Kristofferson, Ida

Kurt, Filiz

Meyer zu Hörste, Michael

Institute of Transport Research:Publications

Flex-Rail Final Results Dissemination WebinarThe IMPACT-2 model for Shift2RailMichael Meyer zu Hörste, DLRFiliz Kurt, DLRIda Kristoffersson, VTI30th June 2021Overview of the IMPACT-2 model of Shift2Rail LCC modelCapacity modelPunctuality modelMode choice modelCustomer experience modelKPI modelSource: www.shift2rail.org Overview of the IMPACT-2 model of Shift2Rail 1. Quantitative KPI model• Strict focus on technological innovations• Consequent percentages used• Target is the maximum achievable improvement as a priority for the respective KPI• Based on generic scenarios2. Customer Experience • Focus on Areas of Major Potential for Improvement i.e. improving attractivity of the Rail System• Based on feedback from customers3. Mode-Choice model  • Focus on the increased use of the Rail System • Based on real ScenariosRelation of the IMPACT-2 model of Shift2Rail KPI modelMode choice modelSUSTAINABLE Remove barriersfor passengersKPI Life-Cycle CostKPI CapacityKPI ReliabilityIP4 « IT solutions »IP2 « CCS »IP1 « Rolling stock »CUSTOMER EXPERIENCEMODAL SHIFTIP3 «Infrastructure»Customer experience modelInternal structure of the KPI modelInput parameters Models for LCC, punctuality & capacity ResultsSheet 7: LCCSubsystemSheet 8: UnreliabilitySubsystemSheet 9: Capacity_SubsystemSheet 10:LCCSubsystem_FreightSheet 11: Unreliability_FreightSheet 12: Capacity_FreightSheet 3: InputParametersSheet 4: DistributionSheet 5: ImprovementsSheet 6: OverviewSheet  1: Cover & HistorySheet  2: SPDParametersSheet 13: DecisionsSheet 14: SourcesInternal structure of the KPI modelSelection of SPDOverall resultsResults w.r.t. to the IP-specific part of the baselineResults w.r.t. to the IP part of the overall baselinerollingstockcommand & control systemsinfrastructure freightSource: www.pixabay.comKPI-Input for Mode choice model - Operational cost- Track cost- Train (load) capacity- Maximum usable (track) capacityAverage delay minute per train- Information- Booking & Ticketing- Comfort & ServiceIMPACT-2 Mode choice modelling and results• Predefined set of alternatives: e.g. air, car, bus, rail• Preference of an alternative quantified in the utility function:Passenger mode choice models are based on theory of discrete choice9𝑈𝑟𝑎𝑖𝑙 = 𝑉𝑟𝑎𝑖𝑙 + 𝜀𝑟𝑎𝑖𝑙= 𝐴𝑆𝐶𝑟𝑎𝑖𝑙 + 𝛽𝑟𝑎𝑖𝑙𝐼𝑛𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑇𝑖𝑚𝑒𝑟𝑎𝑖𝑙 + 𝛾𝑟𝑎𝑖𝑙𝑇𝑟𝑎𝑣𝑒𝑙𝐶𝑜𝑠𝑡𝑟𝑎𝑖𝑙 +⋯+ 𝜀𝑟𝑎𝑖𝑙𝑈𝑐𝑎𝑟 = 𝑉𝑐𝑎𝑟 + 𝜀𝑐𝑎𝑟= 𝐴𝑆𝐶𝑐𝑎𝑟 + 𝛽𝑐𝑎𝑟𝑇𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒𝑐𝑎𝑟 + 𝛾𝑐𝑎𝑟𝑇𝑟𝑎𝑣𝑒𝑙𝐶𝑜𝑠𝑡𝑐𝑎𝑟 +⋯+ 𝜀𝑐𝑎𝑟…• Assuming 𝜀 follows Gumbel distribution →Multinomial Logit model𝑃𝑐𝑎𝑟 =𝑒𝑉𝑐𝑎𝑟𝑒𝑉𝑐𝑎𝑟 + 𝑒𝑉𝑏𝑢𝑠 + 𝑒𝑉𝑟𝑎𝑖𝑙 + 𝑒𝑉𝑎𝑖𝑟• Only the end situation when all Shift2Rail innovations are realized is modelled – not the implementation path• Changes in population development, income etc. are not considered – the innovations are applied to today’s situation to isolate the effects of innovations• Only one corridor per SPD is considered• Only demand in the peak hour is modelled• Only one type of traveller is considered: an ”average” traveller• Total number of travellers (for all modes) is assumed to be constant• Congestion on the road network is not taken into accountAssumptions10Source: www.pixabay.com• To build the baseline mode choice models, we need: • Baseline demand • Service attributes: travel time, travel cost, average delay, customer experience variables (Booking & ticketing, information, comfort) etc.• Passenger valuations: value of time (Swedish, French and EEU Value of time sets), value of customer experienceBaseline mode choice models11• There exists supply constraints• Number of trains per hour is limited by the maximum usable track capacity• Number of passengers per train is limited by train seat capacity• Negative effects of crowding are captured by a discomfort factor (based on the load factor) Supply constraints12We assume operators will only adjust ticket cost and frequency:➢High-speed: operators maximize profit both in baseline and in future scenarios➢Regional and metro: Producer surplus is kept as in baseline and profit above that is used to decrease ticket prices and/or increase frequencyOptimisation1303/09/2021Important characteristics of the studied corridor• Busy corridor in a high-density area• Maximum usable track capacity reached already in baseline (12 trains/h)• Large share of long-distance rail already in baseline (24%)• Average delay small compared to corridor travel time• Main competing mode is private carSPD High-speed passenger railImprovements in S2R impact scenario – High-speed• Maximum usable track capacity increases substantially →important for operator’s decision regarding train frequency (running at full capacity in baseline)• Full deployment of high-speed S2R customer experience improvements assumed (100%)• Substantial reduction of average delay minutes (-35%) but delay minutes are small compared to in-vehicle travel time for the corridorInput data item UnitPercentage differenceAverage delay minute per trainmin -35%Train capacity seats/train +11%Maximum usable track capacitytrains/h +33%Operational cost €/train -6%Track cost €/train -16%Customer experience variablesNormalized to 1+100 %• Modal share• Significant effect of S2R innovations (rail modal share increases from 24% to 35%) • S2R scenario rail modal share does not depend on the value of time (VOT) assumptionsResults: High speed• Frequency in S2R impact scenario has reached improved maximum usable track capacity, which is the main driver (reduction of waiting time)• Customer experience improvements have substantial effects, but they are constrained by the maximum usable track capacity• Modest effects of delay reduction and reduced operational and track costsHigh speed – Which factors contribute the most?• Moderate and optimistic Automatedvehicles (AVs) scenarios• Moderate and optimistic Electric vehicles (EVs) scenarios• Assumptions on market share and changes in value of time and travel costfrom literature review• Only minor changes in assumptionsbetween high-speed, regional and metroAlternative future scenarios for AV and EV innovationData item Source Adopted values AV innovation Passenger valuations of peak hour average in-vehicle travel time for AVs Kolarova et al. (2018) [19] ; Correia et al. (2019) [20] Moderate 86% and Optimistic 73%  Passenger valuations of peak hour average access and egress travel time for bus Kolarova et al. (2018) [19] Moderate 84% and Optimistic 67%  Peak hour average access and egress travel time for bus Near2050 D5.3 (2018) [18];  CoExist D4.2 (2020) [29]  Moderate 100.5%  Optimistic 97%  Peak hour average in-vehicle travel time for AVs Milakis et al. (2017) [22]; Near2050 D5.3 (2018) [18]; CoExist D4.2 (2020) [29] Moderate 100.5% and Optimistic 97%  Peak hour average travel cost for AVs Milakis et al. (2017) [22];  Near2050 D5.3 (2018) [18];  Fagnant, et al. (2015) [24] Moderate 104% and Optimistic 75%  Market share of AVs Milakis et al., (2017) [22]  Moderate 40% and Optimistic 100%  Climate innovation Peak hour average travel cost for EVs Jensen et al. (2017) [26]; Bösch et al., (2018) [25]; Lutsey and Nicholas (2019) [27] Moderate 40% and Optimistic 20%  Market share of EVs Liu et al. (2017) [15];  Moderate 50% and Optimistic 100%   • Shift2Rail innovations are also present, results for Swedish value of time set• Moderate AV and EV innovation do not affect rail demand but lower ticket prices• Optimistic EV innovation wipe out the rail demand increase of S2RAV and EV scenario results – High-speed  Scenario nameRail mode share (%) Ticket price (€) Frequency Load factor Producer surplus (€)Consumer surplus (€)Baseline 24% 47 12 0.80 176760 0Shift2Rail35% 63 16 0.80 393771 31438(48%) (34%) (33%) (0%) (123%) /Moderate AV 35% 59 16 0.80 365955 111147(48%) (26%) (33%) (0%) (107%) /Moderate EV 35% 43 16 0.80 251006 440542(48%) (-8%) (33%) (0%) (42%) /Optimistic AV 29% 27 16 0.66 97432 881578(23%) (-43%) (33%) (-17%) (-45%) /Optimistic EV 17% 23 11 0.58 37906 1099185(-27%) (-52%) (-8%) (-28%) (-79%) /• Similar model type as for high-speed SPD, even though the alternative modes differ• Frequency much lower than maximum usable track capacity (capacity constrained only at some nodes)• Average delay minutes decreases substantially (-52%)• Significant effect of S2R innovations (rail modal share increases from 18% to 29-40% depending on the value of time (VOT) assumptions)• Already Moderate EV innovation reduce S2R rail demand increasessubstantially• Optimistic AV and EV innovation wipe out the S2R rail demand increasesSPD Regional• Similar model type as for high-speed SPD, even though the alternative modes differ• Frequency at maximum usable track capacity and is not increased by S2R innovations• Only minor effects of S2R innovations (rail modal share increases from 30% to 31%)• Inelastic SPD – Small demand changes also in Optimistic AV and EV scenariosSPD MetroSPD Freight - Modelling• KPI computations based on a generic corridor• Modal share computations are done over an entirenetwork (Sweden).• Network model: Samgods (cost-minimizing model)• We represent improvements in terms ofpercentages. • Evaluation: Tonnes-km on Swedish territory only(and territorial waters). Reason for this is thatflows over the Baltic Sea may cause untypicalresults for European conditions.• Very strong impact on modal shift by S2R innovations (rail modal share increases from 21% to 32-47% depending on capacity constraints on rail or not)• However, large variations for different commodity types.• Most important drivers are (probably): reduced operational costs, driving timeand max load capacity.• Assumptions that S2R improvements are done on the whole rail network may be too optimistic (?) • No improvements on sea have been considered. SPD Freight – ResultsBack upModelling Approach for the KPI scenariosBaseline scenarioFuture scenarioShift2Rail innovationsReference scenario:Source: www.shift2rail.org Source: www.pixabay.comModelling Approach per KPI1. LCC model• Capital and Maintenance cost of IP1, IP2, IP3, IP5 and Operational• IP-wise sum of cost share of TD in baseline in % and improvement by S2R innovations %2. Capacity model• Capacity calculation consist of three main parts:• Track Capacity (number of trains per peak hour / day)• Train Capacity (passenger / metric ton per train)• Coupling ability (coupled units per train)• For Passenger SPDs: Passengers in Peak Hour• For Freight SPD: Freight in 24h3. Punctuality model• Failure rates linked to delay minutes based on historic data• Reduction of Delay Sources in % by S2R InnovationsKPI-Input for Mode choice model LCC modelCapacity modelPunctuality modelKPI modelMode choice modelAverage delay minute per trainTrain capacity / Train load capacityMaximum usable capacity / maximum usable track capacityOperational costTrack costKPI-Input for Mode choice model Source: www.shift2rail.org S2R Customer Experience Variables29Booking and ticketing Information Comfort & servicesPersonalized booking Real-time information Train layoutIntegrated ticket systemTravel assistant Train noiseMultimodal shoppingInformation on ancillary servicesStation designSimple ticket(s) purchaseNavigation pre/during tripStation servicesOffer adapted to my needSupport in disruptionAMPIs related to IP1 & IP3AMPIs related to IP4Improvements in S2R impact scenario – Regional• Average delay minutes decreases substantially (-52%)• Large increase in maximum usable track capacity but has no effect• Full deployment of regional S2R customer experience improvements assumed (100%)Input data item UnitBaseline valueS2R impact scenarioPercentage differenceAverage delay minute per trainMin 6.9 3.3 -52%Train capacity seats/train 220 248 +13%Maximum usable track capacitytrains/h 14 20 +36%Operational cost €/train 444 377 -15%Track cost €/train 600 485 -20%Customer experience variablesNormalized to 11 2 +100 %• Modal share• Significant effect of S2R innovations (rail modal share increases from 18% to 29-40%) • S2R scenario rail modal share depend a lot on the value of time (VOT) assumptionsResults: Regional• The main drivers of increased rail demand for French and Swedish VOT are frequency increase (reduction of waiting time) and delay reduction.• The main drivers of increased rail demand for EEU VOT are customer experience innovations (but this is to some extent an artefact of the model)• Modest effects of increased train capacity and reduced operational and track costsRegional – Which factors contribute the most?• Shift2Rail innovations are also present, results for Swedish value of time set• Already Moderate EV innovation reduce S2R rail demand increases substantially• Optimistic AV and EV innovation wipe out the S2R rail demand increasesAV and EV scenario results – Regional Scenario name Rail mode share (%) Ticket price (€) Frequency Load factor Producer surplus (€) Consumer surplus (€)Baseline 18% 6.9 2 1.83 3225 0Shift2Rail37% 6.6 3 2.09 7613 10343(102%) (-5%) (50%) (14%) (136%) /Moderate AV 28% 6.4 2 2.41 5986 10926(55%) (-7%) (0%) (32%) (86%) /Moderate EV 23% 6.3 2 1.92 4269 27282(24%) (-9%) (0%) (5%) (32%) /Optimistic AV 14% 5.8 2 1.23 1817 55994(-21%) (-16%) (0%) (-33%) (-44%) /Optimistic EV 12% 5.5 2 1.03 1078 67077(-34%) (-20%) (0%) (-44%) (-67%) /Improvements in S2R impact scenario – Metro• Minor improvements in train capacity• No improvement of maximum usable track capacity which is an important constraints for this metro corridor• Full deployment of metro customer experience (CE) improvements assumed (100%), but low valuations of CE improvements for metro Input data item UnitBaseline valueS2R impact scenarioPercentage differenceTrain capacity seats/train 900 916 2%Maximum usable track capacitytrains/h 24 24 +/-0%Operational cost €/train 83 70 -16%Track cost €/train 60 54 -10%Customer experience variablesNormalized to 11 2 +100 %• Modal share• Minor effect of S2R innovations (rail modal share increases from 30% to 31%) • S2R rail modal share does not depend on the value of time (VOT) assumptionsResults: Metro• Only small rail demand increases across the different factors• Customer experience variables show somewhat larger effects than the other innovationsMetro – Which factors contribute the most?• Shift2Rail innovations are also present, results for Swedish value of time set• Inelastic SPD – Small demand changes also in Optimistic AV and EV scenariosAV and EV scenario results – Metro Scenario nameRail mode share (%) Ticket price (€) Frequency Load factor Producer surplus (€)Consumer surplus (€)Baseline 30.3% 1.68 24 0.85 27413 0Shift2Rail31.2% 1.65 24 0.86 28314 4450(3%) (-2%) (0%) (1%) (3%) /Moderate AV 31.1% 1.65 24 0.86 28133 7778(3%) (-2%) (0%) (1%) (3%) /Moderate EV 30.6% 1.65 24 0.84 27700 15869(1%) (-2%) (0%) (-1%) (1%) /Optimistic AV 29.2% 1.65 24 0.81 26283 43079(-3%) (-2%) (0%) (-5%) (-4%) /Optimistic EV 29.6% 1.65 24 0.82 26629 36318(-2%) (-2%) (0%) (-4%) (-3%) /Samgods: capacity constraints on rail• Computed train flows will exceed realisticlimits (capacities) on some rail linksunless restricted. • A special module has been developed in Samgods to redirect exceeding flows so that the capacity limits (# trains per day) are not exceeded.• Capacity limits have been estimated by the Swedish Transport Administration.• This module has significantly increasedthe computational complexity of the model.  Samgods: cost minimizing model• Starting point: transport demand(160 PC matrices)• Data originates from a commodity flowsurvey + import/export statistics.• End result:flows on a network• Plus everything that can be derived from the flows: tonne-kms, veh-kms, costs, load factorsetc. Improvements in S2R impact scenario – Freight”KPI innovations” ”Time reductions”Input data item UnitSingle wagon trainsBlock trainsCombi trainsAverage delay min/train -59% -59% -59%Max load capacity tonnes/train +20% +50% +70%Track capacity trains/day +5% +5% +5%Operational cost (energy)€/km -10% +20% +70%Operational cost (loco+wagon+labour)€/h -20% -10% 0%Track costs €/km -19% -19% -19%Process time type UnitSingle wagon trainsBlock trainsCombi trainsLoading/Unloading h -50% -50% -50%Shunting at orig&destterminals h -80% -80% -80%Wagon&brake tests h -80% -80% -80%Marshalling h -20% -50%Driving h -29% -33% -44%(Un/load+shunting+ wagon&brake tests)h -56% -56% -56%• Large effects of S2R innovations (rail modal share increases from 21% to 32-47% ) • S2R scenario rail modal share depend a lot on capacity constraints assumptions• The Samgods model has been calibrated for the “with constraints” case (so baseline results differ)Preliminary results: Freight – Modal share21%26%30%32%48%45%43%42%31%29%27%26%0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BaselineTime reduction innovationsKPI InnovationsAll innovationsModal share - with constraints on railRail Road Sea25%38%41%47%45%37%36%32%29%24%23%21%0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BaselineTime reduction innovationsKPI InnovationsAll innovationsModal share - no constraints on railRail Road Sea

The IMPACT-2 model for Shift2Rail

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The IMPACT-2 model for Shift2Rail

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Institute of Transport Research:Publications