European
HSR has largely expanded in the last decades: following the LGV (Ligne àGrande Vitesse) in 1998 connecting Paris and Lyon (420 Km), HSR services
are nowadays diffused in Spain (Renfe), Germany (ICE), Benelux (Thalys)and Italy (TAV) for a total of about 6.500 kilometers; new projects are underdiscussion or are in progress in Sweden, UK, Portugal, Russia and Turkey.
On the other hand, in the Far-East, the pioneer Tokaido-Shinkansen HSR line
(launched in 1964 for the Tokyo Olympic Games, between Tokyo and Osaka)
has nowadays been joined by 5 new HSR lines for a total network extensionof about 2300 Km for 353 million of annual passengers. As of 2011, China is
the country with the more extended routes in service including more than
3.500 km of rail lines at 300 km/h operating speed, and the network is
expected to largely expand in the next years.
Different typologies of HSR management models, depending on the use(exclusive or mixed) of the HSR infrastructure can be distinguished. The
Japanese case, for instance, is an example of exclusive HSR infrastructurewith single operator: the High Speed Railways is entirely separated from therest of the railways network and the High Speed trains run only on suchinfrastructure. On the other side, we find the French operation model, in which
the HS trains can use both HSR and non-HSR (duly potentiated)infrastructures. From the operations perspective, there are cases with a single
operator in charge of the infrastructure and of the operations, as in Japan,
where each of the 6 Shinkansen lines (infrastructure and service) is managed
by a different private operator, or as in Spain and France where there is asingle national operator in charge of the infrastructure and the service; andcases in which there is a distinct owner of the infrastructure and a different
service operator, as it is in Germany, in Italy, and in Benelux for the Thalys,
whose trains run on the HS infrastructure of different National Railways(Netherland, Belgium, France). From 2012, In Italy there will be the first caseof competition among HSR operators on the same infrastructure and thesame market, when the new private HSR operator “Nuovo Trasporto
Viaggiatori” (NTV) will start operations competing with the incumbent
Trenitalia. The Italian case study presents also an additional peculiarity

regarding the network configuration. In facts, while most of the HSR projects
have been conceived to connect pairs of cities in the distance range of 500700 Km (e.g. Tokyo-Osaka, Paris-Lyon, Madrid-Barcelona,…) creating acompeting modal alternative versus the air transport, in Italy, due to thestructure of the Nation, the HSR connects a sequence of medium and large
metropolitan areas at distances in the range of 100-250 Km, becoming a sortof “national-metro” line. As a result the Italian HSR not only does competewith air transport but also, and mostly, with the auto. Such structuraldifferences in the Italian national transportation market, together with theongoing economic crisis with no antecedents in the globalization era, create
the conditions for a unique case study to investigate the behavior of longdistance passengers and to test travel demand forecasting methodologies.
The paper is organized as follows. In section 2 the Italian HSR project ispresented starting from the characteristics of the current network and its
recent developments. In section 3 empirical evidences of the evolution of theinter-province travel demand in the study area (in-scope demand) are
discussed in terms of overall travel demand growth, modal shares andtravelers’ characteristics, before and after the new HSR line operations. The
latter is based on an extensive survey campaign consisting of on-board
counts and on RP-SP interviews gathered during between the years 2008 and2011. In section 4 a modeling framework developed to forecast travel demandin future scenario of analysis is presented; furthermore, in section 5, some
applications of such model simulating several scenarios including services
lines, fares and timetables will be discussed. Finally, conclusions and further
research areas are reported in section 6.

2 THE ITALIAN HSR PROJECTIn Italy, the first HSR service was launched in 1992 between Firenze andRoma with the so called “direttissima”, allowing trains to run at 230 Km/h andcovering the 254 Km between Roma and Firenze in about 2 hours. The
project of the “direttissima”, however, dated back to 1970. As a matter of fact,
the new-generation HSR (i.e. with trains running at 300 Km/h maximum
speed) was launched in December 2005 between Roma and Napoli (205 Km)
and Milano and Bologna (182 Km). The project had a second step onward in
December 2009 when the Milano-Torino (125 Km) and the Bologna-Firenze(79 Km) lines were completed, as well as the urban penetration in the cities ofRoma and Napoli. As a matter of fact, from 2010 the backbone of the Italian
HSR network is fully operative (see fig 1).

The Study area

*37 trains

37+ 23 trains

54 trains

23 trains

18 trains

HSR daily
services frequency

65 trains

65 trains

42 trains

14 trains* Eurostar service

Figure 1: the Italian High Speed Rail network
Today the service includes several city pairs at distances in between 100-250
Km, e.g. Roma-Napoli (205 Km in 1 hour and 10 minutes), Milano-Torino(125 Km in 54 minutes) and Roma-Milano with a direct service covering 515
Kilometers in 3 hours (Table 1). The HSR service frequency on the HSR
network ranges from 1 train/h to 4-5 train/h in the peak period on the sections
between Roma, Firenze and Bologna (Figure 1).
Table 1: distances and travel times by Rail before and after the HSR on
selected OD Pair.

Torino-Milano
Torino-Roma
Milano-Roma
Milano-Napoli
Milano-Bologna
Bologna-Firenze
Roma-Napoli

distance
(Km)
125
640
515
720
182
79
205

Travel times
before HSR
1h-33'
6h-30'
4h-30'
6h-30‘
1h-40'
59'
1h-45'

Travel times
after HSR
54'
4h-30'
3h
4h-55'
65'37'
1h-10'

Decrease of
travel times
-42%
-31%
-33%
-31%
-35%
-37%
-33%

The Italian HSR project is still under development. The travel times station-tostation, which have already been reduced of about 30-40% (Table 1), areexpected to be further reduced with the completion of the new undergroundbypass-stations in Bologna and Firenze that will allow to speeding up theservice in such dense urban areas. Moreover, Trenitalia has announced thelaunch of new-generation HS trains running at 360 Km/h as of 2015, which
would reduce the travel time from Roma to Milano down to 2 hours and 40
min; and several extensions of the current network are in progress, such as

the Milano-Venice and the Torino -Lyon, or are being discussed (e.g. the
Napoli-Bari and the Milano-Genoa sections).
Finally, starting from 2012 the new HSR operator “Nuovo Trasporto
Viaggiatori” (NTV) will be operative on the national HSR network, competingwith the incumbent Trenitalia, giving rise to the first case in the World ofcompetition among HSR operators on the same line (i.e. multiple operators ona single infrastructure). NTV’s fleet will include the Alstom AGV trains with acapacity of 460 seats per train against the ETR 500 and the ETR 600
Trenitalia rolling stock, respectively of 600 and 430 seats per train. In terms ofruns and train-Km, NTV is expected to cover about one third of the Italian
HSR market: 35.000 train-Km/day vs. 78.000 train-Km/day by Trenitalia(Table 2).
Table 2: comparison between service planned by NTV and Trenitalia (newand incumbent HSR Operator)#runs/day TrainKm/day
Trenitalia(year 2011)
NTV(Base scenario)

Avg.
SeatKm/day
Avg.
Distance per(mil.)
TrainCapacity
train

105

78.162

43,9

744

561

51

35.238

15,9

691

460

3 EMPIRICAL EVIDENCES
Since 2008 a vast campaign of surveys has been carried on monitoring theevolution of the national demand of passenger transport, compared to themodification of HSR services supplied, in terms of total trips, modal split
between auto and train services (i.e. Intercity, Eurostar, HSR), individual trip
frequency, travelers daily routine and life-styles. The surveys consisted of: a)on-board counts on the HSR and Intercity trains, carried on in May andOctober 2009, May and October 2010, and in May 2011; b) RP/SP interviews
carried on in May 2009 and October 2010. The counts were used to update
the existing outdated OD matrices of Eurostar and Intercity trips, and to
estimate the current inter-province OD flows on Intercity and HSR (the latter
having, in the last year, gradually replaced the Eurostar service). Together
with the source data available of the national highway traffic (AISCAT2009,2010) and of the domestic air passenger demand (ENAC 2009,2010),
the total demand in the study area (Figure 1), and the relative modal shares
have been estimated for year 2009 and year 2010.
Between the main cities connected by the new HSR lines (i.e. the cities of
Napoli, Roma, Firenze, Bologna, Milano and Torino ), it can be observed(Table 3), both in terms of Passenger and Pass-Km’s per year, there was asubstantial reduction of the share of trips in auto (i.e. Passengers from 45% to37%, Pass-Km from 31% to 25%) and an increase of HSR modal share (i.e.
Passengers from 37% to 49%; Pass-Km from 42% to 54%); air transport
basically has maintained its market shares; Intercity showed a slight decrease
from 9% to 6%.

Table 3: modal split of trip between the main provinces served by HSR, in theyears 2009 and 2010.

Auto
Aereo
Intercity
HSR
Total

Pax/year (million)
2009
2010
8,0
45%
8,0
1,4
8%
1,7
1,6
9%
1,4
6,6
37%
10,9
17,6
100%
22,0

37%
8%
6%
49%
100%

PaxKm /year (million)
2009
2010
1800,7
31%
1799,1
25%
980,0
17%
1072,0
15%
523,8
9%
442,5
6%
2434,342%3901,054%5738,9100%
7214,6100%

The percentage variation of the HSR modal shares of Passengers (+32%)and
Pass-Km’s (+29%) compared to the reductions of the HSR travel times,
ranging from 30%-40% (see Table 1) allow us to estimate an observed HSR
demand direct elasticity with respect to travel time ranging from -0,81 to -1,1
in terms of Passengers and from -0,71 to -1,0 in terms of Pass-Km’s. Looking
more in depth at single OD pairs, we observed the variations of HSR demandvary significantly with the OD distance: for example between Firenze andBologna (79 Km) after the introduction of the HSR services (which reduced
the travel time by 37%) there was an increase of demand of 91%
corresponding to an elasticity of -2,5; between Milano and Napoli (720 Km)
after the introduction of the HSR services (which reduced the travel time by31%) there was an increase of demand of 41% corresponding to an elasticityof -1,3. The elasticity’s reported here are very substantial; they arecomparable with the elasticity’s reported in the literature by studies of the typepresented here (see, for instance, Yao and Morikawa, 2005; Park and Ha,
2006; Roman et al., 2007). On the average, if we consider the overall increaseof demand volume from 2009 to 2010 (i.e. including both demand growth,
induced and diverted components) between the above cities (i.e considering
the percentage variation of the HSR total volumes), we estimate an increaseof HSR passengers from 6,6 million to 10,9 million (+65%) and of HSR PassKm’s from 2,4 billion to 3,9 billion (+63%). Such percentages give rise to an
overall elasticity value ranging from -1,6 to -2,1 in terms of passengers andfrom -1,5 to -2 in terms of Pass-Km’s (Table 4).
Table 4: observed HSR demand direct elasticity’s with respect to travel times.
HSR modal shares

HSR total volumes

Pax-1,1 ÷ -0,81-2,2 ÷ -1,6

PaxKm

-1,0 ÷ -0,71-2,0 ÷ -1,5

In the whole study area, the increase of HSR demand between 2009 and2010 was about 34%, from 6,7 billion to 9,1 billion of passenger-km (Table 5).
The increase of demand in percentage is much higher than the increase of
supply both in terms of seat-Km (+11,6%) and in terms of train-Km (+28,3%).
Moreover, such increase of demand seems not to run down in 2011, where an
additional 8% of HSR passenger-Km can be observed against an almost
invariant supply of train-Km (+2,9%) and seat-Km (-1,6%).

Table 4: HSR demand and supply evolution between 2009-2011.
train-km/year(thousands)

seat-km/year(mil.)

Pass-Km/year(mil. )

19.62425.18328,3%25.9052,9%32,2%12.75014.22611,6%14.461
1,6%
13,4%

6.767
9.067
34,0%
9.791
8,0%
44,7%

2009
2010
∆% (2010-2009)2011∆% (2011-2010)∆% (2011-2009)

The increase of passenger-Km partly due to increased modal share, partly
due to the partial recovering of the national economy after the financial crisisof 2008-2009, and partly due to the additional demand induced by the HSR. In
facts, a variation in the generalized travel cost on HSR services can induce(Table 6): a) a shift between alternative railways services (intercity vs. HSR),
and from alternative modes of transport (air, auto) to HSR (i.e. diverted
demand); b) a change in the destination of the trip, and/or in the trip frequency(i.e. induced demand).
The induced demand, in turn, may depend either “directly” on the generalized
travel cost, i.e. the trip become more frequent because traveling with HSR isfaster, cheaper and/or more comfortable, or “indirectly” due to a modificationof the travelers’ lifestyle, e.g. travelers start commuting (i.e. making more
frequent trips) due to the relocation of the residence or of the workplace, andpartly due to changes in land use, e.g. the number of residents, jobs andactivities.
Table 6: taxonomy of the impacts on HSR demand.
from other modes
DIVERTED
DEMANDfrom other rail
servicese.g. shift from Intercity to HSR

directe.g. increase of trip frequency,change of trip destination

indirecte.g. increase of mobility due
to change in life-styles andland use

INDUCEDDEMAND

DEMANDGROWTHe.g. shift from air/auto to HSRe.g. increase of mobility due
to economic growthendogenous
factors

exogenous
factors

To investigate more in depth the above phenomena, particularly those related
to the induced demand, a retrospect survey has been designed and amodeling architecture simulating in a disaggregate manner the above
components of the HSR demand are under development. Preliminary resultsof the retrospect survey have been gathered on the Roma-Napoli segment(Cascetta et al., 2011). Here the travel time has, firstly, been reduced from 1
hour and 45 minutes by the Eurostar service, at a fare of 22 and 31 euro per(one-way) trip respectively for 1st and 2nd class, before the opening of HSR,to 1 hour and 25 minutes, at a fare of 33 and 43 euro per (one-way) trip
respectively for 1st and 2nd class, after the opening of the December 2005
(i.e. the whole line except of the urban penetration in Roma and Napoli).
Secondly, it has been further reduced to 1 hour and 10 minutes, at a fare of
45 and 57 euro per (one-way) trip respectively for 1st and 2nd class, after thecompletion of the urban penetrations in Roma and Napoli. Against thesechanges in the level of service the modal share on HSR demand hasconstantly increased during the year 2005-2010 despite the decline in total (allmodes) demand observed in the years 2008 and 2009, due to the global
economic crisis (Table 7).
Table 7: demand evolution on the Roma-Napoli segment (average-day
estimates)
2005

2007

2008

2009

2010

Auto

10.93361,5%10.855

56,3%

8.237

52,8%

6.472

49,7%

6.719

41,5%

HSR/
Eurostar

3.741

21,0%

5.181

26,9%

4.828

30,9%

4.384

33,7%

7.185

44,4%

Intercity

3.108

17,5%

3.241

16,8%

2.549

16,3%

2.161

16,6%

2.287

14,1%

17.783 100,0% 19.277 100,0% 15.614

100,0%

13.018 100,0% 16.192

100,0%The retrospect survey, carried in 2008 among travelers using different trains
services (i.e. HSR, Eurostar and Intercity) and drivers on the highway
between Napoli and Roma, after one year of operations of HS trains allowed
the estimation of the components of the generated demand. Considering the
average working day, it has been estimated a 53,7 % of HSR travelers that
did not change trip frequency and mode/service (i.e. travelers who travelled
Eurostar, now moved on the HSR), the remaining 46,3% consisting of:travelers (12,5%) who did not make any trip before HSR (i.e. new demand“indirectly/exogenously” induced by change in lifestyle; travelers (15,4%) who
have increased their trip frequency; travelers (6,9%) who diverted from othermodes; and travelers (11,5%) who diverted from other services, mainly fromintercity trains (Tab.8).
Table 8: HSR demand segmentation on the Roma-Napoli section (2008).
Working day

Saturday

Sunday

53,7%

40,1%

42,1%

46,3%

59,8%

57,9%

new demand

12,5%

13,6%

8,8%

increasing frequency

15,4%

12,7%

15,6%

diversion to HSR from other modes
diversion to HSR from other Rail
services

6,9%

11,6%

14,0%

11,5%

21,9%

19,5%

passenger not having changed
frequency and mode
passenger having changed frequency
and mode

Furthermore, it has been observed most of the HSR induced trips were due tobusiness trips (percentages ranging from 38,7% in Sundays to 57,4% inworking days) while education related trips (percentages ranging from 3,4% in
Sundays to 6,2% in working days) and other purposes trips (percentages
ranging from 52,5% in Sundays to 30,2% in working days) showed lower
fractions. Moreover, a significant percentage of HSR induced demand was
related to commuting (percentage ranging around 6%) corresponding topeople that moved the residence in the first year of operations (mainly fromRoma to Napoli) starting to commute every day (Table 9).
Table 9: segmentation of the induced demand on the Roma-Napoli section bytrip purpose (2008).
Working day

Saturday

SundayBusiness

57,4%

68,5%

38,7%

Commuting(i.e. residential re-location)

6,2%

0,0%

5,4%

Education related purposes

6,2%

8,7%

3,4%

Other purposes trips

30,2%

22,8%

52,5%

100,0%

100,0%

100,0%

4 THE DEMAND FORECASTING MODELING ARCHITECTURE
To forecast the Origin-Destination (OD) passenger volumes by HSR services,a methodology based on integrated demand and supply models, estimatedand validated by RP-SP surveys and traffic counts, has been developed(Figure 2).
The OD matrices by railways services (Intercity and HSR) and by auto and airtransport are updated based respectively on the traffic counts and source datausing a GLS estimator (Cascetta, 2009). The “National demand growth” model
projects the base year total OD volumes to future years, according toassumed macroeconomics trends (e.g. GDP growth, oil price).
The schedule-based mode choice model estimates the market share ofdifferent inter-province transportation modes, including alternative rail
services, such as Intercity, High-Speed, 1st and 2nd class, and individual
trains (i.e. runs) characterized by different timetables. Within the proposedmodeling forecasting framework the run-based model is applied at a first levelto the current supply scenario in order to reproduce the current mode-service
share by the fine tuning of the mode/service Alternative Specific Constants(ASC) (i.e. model calibration); at a second step the model, fed by the level ofservice attributes computed by the supply model, forecast the OD modal
share in the future scenario.
The induced demand model (see Ben-Akiva et al., 2010), estimates the
additional HSR demand generated by changes in destination and tripfrequency due to the improvement of HSR generalized travel cost.
Finally, the OD flow on the HSR services are assigned to individual trains
allowing the forecasting of occupancies on each train and each station-tostation link.

Future supply
scenarios

Supply Models(diachronic
network)

DesiredDeparture Time
(DDT) demand
distribution

OD matrices(all modes)
by time slice

Schedule-basedmode choicemodel(by purpose
and user class)Current OD
matrices(corrected bytraffic counts)

Modelcalibration

Current sypply
scenario

Schedule-basedmode choice
model(Estimation)

RP-SP Surveys

OD modalsharesFuture OD
matrices(NTV ETRENITALIA)

Demandgrowth model

Traffic
Countsandsourcedata

Flows onindividual
trainsInduceddemand model

Figure 2: the HSR demand forecasting methodology
4.1 Supply models
The overall modeling framework is currently based on a zoning system
consisting of 220 zones (Figure 3).The study area includes all the Italianregions with the exception of the islands of Sicily and Sardinia. The “external”
regions of Abruzzo, Molise and Umbria (on the Adriatic corridor) and theregions of Trentino-Alto Adige and Valle d’Aosta (in the North) are each
represented by one zone. All the other Italian provinces have been split into
two zones: one zone for the main city of the province and one zone for the
rest of the province. Moreover, the main Italian cities have been further
divided into several zones: Roma into 13 zones; Napoli into 8 zones; Torino
into 6 and Milano into 10 zones.
A system of transportation network and service supply models has beendeveloped to estimate the OD level of service by road, air, and rail. The
supply models includes: a) a road network consisting of a graph with 1900
nodes and 7000 links representing 35000 Km of the national roads; b) a rail
network consisting of a graph with 2600 nodes and 5500 links representing
14500 Km of railways (60% with single track, 40% with double track).
In the current transport scenario, the simulated services supply system
includes 500 daily domestic flights between major Italian airports plus thefollowing railway services: 111 High-Speed trains; 232 intercity trains; 4.466
interregional and regional trains (the latter here represented to simulate theaccess-egress to HSR services). The resulting diachronic network includes
about 126.500 nodes and 330.000 links.

Road graph2600 nodes
5500 links(representing 14500 Km)

Railways graph

1900 nodes
7000 links(representing 35000 Km)

Figure 3: the adopted zoning system of Italy and the road and railways
infrastructures networks
4.2 The demand growth model
The study here presented, started in 2008, in the middle of the severe phaseof the current financial and economic crisis. The expectations on the Economy
decline had no antecedent in the globalization era, so in order to estimate theimpacts on the total (all modes) demand volumes, instead of using the usual
elasticity’s from previous studies on HSR, a specific demand growth model
was developed to capture a correct response to the downturn of the National
Economy. This is here reported in details as it has been the base for theapplications presented in the next section 5.
The demand growth model is a linear regression model relating the demand
volumes to general economic factors with a specific focus on past observed
recession and stagnation period: the evolution of demand (d) depends on the
Gross Domestic Product (GDP) and the oil price percentage variation
between two successive years:
Δ[GDPt ]Δ[GDPt ]Δ[Oil _ price t ]Δ[d t ]
= α + β + ⋅ (1 − δ t ) ⋅+ β − ⋅δt ⋅+γ ⋅d t −1
GDPt −1
GDPt −1
Oil _ price t −1
⎧1 Δ[GDPt −1 ] < 0,01
otherwise⎩0

δt = ⎨

The model was calibrated using time series (1980-2007) data of GDP,
expressed in constant 2000 Euro, provided by ISTAT, Italian Institute of
Statistics, and the yearly average oil price, also expressed in 2000 Euro,
provided by Italian Ministry of Economic Development.
The model is used to derive elasticity’s of demand with respect to GDP growthfor periods of increasing economy and for periods of economic stagnation.
Different assumptions have been made to identify the threshold between
those periods. Starting from 0% (the threshold which typically identifies
periods of economic recession), we have found out that a 1% threshold is themost appropriate value in reproducing the traffic estimates and in terms of
goodness of fit. In facts, the multiplier of the GDP evolution has two values: alower value "beta -" if the growth is below 1% and a higher value "beta +" if
the GDP growth greater than 1%. The resulted elasticity’s on GDP and oil
price is consistent with those used in Spain and France although notdifferentiated according to the GDP variation sign (Cabanne, 2003).
Table 9: estimated parameters of the demand growth model.

alphabeta +
beta gamma%
rho_2
#obs

Parameter
value
0,001
1,186
0,611
-0,1570,65
28

t_ratio

P>z

0,851
4,568
1,554
-2,517

0,443
0
0,098
0,011

By applying the model to years 2008, 2009 2010 using respectively the GDP
delta% values of -0,9%, -4,6%, -0,50%, the model predicts a reduction of
travel demand comparable to those observed by AISCAT, i.e. the associationof national Highways (Table 10).
Table 10: observed vs. (back-casting) estimated travel demand growth in Italy.
GDP %variations

Total (all modes) Demand variation %Model estimates
Observed values *2008-0,90%-0,50%-0,50%

2009-4,60%-2,76%-2,40%-0,50%
2010*Source: AISCAT-0,26%-0,20%

4.3 The schedule-based mode-service choice and assignment model
The mode- service choice model is a schedule-based mode-service choicemodel aiming at simulating the competition between transportation modes and
HSR operators (i.e. Trenitalia and NTV) based not only on travel times andcost but also on the actual departure time of the trains and flights (i.e.
timetables).
The model is set as a Nested-Logit models (see Ben-Akiva and Lerman,
1985) with a nesting structure to capture higher degrees of substitutions

among specific subsets of modal alternatives, particularly the HSR
alternatives provided on the same route by different operators, i.e. NTV vs.
Trenitalia (Fig.4). To Authors’ knowledge this is the first application of an
integrated mode-service-run choice model to intercity travel demand at thenational scale.
Θ0

RAIL

AUTO

AIR

…

Run i
HSR

Intercity

Run i

Trenitalia

2st class

1st class

…

Run i

…

1st class

Run i

NTV
1st class

…

Run i

1st class

…

Run i

2st class

…

Run i

…

Figure 4: the nesting structure of the schedule-based mode/service choicemodel.
Travelers are assumed to have the following 8 mode/service alternatives:• Auto• Air• Intercity Trenitalia 1st class• Intercity Trenitalia 2nd class• High Speed Trenitalia 1st class• High Speed Trenitalia 2nd class• High Speed NTV 1st class• High Speed NTV 2nd class
For Air and Rail (intercity + HSR), the alternatives runs available are
distinguished by departure time. The choice set of the runs was obtained bycomparing different choice-set generation rules, and computing the coverage
ratio observing the runs actually chosen by the travelers in the RP survey.
Travelers are supposed to select the first non –dominated runs “early” and thefirst non-dominated run “late” with respect to the Desired Departure Time
(DDT). The DDT distribution among travelers has been estimated using an RP
survey carried out in October 2010 through the maximum Likelihood method,
for two difference distance class, i.e. less and greater than 400 Km, and two
travel purposes, i.e. Business and Other purposes (Figure 5).

Figure 5: the estimated Desired Departure Timedistance class and travel purpose.

(DDT) distribution by

The models were estimated for two trip purposes (i.e. “Business” and “Other”)
through Maximum Likelihood method, using SP survey in order to capture
preferences for new on-board services such as free internet, and, mostly, the
new NTV operator not yet in the market. The estimation was based on
disaggregate data gathered in a RP-SP survey carried out during October2010. The following attributes of the systematic utility of the alternatives
resulted to be statistically significant:- Level of service attributes:• Travel time
• Travel cost, by 4 categories of travelers (travel alone vs. party,
reimbursed vs. not reimbursed)• Access/egress time, for Air and Rail• Schedule delay w.r.t. DDT- Socio-economic attributes:• Professional condition (high vs. low)
• Degree (yes vs. no)• Travel frequency
A stochastic assignment model with no capacity constrains was adopted toload the diachronic network and estimate the flow on individual trains. In
Figure 6 a scatter diagram of the assigned flows on-board on each HS trains
and links predicted by the model and counted in the working day of October2010 is reported.

Assigned flows

counted flows
Figure 6: scatter diagram of predicted vs. counted on-board trains (October2010).

May (counted flows)

The rho squared is 0,48 which is comparable to the rho squared (i.e. 0,52) ofthe regression plotted out using the counted flows in May and October 2010;
in other term the error of the model is within the day-to-day variability of theobserved train flows on the HS links.

October (counted flows)
Figure 7: scatter diagram of counted flows on May and October 2010.

5 APPLICATIONS
The model has been applied to predict the impacts on national passenger
volumes of the new HSR services and operators as of 2012 (i.e. incoming of
the new operator NTV) and subsequent years. Different hypotheticalscenarios have been tested under different macroeconomic assumptions andmarketing strategies of the HSR competitors on the long distance (i.e. NTV vs.
Trenitalia).
It should be stressed that reference and alternative scenarios reported in thefollowing are hypothetical and do not represent commercial choices made byboth NTV and Trenitalia operators.
5.1 Reference scenario
Assuming the 2010 as base-year, GDP growth up to 2015 was estimatedusing the current forecasting of the Italian Ministry of Economic Development.
Using the demand growth model, the cumulated demand growth accordingly
estimated is equal to +8,2%.
The reference scenario (year 2012) with the NTV service fully operative
shows an increase of passenger-Km per year of 9,2% w.r.t. 2010 with a split
between NTV and Trenitalia respectively of 33.4 % and 66,6%. Given an
overall demand growth in 2012 of 3,3%, a net increase of demand due to theadditional HSR services of NTV of a significant 5,9 % (i.e. about half a billionof Pass-Km) can be estimated, representing the sum of the induced anddiverted demand at year 2012 cause by the increase of HSR frequencies.
In the reference scenario it was assumed that the NTV service network
reproduced the structure of Trenitalia services (Figure 8).
5.2 Policies tested
The applications here presented regard some potential strategic andoperational policies of the new operator NTV, such as :• modification of the service structure, i.e. reduce the service on the Torino Milano-Salerno HSR line and add new HSR services on the Adriatic
corridor (MI-BO-PE)1 and Torino -Milano-Venice corridor running on mixed
HSR and conventional-rail sections (scenario 1)• Variation of the fares (scenario 2)• Optimizing the timetables to increase passenger flows (scenario 3)
In the scenario 1, the results show that it is possible with the same quantity of
train-Km reduce the rolling stock fleet by 1 unit and increase the passengerkm’s of 3%.
In scenario 2, two hypothesis have been tested starting from a reference casein which the first class fare were the same and the second class fare of NTV
were 8% less than Trenitalia: Case 1) Trenitalia reduces the HSR fares of20%; Case 2) both the HSR operators reduce the fares by 20% w.r.t.
reference case.

1

To test such scenario a modification of the initial zoning system was needed: the region
Marche and Abruzzo were split respectively into 10 and 6 new zones.

new services on Turin‐Milan‐Venice
MilanoTorinoVenezia
PadovaBologna

new services on the Adriatic corridor

Firenze

Roma
NapoliSalernoReference HSR
service structure
Figure 8: modification to the NTV service (scenario 1).
In the first case, Trenitalia market share increase from 66,3% to 71,5% andconversely NTV market share reduces from 33,4% to 28,5%; the HSR overall
demand increases by +5,4%. In the second case, the overall HSR demand
increases by 7,3% while the HSR market shares remains almost the same asin the reference case. This allows us to estimate some direct and cross (HSR
demand) arc-elasticity to the fares. In particular it can be estimated a directelasticity of total HSR demand w.r.t. fares of -0,37 and a HSR operators
cross-elasticity of +0,74 (i.e. a fare reduction of by one operator induce a
decrease of traffic on the competing operator). Higher value of crossoperators elasticity’s than modal elasticity’s are to be expected given thecorrelation structure of the perceived utility according to the nested structureof Figure 4.
Finally, the third application (Scenario 3) was conceived to show how and towhat extent modifications of the timetable to better fit the curve of the travelers
desired departure time (DDT), can improve the patronage. An example isreported in Figure 9, where the traffic flow diagram on a specific no stop train
from Milano to Roma is reported in two different cases of departure time from
“Milano P. Garibaldi” station: at 6.45 am and at 7.15 am. It can be observed(Figure 9) that in this case shifting onwards the departure time of the train of
30 min, increases the on-board traffic of a substantial amount.

Figure 9: passengers on board a specific train under different departure time
hypothesis.

6 CONCLUSIONS AND FURTHER RESEARCH AREAS
The empirical evidences from the Italian HSR market, presented in this paper,show an outstanding increase of passenger-Km between 2009 and 2011(+45%) on the Italian HSR network. This is partly due to increased HSR
modal share, partly due to the recovering of the national economy after thefinancial crisis of 2008-2009, and partly due to the additional demand inducedby the HSR. Compared to the variation of travel time (ranging from 30% to
40% depending on the OD), direct elasticity’s values of HSR demand w.r.t.
travel time, significantly greater than 1 (in absolute value) were observed.
Such values are very substantial, but are comparable with those reported in
the literature. Moreover, an inverse relationship of the elasticity values withthe distance has been observed, i.e. the lower the OD distance the higher theelasticity’s of HSR demand, suggesting for non-linear effects of induced
demand which need to be verified more in depth and possibly quantified.
Further investigations will focus on the week-end mobility and on theforeigners (occasional) demand flows.
On the other hand, the modeling framework here developed, based on mixed
RP-SP interviews gathered in between 2009 and 2011 to forecast the national
passenger demand, presents a nesting structure to capture higher degrees ofsubstitutions among specific subsets of modal alternatives, particularly the
HSR alternatives provided on the same route by different operators, i.e. NTV
vs. Trenitalia. The estimated (by the model) direct elasticity’s of HSR demandwith respect to HSR fares is -0,37; a value of +0,74 of cross elasticity’s w.r.t.
among competing HSR operator have been estimated.
The OD flows on the HSR services have been assigned to individual trains
allowing the forecasting of occupancies on each train and each station-tostation link. The error of the model has resulted to be within the day-to-day
range of variability of the flows. The area of improvement of the current

modeling framework is in more disaggregate models of induced demand andin a finer level of simulation of the urban accessibility, introducing a moredisaggregate access/egress mode choice modeling.
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