Evaluation of supply quality in passenger transport as a basis for the assessment of railway infrastructure measures

Transcription

1 Evaluation of supply quality in passenger transport as a basis for the assessment of railway infrastructure measures DB Netz AG I.NVT 7 Univ.-Prof. Dr.-Ing. Andreas Oetting, Angela Rio presenter: Michael Rittner Stuttgart,

2 Outline Introduction & Requirements Empirical Methods Findings & Conclusions Outlook 2

3 Regarding future traffic growth the project FreeFloat 1 supports the objectives of DB Netz AG as well as the requirements of politics objectives of DB Netz AG enable traffic growth use investment ressources optimally defuse bottlenecks in the network make system reserves useable enhance punctuality requirements of politics manage the future traffic growth in an economically efficient and sustainable way strengthen competitiveness of the railway ensure an efficient and modern railway infrastructure strengthen the economic position of Germany FreeFloat 1 funded by: 3

4 State of the art: The evaluation of quality measures is limited example benefits of an infrastructure measure increase of capacity reduction of in-vehicle-times improvement of timetable quality improvement of operational quality transport demand models generally regard travel costs scheduled travel time scheduled travel time travel time travel costs travel costs travel time travel costs 4

5 negative valuation The model to be developed by Evalinfra provides the basis for an economic evaluation of the quality impacts of infrastructure measures Evalinfra project contents deduction of a quantitative relationship between the valuations of different components of supply quality integration into transport demand models delays scheduled in-vehicle-times example: delays benefits economic evaluation of quality improving measures (in particular delay reducing measures) comparison of capacity and quality measures by a common criterion identification of the most suitable set of measures to increase the attractiveness of the railway system revenues costs profit contribution consistent evaluation of measures along the value chain of the infrastructure manager 5

6 Transport demand models have to consider the basic characteristics of supply quality that can be influenced by the infrastructure manager impact of infrastructure measures on supply quality increase of capacity no impact on supply quality, economic evaluation by number of saleable trainpaths impact on supply quality reduction of in-vehicle-times reduction of in-vehicle-times improvement of timetable quality reduction of scheduled waiting times improvement of operational quality reduction of delays leading to - fewer waiting times at the originating station - fewer in-verhicle-times - shortened or extended changing times 6

7 Evalinfra is devided into 4 work packages (WP) WP 1 Survey freight transport WP 2 Survey passenger transport WP 3 Demand models for the valuation of supply quality WP 4 Implementation and application on a first example network planning (macro) network planning (micro) scheduling disposition 7

8 Outline Introduction & Requirements Empirical Methods Findings & Conclusions Outlook 8

9 A survey was conducted to gain more specific information on how passengers value different components of travel time method of the survey in passenger transport objective: deduction of a quantitative relationship between the valuations of different components of supply quality with focus on delays method: nationwide survey in long-distance and local passenger trains main survey period: October / November 2009 utilizable return: approx. 3,200 of 5,000 questionnaires (64%) survey content: - core of the questionnaire: conjoint measurement matrix (5 different, jointly analyzable matrices) - supplementary information on the customers perceptions and opinions - information on today s journey - basic information on the person different questionnaires for: - long-distance and local passenger trains - three travel distances (50 km, 130 km, 400 km) 9 example: conjoint measurement matrix delay at originating station waiting time caused by missed connecting train changing time travel costs delay at orig. station changing time waiting time caused by missed connecting train travel costs

10 (negative) standardized part worth utility conjoint analysis* Conjoint analysis was used to deduce a quantitative relationship between the valuations of different characteristics of supply quality method of analysis example: conjoint measurement matrix regression line utility attribute 2 marginal utility attribute 2 regression line utility attribute 1 delay at originating station waiting time caused by missed connecting train changing time travel costs marginal utility attribute 1 e.g. additional time * in the project conducted by Prof. H.E. Jochim, FH Aachen University of Applied Sciences, Fachbereich Bauingenieurwesen, Lehrgebiet Bahnanlagen und öffentlicher Verkehr; literature: Backhaus, K., Erichson, B., Plinke, W., Weiber, R.: Multivariate Analysemethoden; 10. Aufl., Berlin/Heidelberg delay at orig. station changing time waiting time caused by missed connecting train travel costs

11 Outline Introduction & Requirements Empirical Methods Findings & Conclusions Outlook 11

12 valuation of stop times in relation to in-vehicle-times [min] Passengers generally seem to value stops more negatively than in-motion times valuation of stop times Part worth utilities for in-vehicle-times and the number of stops both are represented very well by linear functions. An increasing marginal utility cannot be confirmed by the given results for both attributes. travel distance Passengers don t seem to distinguish between expected and additional in-vehicle-times. Passengers generally seem to value stops 1.5 to 2.5 times more negatively than in-motion times. 12

13 A valuation of about 2.5 to 3 for most travel purposes could be confirmed for delays on the train in relation to in-vehicle-times valuation of delays as a prolongation of in-vehicle-time The valuation of delays in relation to in-vehicle-times of JOCHIM [1] could be confirmed for delays that passengers undergo on the train: valuation by long-distance passengers on business or private trips between 2.5 and about 3 valuation by vacation travellers a little higher, yet at the same time scheduled in-vehicle-times are attached less value to by this group A valuation for long-distance commuters could be deduced, comparable very well to the valuation by business and private travellers. A weighting factor of 2.5 could be deduced for travel distances of less than 100 km, which can be used for mass passenger transport. [1] Jochim, H. E.: Verkehrswirtschaftliche Ermittlung von Qualitätsmaßstäben im Eisenbahnbetrieb; Veröffentlichungen des Verkehrswissenschaftlichen Institutes der RWTH Aachen, Heft 54 (1999). 13

14 It can be assumed that all waiting times at a train station caused by delays are valued by passengers in the same way valuation of delays at a station Quelle: FH Aachen The means for the negative standardized part worth utilities indicate a common function with a constant to diminishing marginal utility for delays at the originating station and waiting times caused by a missed connecting train. assumption: All waiting times at a train station caused by delays are valued by passengers in the same way (e.g. also waiting times at a station because of a delayed connecting train). A simplified approach was used to roughly estimate a constant factor for the time being. 14

15 valuation of transfers in relation to in-vehicle-time [min] A comprehensive approach could be deduced for the valuation of transfers valuation of transfers % (valuation of change of platforms) < 100 km km > 200 km travel distance An average transfer with unknown platform (including up to 10 min walking) is valued by passengers like about 15 to 30 minutes additional in-vehicle-time. An average transfer includes an average need of changing platforms. (assumption: With a probability of 90% a transfer requires a change of platforms.) Long-distance passengers attach a negative value of about 15 to 20 minutes of in-vehicle-time to the need of changing platforms for a transfer. unknown platform same platform The value of a transfer can be reduced to about 10 to 15 minutes of in-vehicle-time, when it is ensured that a change of platforms is not required (same platform). 15

16 Outline Introduction & Requirements Empirical Methods Findings & Conclusions Outlook 16

17 Outlook next steps Within the project Evalinfra DB Netz AG has integrated the deduced factors in a travel demand model. The model has been implemented in the network planning system WiZug/MOSES. An implementation of the model in further systems is pursued in order to achieve a consistent assessment of qualitative infrastructure measures. The deduced weighting factors and coherences can be integrated in further travel demand models; yet, attention should be paid to the fact that if parameters are newly integrated in a system of parameters, the whole system should be jointly adjusted according to its strengths and weaknesses. further research Further research should concentrate on a further detailing of the weighting factors by the purpose of the journey for the single attributes. Particularly for the valuation of delays causing additional waiting times at a station a non-linear approach should be pursued. 17