Analysis and Use of Evidence in DfT

Transcription

1 Analysis and Use of Evidence in DfT Presentation at the Centre for Transport Studies, Imperial College & University College London 26 th November 2012 By Dr Preetum Domah Head of Rail Demand Forecasting and Analysis and Dr Patricia Idaewor Rail Commercial Directorate Department for Transport

2 The strategic context for government Legal & regulatory framework Government policy Parliament Stakeholders Government resources Range of values produced

3 DfT Vision An engine for economic growth A greener transport system A safer transport system Devolving control and empowering others

4 Department for Transport Determines overall transport strategy Provides leadership across the transport sector to achieve its objectives Manages relationships with Agencies responsible for the delivery of its vision Partners with regional/local bodies and the private sector to deliver many of the services Regulates, and in some cases licenses, operations across all the modes in the UK

5 Rail Industry Structure RSSB Overarching Safety Regulator Direct Agreements DfT Franchise Agreement Funding Regulation ORR Regulation Agree Industry Plan ROSCOs Lease Operator Contract Infrastructure ATOC Passenger Rail Services Passenger Focus

6 Significant challenge: increased transport demand

7 Government Decision-making Significant challenges in providing transport. Diverse user needs, Limited resources, Technological constraints, Multiple modes, etc. DfT needs to decide what policy to adopt, where to invest and how much. Decisions need to be supported by evidence. Evidence is usually determined through analysis and surveys/consultation.

8 Role of Analysts in DfT Analysts provide good quality advice to inform policy formulation and investment decisions. They ensure best evidence is used in determining policy thrust. They enable the translation of policy into affordable delivery to ensure maximum impact of allocated funding. They provide the justification for government intervention.

9 Analysts in DfT Analysts in the Department include: Economists Transport Modellers Statisticians Operations Researchers Qualitative Analysts (Policy developers)

10 Use of Evidence in DfT Policy formulation. Investment decisions. Agent Management. Why? Principal-Agent (P-A) Problem How? Collaboration between analysts and technical specialists (franchise managers, sponsors, etc). Outcome? Ensure a beneficial contractual agreement minimising impact of the information asymmetry inherent in the P-A problem.

11 Theory of Incentives Incentives theory emerges with the division of labour and exchange. Division of labour induces delegation. Delegation necessitates contracts and the parties responsibilities and rewards. Delegation is needed both within organisations and without (outsourcing).

12 The Principal Agent problem The difficulties in motivating one party (agent), to act on behalf of another (principal). Both parties have different interests and agents have more information (i.e. the asymmetric information effect). E.g. corporate management (agent) vs shareholders (principal); dental patient (principal) vs his dentist (agent) Is the recommended treatment really necessary for the patient or just an income generator for the dentist? Thus Principals cannot directly ensure that Agents are always acting in their best interests, especially when activities useful to the principal are costly to the agent, and where it is costly for the principal to observe the agents actions.

13 Rail Commercial Directorate Train operating companies (Agents), on behalf of DfT (the Principal), provide rail travel opportunities to the public for an incentive. RC manages the relationships to minimise the impact of the P-A problem and hence: Ensure VfM for taxpayers, and protect passengers interests. Ensure delivery of franchise outputs wrt performance, financial returns and committed obligations. Enhance the value of the contracts by delivering new Government policies through in-contract negotiations.

14 Rail Contract Management 17 rail contracts: 14 commercial Franchise Agreements 1 Franchise Agreement based on shared responsibility (Arriva Trains Wales Welsh Assembly Government) 1 Service Agreement under Section 30 of the Railways Act (East Coast) - DOR 1 Concession for High Speed One infrastructure

15 3 Case Studies HLOS Focus: Incremental Investments in Rail Fares Review Focus: Incremental : Policy change as an alternative measure HS2 Project Focus: Large policy intervention Modelling and Analysis required in each of these three case studies. 15

16 Investing in Britain s Railways Photo: The Guardian, Thursday 8 March, 2012.

17 1. High Level Output Specification Railways Act 2005 Control Period 5: Demand growth of 16% Costed by Office of Rail Regulation (ORR) Delivered by Network Rail and through franchises HLOS sets out the railway the SoS wishes to buy over the forthcoming CP. Includes: Capacity increases (new rolling stock and efficient cascade of existing stocks) Network Improvements (electrification, improve links to major ports, enhance major stations to accommodate longer trains) Achieve crowding, environmental, reliability and safety targets.

18 Modelling and Appraisal of HLOS (Process) Identify stress or problem areas -Load Factors -Other Metrics -Location Generate Options -Train lengthening -Changing service frequency Code and Test in NMF -Load Factor Changes -Other Metrics -VfM Iterate until satisfactory outcome is reached Markets were segmented London and South East Intercity, and Regional generated by rail technical experts Helped by analysis and appraisals conducted by TfL and NR.

19 National Modelling Framework Strategic - covering the whole network, but allows examination of local issues such as loading and overcrowding Estimates HLOS metrics/outputs (including value for money) Demand modelled on c.638 demand zones. Smaller stations grouped with principal stations. Network/infrastructure split into 300 sections reflecting route classification, funding boundaries and where there are significant changes in the type or volume of traffic on the network. Crowding modelled by crowding cost, to constrain or unconstrain demand. Model incorporates demand forecasting tool, cost model and emissions model.

20 Rail Demand Modelling Comprehensive station to station ticket based database. Exogenous/endogenous drivers of demand in base and forecast year; elasticities for each driver/demand category. Assignment to trains modelled using time profile and generalised time. Crowding is key policy issue can be modelled in detail by iteration or at a more strategic level.

21 NMF Demand Module Current Ticket Sales Data Time Profile Future growth due to changes in exogenous factors and changes in fares Future Rail Demand Assignment to Trains Crowding functionality Future constrained passenger demand growth Future train service changese Starts from current rail patronage from A to B using ticket sales data Exogenous growth from A to B driven by forecasts of drivers (employment, GDP) and relevant elasticities Allocation to trains via assignment model- best train in the timetable for the passenger Crowding off curves Developed for each TOC National level model small stations amalgamated with big ones

22 Principles of Rail Demand Forecasting Base demand, demand drivers and elasticities PDFH Industry-agreed assumptions and parameters D D t b GDPt GDPb Pop Pop t b 1 Empl Empl t b Fare Fare t b GJT GJT t b CarTime CarTime t b etc. Exogenous drivers employment growth, GDP, changes in competition from other modes Endogenous drivers changes in journey times, train capacity, frequency Elasticities vary by journey purpose, region, journey length etc.

23 Crowding Formula Change in demand is derived from time crowding penalties (added to journey time) Change in demand due to the additional journey time is C D U D where ε = journey time elasticity (-0.4 or -0.7) J = journey time α(clf) = crowding penalty CLF = D C /S D C = constrained demand S = seats (12,100) D U = unconstrained demand (22,000) J Solving the equation above yields D C =16,000, which means that ca. 6,000 pax are crowded off CLF J J

24 Modelled Output (Capacity Metrics)

25 2. Fares and Ticketing Review

26 Key Facts Review launched March 2012 Previous review 2003, by the Strategic Rail Authority Significant changes in the market for travel Advance purchases and single leg pricing Potential distortions due to previous pricing rigidities e.g. Price cap regulation and historic price setting, geographical imbalances Passenger complaints on complexity offsetting regulation Establishing better ways to regulate fares

27 Regulation of Fares and Ticketing - 1 Ticketing regulation refers to the industry-wide agreements which all train operators (including non-franchised, open access operators) are required to adhere to as a condition of their operating licence issued by the Office of Rail Regulation, as well as some clauses in franchise agreements, to ensure rail services continue to operate as an integrated network despite the existence of 21 different train operators. (Fares and Ticketing Review Consultation Document para. 6, p.14, 2012).

28 Regulation of Fares and Ticketing - 2 The statutory basis - section 28(2) of the Railways Act Reasonable fares - in determining what is reasonable she may take into account the interests of rail users and potential rail users and the financial situation including the amount of funding required to operate, maintain, renew and upgrade the railway; and to promote the use of services of more than one train operator, in general and, specifically, by protecting throughticketing.

29 Economic Rationale for Regulation To prevent or minimise potential abuse of monopoly power of rail service providers. To allow or encourage allocative and technical efficiency in supply of rail services e.g. Peak demand management. Allow technical innovation in fares to further improve capacity utilisation e.g. Smart ticketing. Equity distributional impacts, and fairness including a complaints handling mechanism. Minimise transaction costs e.g. through ticketing.

30 Pricing as a Method of Efficient Resource Allocation As opposed to HLOS, pricing can also help manage demand. Issue is with understanding passenger behaviour. Fares elasticities: Fare Elasticities PDFH5 Revisiting Elasticity LSEE-LSEE (Seasons) LSEE-LSEE (non-seasons) LSEE-non-London core cities (Full) LSEE-non-London core cities (Reduced) Source: Revisiting Elasticity-Based Framework (see references at end of slide pack)

31 Issues of Relative Inelasticity of Demand in the Peak (source: Department for Transport, Melbourne) Morning peak = 8:00-9:00 hours Afternoon peak = 17:00-18:00 hours (but flatter) Peak demand drives most rolling stock costs Peak demand is associated with demand inelasticity

32 Analytical Challenge Elasticity-based analysis (own and cross price elasticities) analysis of potentially large fares changes Econometric estimation and forecasting problems Additional evidence on passengers behaviour (SP and other research) Quantification of some aspects of behaviour (complexity of fares structure, impact of zonal fares, new tickets) Model uncertainty and risks and impact on quality of advice Nature of regulation in baskets of fares and practicality of alternative methods of regulation

33 3 High Speed Rail Analysis 33

34 HS2 Route Map 34

35 Service specification assumptions for HS2 (London West Midlands)

36 The Y - Network

37 Key Facts Phase 1 Construction begins 2017 Completion of Phase Capital Cost 18.8 bn Total Cost 26.9 bn Total Benefits 23.6 bn Wider Economic Benefits 4.8 bn Total Length 140 miles Maximum Design Speed 250 mph Jobs 30,000 Y - Network Completion 203? Capital Cost 36.4 bn Total Cost 58.7 bn Total Benefits bn Wider Economic Benefits bn Total Length 330 miles

38 Economic Case for Phase 1 London to West Midlands

39 Economic Case for the Y- Network Summary of Costs and Benefits 39

40 Modelling and Analytical Issues Modelling of large changes in infrastructure and service provision (compared to HLOS) PLANET modelling suite a hybrid of elasticitybased model with choice modelling Demand estimation for new lines no real base matrix to start with To cap demand or not? Cost forecasting and optimism bias? Quantification of wider economic benefits

41 References HLOS Updated Economic Case Interactive Map of the HS2 Route Fares and Ticketing Review Consultation Document Revisiting the Elasticity Based Framework (this is the document referred to earlier in the slides on elasticities).

42 Questions?

43 Contact