high speed railways in italy. a general perspective and ... karlsruhe.pdf445 contacts gathered via...

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High Speed Railways in Italy.

A general perspective and some evidence from a competitive market

Karlsruhe Institute of Technology – June 14th, 2013

Ennio CASCETTA – “Federico II” University of Naples

CONTENTS

• The Italian High Speed Railways (HSR) system • The methodology for planning and monitoring

HSR services

• Empirical evidences from a competitive market

• Conclusions

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3

RECENT DEVELOPMENTS OF THE HSR ITALIAN NETWORK

December 2009: • Opening of the HSR link Bologna-Firenze

• Opening of the link Torino-Milano • Completion of the urban penetration in Naples

Milano

Napoli

Firenze

Bologna

Torino

High Speed Railways 1.355 km

Total National Railways Network

24.179 km

Intermediate steps December 2005: opening of the HSR link between Rome and Naples (205 Km) December 2008: Opening of the HSR link between Milano and Bologna (182 km)

RECENT DEVELOPMENTS OF THE HSR ITALIAN NETWORK impacts on travel times by Rail

OD distance (Km)Travel times

before HSR

Travel times

after HSR

% variation of

travel times

Torino-Milano 125 1h-33' 54' -42%

Torino-Roma 640 6h-30' 4h-30' -31%

Milano-Roma 515 4h-30' 3h -33%

Milano-Napoli 720 6h-30‘ 4h-55' -31%

Milano-Bologna 182 1h-40' 65' -35%

Bologna-Firenze 79 59' 37' -37%

Roma-Napoli 205 1h-45' 1h-10' -33%

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THE ITALIAN HIGH SPEED RAILWAYS (HSR) PROJECT The “Italian Metro” - Ennio Cascetta © Population currently being served by HSR sevices

Population City Province

AREZZO 98788 349651

BOLOGNA 374944 991924

BOLZANO 104.278 507657

BRESCIA 190844 1256025

CHIUSI 8869 272638 (siena)

CONEGLIANO 35514 prov. Treviso

FERRARA 134464 359994

FIRENZE 365659 998098

MILANO 1295705 3156694

MODENA 181807 700913

NAPOLI 963661 3080873

NOVARA 105024 373230

ORVIETO 21059 234665 (Terni)

PADOVA 211936 934216

PARMA 182389 442120

PIACENZA 101778 289875

PORDENONE 51461 315323

REGGIO EMILIA 170420 530343

ROMA 2724347 4194068

ROVERETO 37071 Prov. Trento

ROVIGO 51872 247884

SALERNO 140489 1109705

TORINO 900569 2251276

TRENTO 114236 529457

TREVISO 82206 888249

UDINE 99071 541522

VENEZIA 270098 863133

VERONA 265368 920158

9.283.927 25.832.388

High architectural standards for HSR stations

•New HSR station were designed by world-level architects chosen through international competitions •Existing HSR stations were refurbished (e.g. Roma Termini, Milano Centrale, Napoli Centrale)

THE ITALIAN HIGH SPEED RAILWAYS (HSR) PROJECT

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the new station “Roma Tiburtina” (opening in 2012)

Roma

Tiburtina Roma

Termini

HSR

Firenze

Design: Paolo Desideri (ABDR- roma)


Napoli

The bypass of Bologna node (opening in 2012)

and The new station “Bologna Centrale” (expected opening in 2013)

RAILWAYS GALLERIES

Artificial Single track (x2) Double track

Design: Arata Isozaki + Ove Arup & partners


Bologna HSR node

Verona Padua Venice

Milan

Firenze

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The new station “Torino Porta Susa” (opening in 2013)


Design: AREP with S. d’Ascia e A. Magnaghi Architects

Undergoing projects the new station Napoli “Afragola” (expected opening in 2014)

Design: Zaha Hadid Architects

Roma

Salerno


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Undergoing project the bypass of Firenze node

The new station Firenze “Belfiore” (expected opening in 2015)

Design: Norman Foster + Ove Arup & partners

HSR Urban Galeries

• April 2012 Incoming of the new private HSR operator (“Nuovo Trasporto Viaggiatori”, NTV) competing with the national incumbent TRENITALIA

OPENING OF HSR MARKET TO COMPETITION

While plans for new high-speed rail (HSR) in the US are mired in controversy, in Europe, both private and public sectors are showing renewed interest in HSR. Italian company NTV has launched Europe’s first majority privately owned HSR service, …

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NTV shareholders

NTV partner Holdings Shareholders of NTV partner

Totale MDP Holding 33,5% Della Valle - Montezemolo - Punzo

(equal holding)

IMI Investimenti 20,0% Intesa SanPaolo

VFE-P SA 20,0% SNCF

Generali Financial Holdings FCP-FIS 15,0% Generali

Nuova Fourb 5,0% Bombassei

MaIS Spa 5,0% Seragnoli

Reset 2000 1,5% Sciarrone

Total 100,00%

ONE YEAR OF COMPETITION

• April 2013 “DA ZERO AD ITALO “ a book edited by NTV describing the first year of competition on the High Speed Rail network

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HSR supply growth (2009-2013)

CONTENTS


services


• Conclusions

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THE METHODOLOGY FOR HSR DEMAND FORECASTING: Elastic demand multimodal scheduled-based assignment model

Demand growth model

Future supply

scenarios

Schedule-based

mode choice

model

(by purpose

and user class)

Current OD

matrices

(corrected by

traffic counts)

Trend

OD matrices

(all modes)

by time slice

Induced

demand model

Future OD

matrices

(HSR)

Desired Departure Time (DDT) demand

model

Multimodal

Supply Models

(diachronic

networks)

Flows on

individual

trains

LOS

attributes

Schedule-based

Network loading

model

Path Flows on

the HSR

network

SOME REFERENCES

• Cascetta E., and Coppola P. (2011) “High Speed Rail Demand: Empirical and Modeling Evidences from Italy” Proceedings of the European Transport Conference 2011 (ETC), Glasgow, UK.

• Cascetta E., and Coppola P. (2012) “An elastic demand schedule-based multimodal assignment model for the simulation of high speed rail (HSR) systems” EURO Journal of Transportation and Logistics, Springer pp- 3-28 - ISSN 21982 4376

• Cascetta E., Coppola P., and Velardi V. (2013) “High Speed Rail demand: before and after evidences from the Italian market” disP – The Planning review. ETH, Zurich. (forthcoming)

• Cascetta E., Coppola P. (2013) “Competition on fast track: an analysis of the first competitive market for HSR services” forthcoming at the next 16th Meeting of the EURO Working Group on Transportation, 3-4 September 2013, PORTO (Portugal)

• Cascetta E., Coppola P. (2013) “High Speed Rail (HSR) induced demand models” forthcoming at the next 16th Meeting of the EURO Working Group on Transportation, 3-4 September 2013, PORTO (Portugal)

Demand_Growth.ppt

Mpdal_Choice_Calibration.ppt

Induced_ Demandl.ppt

Mpdal_Choice.ppt

Mpdal_Choice.ppt

Mpdal_Choice_Calibration.ppt

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THE METHODOLOGY FOR DEMAND FORECASTING

Supply models

220 zones: -each Province in the catchment area split into two zones (i.e. the main city and the rest of the province)

- the regions Abruzzo, Molise, Trentino-Alto Adige and Valle d’Aosta one zone

- the main Italian cities (Rome, Milan, Naples, Turin, Florence, Bologna) cities split into multiple zones


Supply models Zoning example: the 13 zones of the city of Rome

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Supply models the road graph the railways graph

1900 nodes 7000 links (representing 35000 Km)

2600 nodes 5500 links (representing 14500 Km)


The services simulated using a diachronic network includes: - 800 daily domestic flights between major Italian airports - the following railway services:

• 111 High-Speed and Eurostar trains; • 232 intercity trains; • 4.466 interregional and regional train

The diachronic network consists of: - 126.526 nodes - 329.657 links

Supply models

LINE-BASED APPROACH

Terminal A Terminal B Terminal C

RUN-BASED APPROACH

Terminal A

Terminal B

Terminal C

IC634

IC640

IC640IC741

tim

e

space

spac

e

IC741

12.35

12.37

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(diachronic network) alternative paths generation model

Hp 1: choice set includes the run before and the run after the Desired Departure Time (DDT)

AIR HSR Trenitalia HSR NTV

Coverage ratio 77% 64% 59%

Hp 2: choice set includes all the runs departing in a time window of a given length

Hp 3: choice set includes all the non-dominated runs w.r.t. travel time, cost and early/late penalty • Coverage ratio = 98% • The same coverage ratio would require

300% more paths with the time-band path generation model

The mode-service-run choice model

Nested logit models 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, NTV vs. High Speed Trenitalia (AVTR).

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25

SURVEY AND COUNTS BETWEEN 2009-2013

May 2009 : RP-SP survey + passengers counts October 2009 : passengers counts May 2010 : passengers counts October 2010: RP-SP survey + passengers counts May + October 2011: passengers counts May + October 2012: MO.VI. Panel + passengers counts May 2013: passengers counts (undergoing)

↘OD-estimation + Frequency-based model specification

↘OD-updating

↘OD-updating

↘OD-estimation + Schedule-based model specification

↘OD-updating

↘OD-updating + Trip-frequency model specification

BEFORE AFTER

December 2009

The schedule-based mode-service-run choice model: RP-SP survey

The RP data contacts recruited at home, stations and airports

# observations

Airports 385

Stations 1633

sampled at

home from the

population of

travelers

1332

cities with

HSR stations 954

Air 79

Train 166

Auto 709

other cities 369

Air 23

Train 44

Auto 302

Total 3341

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The mode-service-run choice model: RP-SP survey

The SP survey 445 contacts gathered via web with 6 games per respondent to test

• fare levels • L.o.S. attributes (travel time + access/egress) • Run departure time • New HSR operator (i.e. NTV)

# scenarios

completed

#

respondents %

#

observations %

1 82 18% 82 4%

2 20 4% 40 2%

3 11 2% 33 2%

4 6 1% 24 1%

5 3 1% 15 1%

6 323 73% 1938 91%

Total 445 100% 2132 100%

Source: Cascetta E., Coppola P. (2012) ‘An elastic demand schedule-based multimodal assignment model for the

simulation of high speed rail (HSR) systems’, European Journal of Transportation and logistics, DOI: 10.1007/s13676-012-0002-0

The mode-service-run choice model: estimation results All Business Other purposes

Value (t-test) Value (t-test) Value (t-test)

Access Egress time (min) -0,00805 (-5,25) -0,00555 (-2,13) -0,0103 (-5,09)

travel time (min) -0,00700 (-4,89) -0,0133 (-5,82) -0,0054 (-1,51)*

Early schedule delay (min) -0,00467 (-4,52) -0,00188 (-1,39) -0,00677 (-4,13)

Late schedule delay (min) -0,00917 (-19,27) -0,0130 (-16,2) -0,00617 (-10,90)

Cost by auto if travel in group (Euro) -0,0292 (-4,52) -0,0524 (-2,69) -0,0295 (-2,08)

Cost by auto if travel alone (Euro) -0,0386 (-5,56) -0,0527 (-3,76) -0,0405 (-4,14)

Cost by Intercity if reimbursed (Euro) -0,0247 (-1,88) -0,0158 (-2,73) **

Cost by Intercity if NOT reimbursed (Euro)

-0,022 (-5,13) -0,0377 (-3,50) -0,0172 (-3,45)

Cost by HSR if reimbursed (Euro) -0,00857 (-1,42)* -0,0120 (-4,11) **

Cost by HSR if NOT reimbursed (Euro) -0,0156 (-10,90) -0,0284 (-11,1) -0,00256 (-2,04)

Cost by Air if reimbursed (Euro) -0,0079 (-1,48)* -0,0109 (-1,91) **

Cost by Air if NOT reimbursed (Euro) -0,0150 (-7,15) -0,0201 (-5,65) -0,0194 (-5,21)

Dummy Male (1/0) +0,406 (1,91) +1,400 (2,41) +0,550 (2,28)

Dummy priority gate at the airport (1/0) +0,194 (0,97)** +0,242 (1,97) **

Rho-square 0,154 0,161 0,218

Adjusted rho-square 0,150 0,153 0,209

Final log-likelihood -5341,875 -3006,045 -2137,475

Initial log-likelihood -6317,116 -3583,503 -2733,613

Sample size 1884 1067 817

* significant at level of 85%; ** parameter not statistically significant

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Values of time

Business Other

access time/travel time 0,8 2,1

VOT (converted in Euro/h) 59,7 21,2

Values comparable with those found in the literature, e.g. a study on HSR in Japan (Yao et al. 2005)

The mode-service-run choice model: estimation results

All purposes Business Non-Business

Cost by auto if travel in party (Euro) 14,4 15,2 11,0

Cost by auto if travel alone (Euro) 10,9 15,1 8,0

Cost by Intercity if reimbursed (Euro) 17,0 50,5

Cost by Intercity if NOT reimbursed (Euro) 19,1 21,2 18,8

Cost by HSR if reimbursed (Euro) 49,0 66,5

Cost by HSR if NOT reimbursed (Euro) 26,9 28,1 12,7

Cost by Air if reimbursed (Euro) 53,2 73,2

Cost by Air if NOT reimbursed (Euro) 28,0 39,7 16,7

MODEL VALIDATION: Scatter diagram of assigned and counted flows HSR line sections Single train sections

counted flows counted flows

Ass

ign

ed

flo

ws

Ass

ign

ed

flo

ws


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MODEL VALIDATION: Scatter diagram of counted flows in May and October 2010

October (counted flows)

May

(co

un

ted

flo

ws)


THE INDUCED DEMAND

A significant induced demand effect of HSR services has been observed using retrospect before and after surveys and simulations (see later): - Rome-Naples link (2008) - NTV network (2012)

DIVERTED

DEMAND

from other modes e.g. shift from air/auto to

HSR

endogenous

factors from other rail services

e.g. shift from Intercity to

HSR

INDUCED

DEMAND

direct

e.g. increase of trip

frequency, change of trip

destination

indirect

e.g. increase of mobility due

to change in life-styles and

land use exogenous

factors

DEMAND

GROWTH

e.g. increase of mobility due

to economic growth

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Results from the national panel MO.VI. (Pragma, 2012)

• 8.067 interviews related to trips over 50 Km

• information gathered : Individual Socioeconomic characteristics (gender, education, professional

activity, etc. etc. )

Characteristics of the trips (O-D, mode, purpose, trip-frequency,…)

THE INDUCED DEMAND

The induced demand model

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Trip frequency model (“directly” induced demand)

Trip frequency model (“directly” induced demand) (1/2) Models estimates (using Max-likelihood method)

Parameters Unit Business Non-Business

a) b) c) a) b) c)

b_job1 100.000 -0,160* -0,134 -0,148 -0,203 -0,141 -0,140

(1,77) (-1,98) (-1,95) (-2,87) (-2,02) (-2,01)

b_job2+ 100.000 -1,34 -2,30 -1,96 -0,919 -1,49 -1,44

(-4,15) (-5,75) (-5,60) (-3,00) (-4,61) (-4,52)

b_degree1 1/0 0,49 0,498 0,580 0,480 0,509 0,508

(4,08) (4,14) (4,22) (5,69) (6,01) (6,00)

b_degree2+ 1/0 1,04 1,02 1,04 1,04 1,06 1,06

(5,17) (5,06) (5,17) (4,61) (4,68) (4,68)

b_Employee_high1 1/0 0,307* 0,338* 0,348* -0,201** -0,134** -0,136**

(1,74) (1,82) (1,84) (-0,53) (-0,35) (-0,36)

b_Employee_high2+ 1/0 0,985 1,10 1,10 0,950* 1,10* 1,09*

(1,93) (2,13) (2,15) (1,60) (1,86) (1,84)

b_male1 1/0 0,497 0,503 0,501 0,283 0,290 0,289

(4,83) (4,88) (4,86) (3,87) (3,95) (3,94)

b_male2+ 1/0 1,30 1,31 1,30 1,13 1,14 1,14

(6,47) (6,50) (6,47) (5,11) (5,13) (5,12)

b_logsum1 min 5,04 1,64 4,76 4,37

(21,82) (4,11) (37,29) (17,69)

b_logsum2+ min 7,29 2,90 6,33 4,64

(19,29) (4,87) (15,36) (6,04)

b_Threshold_min120_1 1/0 1,89 0,261

(13,11) (1,87)

b_Threshold_min120_2+ 1/0 2,97 1,12

(10,60) (2,59)

b_asc0 1/0 8,95 9,31 10,2 11,5 11 10,9

(-46,31) (-45,69) (-39,52) (30,36) (-38,01) (-36,89)

b_asc1 1/0 1,34 1,48 2,12 3,30 3,02 2,94

(-6,29) (-6,66) (-7,66) (8,49) (-10,08) (-9,64)

Init log-likelihood -611726,0 -611726,0 -611726,0 -612251,1 -612251,1 -612251,1

Final log-likelihood -4163,4 -3918,2 -3789,5 -6766,1 -6047,4 -6042,3

Number of observations 556817 556817 556817 557295 557295 557295

* statistically significant at level of 90%; ** parameter not statistically significant

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Trip frequency model (“directly” induced demand) (2/2) Models estimates (using Max-likelihood method)

Cascetta E., Coppola P. (2013) “High Speed Rail (HSR) induced demand models” forthcoming at the next 16th Meeting of the EURO Working

Group on Transportation, 3-4 September 2013, PORTO (Portugal)

Result consistent with travelers daily activities time-constraint s

Parameters Unit Business Non-Business

d) d)

b_job1 100.000 -0,184 -0,134

(-1,90) (-1,96)

b_job2+ 100.000 -1,87 -1,31

(-5,18) (-3,96)

b_degree1 1/0 0,528 0,520

(4,39) (6,16)

b_degree2+ 1/0 1,07 1,08

(5,31) (4,76)

b_Employee_high1 1/0 0,337 -0,165

(0,82) (-0,43)

b_Employee_high2+ 1/0 1,07 1,03

(2,08) (1,74)

b_male1 1/0 0,504 0,291

(4,89) (3,98)

b_male2+ 1/0 1,30 1,14

(6,48) (5,13)

b_time 1 (Tmin < 120) min -0,00248 -0,00192

(-1,98) (-2,09)

b_time 2+ (Tmin < 120) min -0,00974 -0,00324*

(-2,44) (-1,67)

b_time 1 (Tmin > 120) min -0,0173 -0,0185

(-16,29) (-23,77)

b_time 2+ (Tmin > 120) min -0,0345 -0,0319

(-8,38) (-8,01)

b_asc0 1/0 5,90 6,89

(16,86) (13,18)

b_asc1 1/0 0,108** 1,84

(0,81) (3,33)

Init log-likelihood -611725,9 -612251,1

Final log-likelihood -3722 -6824,7

Number of observations 556817 557295

* statistically significant at level of 90%; ** parameter not statistically significant

APPLICATIONS OF THE DSS

Monitoring of intercity Italian market (see later) Design of strategic policies Services and Rolling Stock Fares Design of operational policies Timetable

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APPLICATIONS

Example of strategic policies : fares

HSR Service supply (invariant)

Ref. Scenario Alternative scenario 1

TrainKm/day SeatKm/day

(mil.) 1st class 2nd class

paxKm (mil.)

% 1st class 2nd class paxKm (mil.)

delta% %

Trenitalia 78.162 43,9 Base Base 7.128 66,6% -20% -20% 8.066 13,2% 71,5%

NTV 35.238 15,9 base base-8% 3.569 33,4% = = 3.209 -10,1% 28,5%

10.697 100,0% 11.275 5,4% 100,0%

HSR Service supply (invariant)

Ref. Scenario Alternative scenario 2

TrainKm/day SeatKm/day

(mil.) 1st class 2nd class

paxKm (mil.)

% 1st class 2nd class paxKm (mil.)

delta% %

Trenitalia 78.162 43,9 Base base 7.128 66,6% -20% -20% 7.715 8,2% 67,2%

NTV 35.238 15,9 base base-8% 3.569 33,4% -20% -26,6% 3761 5,4% 32,8%

10.697 100,0% 11.476 7,3% 100,0%

APPLICATIONS

Strategic policies tested: fares

MODEL ELASTICITIES direct elasticity of total HSR demand with respect to HSR fares : -0,37

cross elasticity of individual HSR operator w.r.t. fares of competing HSR operator: +0,74

OBSERVED ELASTICITIES Direct elasticity of total travel demand w.r.t. fares (in 2012) included in a range between -0,30 and -0,40

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CONTENTS


services


• Conclusions

Trends in National demand 1990-2009: before HSR

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Trends in National demand 2009-2012: after HSR

Trends in National demand 2009-2012: after HSR Focus on the HSR “core area”

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HSR demand growth between 2009-2012

46

Avg. working day HSR load factors on the main OD pairs (Survey estimates Oct 2011 vs. Oct 2012)

pas

sen

gers

/day

)

Data not including Roma-Milano direct service (Fast)

2012

2011

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INDUCED DEMAND VS. DIVERTED DEMAND 1. Application of the national demand trends by mode to the core area

OD flows to obtain projected demand by mode in the core area

2. Difference between observed demand in the core area and the projected demand on mode “m”(as in step 1) diverted demand from mode “m” to HSR

3. Induced demand: total observed HSR demand – sum of diverted demand from other modes/services (as in step 2)

HSR demand growth between 2009 and 2012: induced vs. diverted components

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Modal shares between 2009-2012

Focus on a specific OD pair (i.e. Rome-Milan trips)

76,8%

1,9%

5,1%

16,3%

yes defintely yes partly not at all

HSR IMPACTS ON PEOPLE LIFESTYLES

Source: Mo.Vi. Panel (Pragma, 2012)

“Would you say HSR did influence your decision of moving

residence/workplace location ?”

«In the last two years (2011-12) did you move your residence/workplace location?»

76,8%

23,2%

no

yes

Estimates for HSR users

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HSR IMPACTS ON MOBILITY FOR TOURISM The percentage of “itinerant” tourists in Italy has grown in the last years, conversely to “non-itinerant” tourists (Pragma survey, 2013)

Non-itinerant

itinerant

Mill

ion

of

to

uri

sts

This phenomenon is stronger in regions served by HSR

The effects of competition on HSR (1/4)

• More flexible fare structure : different fare rules multiple ticket price-point for each rule

2009-2010 2011 2012

BASE BASE BASE

tariffs mini Economy

Super-Economy /Low-Cost

• More choice of on-board level of service:

2009-2010

1st class Executive /Club

classes 2nd class Business /Prima

Premium

Standard

2011-2012

/Smart

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• Reduction of the average HSR price

• New services on-board and at the station


new services

entertraiment (cinema, TV news ,…)

silent area

limousine service available at the station

local transport reservation of parking place at the station

Integrated local public transport and HSR ticket

tourism deals and discounts with hotels, museums, and other local providers

on board

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MI

TO

VE

FI

RM

NA

SA

FG

BA

RC

BZ

UD

AN

Trenitalia

NTV

Servizi futuri su rete tradizionale

BS

VR

PD

BO

Servizi AV su rete AV

Servizi AV su rete tradizionaleTrenitalia

NTV

NTV

• New HSR connections


HSR services on HSR lines

HSR services on traditional lines

New HSR services

CONTENTS


services


• Conclusions

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Conclusions

HSR services impacted very significantly on the Italian passengers market

• Shifts from air and car to HSR • Significant generated demand

Competition in the HSR market has been positive so far • increase in trains frequency • reductions in average prices • increases in service quality

Surveys and models were essential in monitoring and designing HSR services

• schedule based elastic demand assignment models • trip frequency models with temporal thresholds

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