STRC 3rd. Time is money - The valuation of Swiss travel times and travel time variability. Arnd König Kay W Axhausen

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1 Time is money - The valuation of Swiss travel times and travel time variability Arnd König Kay W Axhausen Paper submitted for STRC 2003 March 19-20, 2003 STRC 3rd Swiss Transport Research Conference Monte Verità / Ascona, March

2 Time is money - The valuation of Swiss travel times and travel time variability Arnd König IVT ETHZ Zürich Phone: Fax: koenig@ivt.baug.ethz.ch March 19-21, 2003 I

3 Abstract A main target of most infrastructure projects in transportation systems is the minimisation of travel time. Neglecting external costs faster travel times lead to benefits for system users. A common tool to express these benefits is the definition by a monetary based value, the value of travel time. E.g. this value is implemented in cost benefit analysis one of the most important factors for the calculation of benefit of these transportation infrastructure projects. Another aspect is the evaluation of variable travel times and unexpected delays by travellers. There are already different European and US studies done, concentrating on the willingness to pay for the reduction of travel times in a national context. The transport planning group of the IVT (ETH Zurich) performed recently different studies measuring travel behaviour where variables as travel time and trip costs were included. Especially one study concerns mainly the value of travel time. Although this study has got also a national Swiss context, there are many methodological differences. We try to measure a couple of VoTs which differ e.g. by trip purposes, types of persons or the choice models (route, destination and mode choice). The measurement instrument is in all cases a stated preference survey followed by a discrete choice analysis. The estimated parameters allow to compute a value of travel time for different trip purposes and user groups. Differences between the choice types can be named. This paper gives an overview of the recent studies. A second part discusses the construction and the design of the survey. It outlines some specific features which have to be considered when designing a survey like this. The research is in progress, so a third part concentrates on the choice model estimation and some first results. Keywords VoT Discrete Choice Modelling Swiss Transport Research Conference STRC 2003 Monte Verità II

4 1. Modelling values of travel time variability 1.1 Approach and recent studies The transport planning group of the IVT (ETH Zurich) performed recently different studies measuring travel behaviour where variables as travel time and trip costs were included. Especially one study concerns mainly the value of travel time. Although this study has got also a national Swiss context, there are many methodological differences. We try to measure a couple of VoTs which differ e.g. by trip purposes, types of persons or the choice models (route, destination and mode choice). The measurement instrument is in all cases a stated preference survey followed by a discrete choice analysis. The estimated parameters allow to compute a value of travel time for different trip purposes and user groups. Differences between the choice types can be named. There are already different international studies done, concentrating on the willingness to pay for the reduction of travel times in a national context. Table 1 gives an overview of the wide range of the valuing of travel time in different countries, transport modes and trip purpose. Depending on person groups, socio demographic characters and measurement methods the values differ even more. The first paragraph of the table shows recent international studies which focus directly on the measurement of peoples valuation of travel time reduction. In most cases the studies concentrate only on a few details of travelling aspects (modes, trip purposes...). The second paragraph shows values of Swiss research projects. The younger studies didn t focus on the valuing of travel time as a main topic, but the data sets allowed to estimate specific values. The last paragraph presents assumptions of values used in German and Austrian planning processes. The values for the Avanti Initiative were also assumed. 3

5 Table 1 Overview recent VoT Studies [CHF/h travel time reduction] Study Year Car Comm. Business Other Total Dutch VoT study 1 Danish VoT Study 2 Finnish VoT Studies 3 Portuguese railway authority 4 VoT on UK Roads 5 Californian drivers will.-to-pay-study 6 Korean VoT Study Rail Comm. Business Other Total Quantif. v. Zeitgewinnen NUP Swissmetro Verifizierung Prognosemethoden Einführung ICN BVWP (D) Österr. Schieneninfrastruktur Avanti Initiative Numbers are not corrected for inflation Hague Consulting Group (1990) 2 Jovicic and Hansen (2003) 3 Karasmaa (2001) 4 Bierlaire (1995) 5 Hague Consulting Group (1999) 6 Brownstone, Ghosh, Golob, Kazimi and Van Amelsfort (2003) 7 Joong and Sunduck (2003) 8 Lüthi (1980) 9 Bierlaire, Axhausen and Abay (2001) 10 Vrtic and Axahausen (2002) 11 Chaumet and Eritmann (1995) 12 Chaumet, Cerwenka, Bruns, Erismann, Kern and Stern (2000) 13 Chaumet and Eritmann (1995) 4

6 1.2 Discrete choice models The usage of discrete choice models is established in all kinds of transport planning. First approaches where developed by Domencich und McFadden (1975) and Ben-Akiva und Lerman (1987). The roots of these econometric utility models can be found in the field of economic science. The most common model family is the logit model with its extensions. In principle, the subjective individual utility is calculated for all possible alternatives. The alternative with the highest utility will be chosen. The model assumes a rational choice by observed of the transportation network users. The aspect of the subjectivity is very important, because the estimated behaviour is based on the subjective information, which differs from person to person; e.g. there is substantial variety in the knowledge about the offer of a specific public transport company. To consider this fact, the objective utility of an alternative has to be modified by a stochastic term. Mentioned above, a first assumption is, that every person is informed perfectly about the attributes of all alternatives: The possible destinations, the available modes (including data about the time table, the travel times, parking possibilities, the trip costs, etc.) or a complete overview of the network. To consider the lack of perfect information about the transport system and the preferences of the users the estimated utility in the model consists of two parts; see alsoortuzar und Willumsen (1994): One measurable, systematic part V jq, representing the value of the objective utility of an alternative j for a person q One stochastical part respectively error ε jq, of V jq considering the individual unobserved characteristics of each user The utility U jq is calculated as follows: U = jq V jq ε jq With V jq considering the attribute of the alternatives, the choice situation of the user and the characteristics of the user. More detailed information with reference to discrete choice analysis can be found in Ben- Akiva und Lerman (1987), Ortuzar und Willumsen (1994) or Maier und Weiss (1990). 5

7 Discrete choice modelling is a very common tool, for measuring values of travel time reductions. The ratios of the estimated parameters which describe the influence of the variables in the utility function represent the monetary and time related values. 2. Survey design and data preparation The recruiting of participants for this survey was done by the KEP-telephone-interviews of the SBB/CFF/FFS. Addresses, socio-demographic data and also trips the respondents were available for the construction and the analysis of the survey. The questions of this study are SP-experiments. SP-experiments with hypothetical situations consider very much to the input needs of discrete choice models. Different choice types, transport modes, trip purpose etc. can be constructed and modelled quite well in principal. The difficulty is the way of presenting the variables, because the subject has to be understandable and plausible to the answering person. Table 2: Combinations of SP-experiments in the survey Mode of reported trip Car PT Combination Mode choice Route choice with chosen mode Car PT Route choice with non-chosen mode Car PT (Destination choice) () () () () Share (main study) 39.0% 20.3% 40.7% - No. of respondents (main study) Originally the questionnaire of this study consists of four parts. The parts 1, 2 and 3 are designed as SP-experiments with six or nine iterations of each. The characteristics of the choice variables vary in each experiment. The orthogonal design was generated by the statistical software SPSS The experiments and the characteristics of the choice variables base on the trip the participants reported in the KEP telephone interview. Part 1 is a mode choice experiment (car and bus or rail), which was presented to those participants, who have a car available. 6

8 Table 3: Types of SP-experiments Mode choice car rail (main study version) CAR RAIL Travel costs: 18 Fr. Travel costs: 23 Fr. Total travel time: 40 minutes Travel time: 30 minutes... congested: 10 minutes Headway: 30 minutes... uncongested: 30 minutes No. of changes: 0 times Your choice fi Route choice rail (main study version) RAIL RAIL Travel costs: 20 Fr. Travel costs: 23 Fr. Travel time: 40 minutes Travel time: 30 minutes Headway: 15 minutes Headway: 30 minutes No. of changes: 1 times No. of changes: 0 times Your choice fi Destination choice (pretest only) SHOPPING CENTER A SHOPPING CENTER B Travel time: 9 minutes Travel time: 30 minutes Travel costs: 22 Fr. Travel costs: 20 Fr. Price basket of goods: 120 Fr. Price basket of goods: 100 Fr. Your choice fi Part 2 is a route choice experiment. Participants got choice experiments with the mode they chose for the reported trip. It was also planed to present one group of persons a route choice 7

9 experiment with the mode they didn t choose. Estimation results of two pretests showed that the route choice of the non-chosen mode only worked with car drivers. For PT-user (with driving licence) no plausible results could be estimated. Part 3 was a destination choice experiment. The participants were asked to choose between two shopping centres. One was cheaper but further away then another centre. Unfortunately this totally new idea gave no plausible estimation results either. In the main study it had to be dropped. Table 2 show the combinations of the SP-experiments sent out. In Part 4 of the survey additional questions were asked like questions concerning the reported trip, the person and the household and if a business trip was reported questions concerning working time on business trips. Table 3 shows the final presentation of mode and route choice experiments and the skipped presentation of destination choice experiments. As mentioned one difficulty is the presentation of the choice variables. Table 4 shows three different types of presenting car travel time and the appending estimation results. One approach of the study was to estimate values for different road traffic conditions. And second decision process shouldn t be too trivial with more choice variables than just travel time and travel costs. One possibility to estimate VoTs for different traffic conditions is to divide the presented travel time into two parts, congested traffic and free flow. The first pretest presented a travel time and a percentage of this time representing driving in a congestion. The result was a very high valuation of the congested part of the trip and a low ratio of the values for uncongested and congested trip parts. Considering that this ratio is measured in other VoT studies, i.e. the UK study, is about 1/1.5 this percentage was presented in the second pretest in minutes. The estimation result gave lower absolute values but a ratio between uncongested and congested parts of the travel time which was even lower than the variation of pretest 1. A reason for this could be the emphasis on the congestion part because of the missing of the uncongested part. This gap was closed in the main study, which gave much more plausible results. The completion of total travel time, its congested and uncongested part reduced the VoT of congestions by more than a half. The VoT in free flow traffic is less influenced by the type and the complexity of the presentation. Another sensitive point concerning the design of VoT studies is the presentation of values and their distribution. For the measurement of monetary values of travel time with SPexperiments at least two variables for time and costs must be presented for each alternative. That gives the respondent the opportunity to calculate a standardised value for all alternatives. 8

10 These trade-offs must be constructed very carefully, because there s a high risk of manipulate the results! Table 4 Comparison of different types of presentation and estimation results (car route choice experiments) Pretest 1: Presentation of car travel Estimated VoT for car travel [CHF/h] ROUTE A Travel time: 40 minutes...in uncongested traffic: Travel costs: 18 Fr....in congested traffic: Share of congestion: 25% Ratio: 1 / 3.15 Pretest 2: Presentation of car travel Estimated VoT for car travel [CHF/h] ROUTE A Travel costs: 18 Fr....in uncongested traffic Travel time: 40 minutes...in congested traffic congested: 10 minutes Ratio: 1 / 3.59 Main study: Presentation of car travel Estimated VoT for car travel [CHF/h] ROUTE A Travel costs: 18 Fr....in uncongested traffic Total travel time: 40 minutes...in congested traffic congested: 10 minutes Ratio: 1 / uncongested: 30 minutes 9

11 Table 5 Pretest 1: Presented trade-offs and estimated VoTs (reply route choice car experiments) Trade-offs presented in survey (reply): Mean [CHF/h]: 58.9 Std. Dev. [CHF/h]: 32.1 Sample size [ ]: 336 Estimation: VoT [CHF/h]: % 25% 20% 15% 10% 5% 0% CHF/h Pretest 2: Trade-offs presented in survey (reply): Mean [CHF/h]: 46.5 Std. Dev. [CHF/h]: 25.9 Sample size [ ]: 246 Estimation: VoT [CHF/h]: % 25% 20% 15% 10% 5% 0% CHF/h Main study: Trade-offs presented in survey (reply): Mean [CHF/h]: 34.9 Std. Dev. [CHF/h]: 19.2 Sample size [ ]: 2674 Estimation: VoT [CHF/h]: % 25% 20% 15% 10% 5% 0% CHF/h 10

12 Table 6 Socio-demographic characteristics of the different samples KEP Pretest 1 Pretest 1 Main study MZ 00 PT-discount: Halbtax-ownership GA-ownership Car availability always sometimes never Education Primary lower secondary Vocational training A-Level, tertiary Working Status None Part-time Fulltime Self-employed Household income [CHF/Year] less than more than no response Two precepts must be considered: First the distribution of these trade-offs in the sample should be uniform in the best case and must at least content a sufficient amount of trade-offs over all distributed values. And second the distribution must be in a plausible range. It has to content small values and not unrealistic high values. Table 5 shows the development of the 11

13 presented trade-off over the two pretests and the main study in the replied questionnaires. The variable values of the pretest offered less small trade-offs. That was corrected in the main study. The range of values was limited from 5 to 100 CHF/h for the benefit of smaller values, but the distribution of the presented trade-offs includes all VoTs reported from others studies. Figure 1 Distribution of the MZ 00 weights in the dataset 15% 10% 5% 0% Weights Mean [CHF/h]: 1.01 Std. Dev. [CHF/h]: 0.96 Sample size [ ]: 1225 Depending on the sample another important issue for the estimation quality rises up: As mentioned above the recruiting of participants for this study was done by the KEP-telephoneinterviews of the SBB/CFF/FFS. At the end of that telephone interview the responds are asked for the willingness to participate on this survey. The fact that the people are asked on behalf of a railway company implies an interest of the progress of that mode by its users. A comparison of the socio-demographic structure of this sample with the total sample of the KEP telephone interview and the Swiss Micro Census (MZ 00) marks this effect. The share of discount card owners increases from the MZ 00 to this study by 13%. The share of GAowners in this study is nearly twice share of the Swiss average. Similar characteristics can be found in terms of education and working status and especially in the house hold income (see Table 6). So depending on the sample there is a need of weighting it. In this case the data is weighted by the MZ 00 shares of age, gender, discount and GA ownership, education, working status and house hold income. Figure 1 shows the distribution with a small share of outliers. 12

14 3. Model estimation The following results base on multi nominal logit approach (MNL). They are estimated with the software BIOGEME vers. 0.5 and 0.6, developed and distributed by Dr. M. Bierlaire. This research work is progress, what has to be considered for the intermediate estimations. Table 7: Route choice experiments: Estimation results...car by car drivers...rail by car drivers...rail by rail users Variables Coeff. t-test Coeff. t-test Coeff. t-test Costs Uncong. travel time Congested travel time Travel time (PT) Headway No. of changes VoT Car uncongested [CHF/h] VoT Car congested [CHF/h] Ratio of congested to uncongested VoT VoT PT [CHF/h] Headway [% invehicle time] Transfer [% invehicle time] N L (0) L (β) adj ρ Having in mind that Table 7 and Table 8 show the first model steps without sociodemographic data and no multidimensional or interaction terms the data gives very good results. Most coefficients are highly significant. An exception is the route choice by car drivers 13

15 for the non chosen mode reported in the KEP-interview. Persons who drive mainly car evaluate variables like headway and the number of changes very differently. One reason could be minor experiences in the unknown mode. The same phenomenon can be observed in the mode choice results. The valuation of the variables differs in those situations symbolising negative characteristics of the (in reality) non chosen mode. PT-users value travel time in congested traffic conditions three times higher than car drivers. Table 8: Mode choice experiments: Estimation results...by all respondents...by car drivers...by PT users Variables Coeff. t-test Coeff. t-test Coeff. t-test Constant Costs Uncong. travel time Congested travel time Travel time (PT) Headway No. of changes VoT Car uncongested [CHF/h] VoT Car congested [CHF/h] Ratio of congested to uncongested VoT VoT PT [CHF/h] Headway [% invehicle time] Transfer [% invehicle time] N L (0) L (β) adj ρ

16 The model fitness especially of the mode choice models is promisingly high. At this point there is provable reason for the low fitness of the route choice car model, in particular heeding the high significance of the coefficients. The estimated values of travel time are slightly higher than the comparable studies mentioned in chapter 1. Regarding the international studies this results underline one main target of the research project: Measuring separate values for the Swiss market and its planning processes. The difference is plausible and can be explained by higher price rate and costs of living. Comparing the data with recent national studies, it is important to remark that design and methology of this study are concentrating exclusively on the topic values of travel time whereas other studies mainly concentrate on other topics and estimate or measure VoTs only as by-product. 4. Further work The replies of this study include only 75 business trips. The discussion makes clear, that this amount is too small to be analysed. The lack of trips with these purposes has to be filled with trips from other data sets. The estimation software allows combined datasets. For modelling and analysis a list of possible types of models, model forms and contents has to be constructed more detailed to trace the coefficients and their strength in the various utility functions. This is concerning to the following points: - Trip purposes - Socio demographics - Non linearity - Elasticity between income and distance and the VoT - Valuation of small time savings - Combination of RP and SP resp. SP and SP data The influence to the utility function of the socio-demographic variables gender, age, income, language, license and car ownership, Season ticket, network and discount card ownership, trip purpose and trip distance has be tested in more detail. Different possible model approaches for the next model steps are: 15

17 Implementation of socio demographics through interaction terms with the design variables: U = (a 0 a i * Soc i ) * Time b * Costs... e or expansion of the ASC (Mode choice only). Implementation of the elasticity between income and distance and the VoT β inc inc 0 * var, e. g time ninc n dist cos t β dist. 0 dist dist Implementation of non-linear terms to test for the effect of small time savings: a 1 * Time a 2 * Time 2 b 1 * Cost b 2 * Cost 2 c 1 * Time* Cost Another model approach considering travel time restriction, based on over all daily time budgets and monetary budgets lead to very good results in a French study where U = const mon. budget * Costs time budget * Time... e See also Blayac and Causse (2001). 5. Acknowledgements This paper is part of the research project Zeitkostenansätze im Personenverkehr being supported by the Swiss Traffic Engineering Association. We would like to thank the SBB/CFF/FFS for their permission and co-operation to use the KEP-Survey and the Link Institute, Lucerne, for providing us the KEP-Data. We would like to thank also Dr. J.J. Bates, Dr. G. Abay and Dr. M. Bierlaire for their input and co-operation and M. Bierlaire for providing the estimation software. 6. References Abay, G. (1984) Kosten - Nutzen - Anlayse für Verkehrsinvestitionen, Admistration und Druck AG, Zürich. Abay, G. and K.W. Axhausen (2000) Zeitkostenansätze im Personenverkehr: Vorstudie, SVI Forschungsberichte 42/00, Bundesamt für Strassen, Bern. Axhausen, K.W. (1995) Was sind die Methoden der Direkten Nutzenmessung, Straßenverkehrstechnik, 39 (5)

18 Ben-Akiva, M.E. and S.R. Lerman (1987) Discrete Choice Analysis, MIT Press, Cambridge. Bierlaire, M. (1995) Enquêtes de préférences révélées et déclarrées concernant le choix modal des voyageurs du corridor Lisbonne - Porto - Braga, Groupe de recherche sur les transports, Departement de mathématique, Facultés Universitaires ND de la Paix, Namur. Bierlaire, M., K.W. Axhausen and G. Abay (2001) The acceptance of modal innovations: The case of Swissmetro, paper presented at the 1st Swiss Tranportation Research Conference, Ascona, March Blayac, T. and A. Causse (2001) Value of time: a theortical legitimization of some nonlinear representative utility in discrete choice models, Transportation Research B, 35 (4) Brownstone, D., A. Ghosh, T.F.Golob, C.Kazimi and D. Van Amelsfort (2003) Drivers willingness-to-pay to reduce travel time: Evidence from the San Diego I-15 congestion pricing project, Transportation Research A, 37 (2) Chaumet, R. and B. Erismann (1995) TGV Rhin-Rhône: Consequences du systeme sur le trafic des voyageurs avec la suisse, Ernst Basler und Partner, Zollikon. Chaumet, R., P. Cerwenka, F. Bruns, B. Erismann, P. Kern and M. Stern (2000) Evaluation von Massnahmen zum Ausbau der Schieneninfrastruktur, Ernst Basler und Partner, Zürich. Domencich, T.A. and D. McFadden (1975) Urban Travel Demand - A Behavioral Analysis, Nord Holland, Amsterdam. Hague Consulting Group (1990) The Netherlands value of time study, final report to Dienst Verkeerskunde, Rijkswaterstaat, Hague Consulting Group, Den Haag. Hague Consulting Group (1999) The value of travel time, final report to Department of Transport, London Hague Consulting Group, Den Haag. Jovicic, G. and C. O. Hansen (2003) A passenger travel demand model for Copenhagen, Transportation Research A, 37 (2) Joong, R.C. and D.S. Sunduck (2003) Estimating confidence interval of value of travel time, Vortrag bei 82th Annual Meeting of the Transportation Research Board, Washington, D.C. Karasmaa, N. (2001) The spatial transferability of the Helsinki metropolitan area mode choice models, Vortrag bei 9th WCTR, Seoul. Lüthi, W. (1980) Monetäre Quantifizierung von Zeitgewinnen, Lang Druck AG, Liebefeld. Maggi, R., M. Peter, J. Mägerle and M. Maibach (2000) Der Nutzen des Verkehrs, Berichte des NFP 41 "Verkehr und Umwelt", D 10, Bern. Maier, G. and P. Weiss (1990) Modelle diskreter Entscheidungen, Springer-Verlag, Wien. Ortuzar, J.de Dios and L.G. Willumsen (1994) Modelling Transport, John Wiley & Sons, Chichester. Prüfungskommission (1981) Bericht zur Überprüfung von Nationalstrassenstrecken (NUP), Bern. Vrtic, M., K.W. Axhausen, R.Maggi und F. Rossera (2003) Verifizierung von Prognosemethoden im Personenverkehr, on behalf of SBB and Bundesamt für Raumentwicklung (ARE), IVT, ETH Zürich and USI Lugano, Zürich and Lugano. 17