## Samenvatting

According to the EPA (1998) single pricing measures will have various specific impacts on purchase and travel behavior. Vehicle fees affect vehicle ownership and vehicle type as the cost of owning a car increases. Emission fees can be added to vehicle registration taxes and/or circulation taxes discouraging vehicle ownership of higher-emitting vehicles. Fuel prices primarily affect vehicle use and may cause travelers to switch modes. Congestion pricing will rather affect car use than vehicle ownership, but may induce the switch to other modes such as public transportation. Parking fees are more likely to affect vehicle ownership and may induce a switch to transport alternatives, especially when linked to popular trips. Incentives such as third payer public transport are seen as a supportive strategy of another active pricing measure as travel behaviour is less influenced by a cost incentive than a disincentive. In case of modal subsidies increasing the use of less-polluting modes through a reduction in their relative price is alleged to impact on modal shifting as the modes being subsidized become very attractive.

While single pricing measures may positively impact consumer behavior and modal shifting to a certain extent, a combination of policy measures is usually recommended for a more valuable outcome. However, in order to effectively evaluate the potential impact policy scenarios, the relative importance of each measure is required.

In this paper we present applications of an experimental procedure (Functional Measurement (FM); Anderson, 1981, 1982) to define the relative importance of single measures when combined into scenarios to promote green car purchase and modal shifting. Functional Measurement allows for the indirect establishment of importance weights, as opposed to self-estimation techniques such as trade-off, part-worth or point-allocation. Moreover, FM has shown to produce theoretically and empirically valid attribute weights in various areas of cognitive information processing and has successfully been applied in transport economics (Louvière and Levin, 1978, Louvière, 1984, Mairesse and Macharis, 2009). A typical FM judgment task consists in the evaluation of a set of different stimulus-combinations by producing a response on a continuous rating scale. In this case, participants are required to rate the probability of buying a car or switching modes given a combination of levels of pricing measures in a full-factorial design and (e.g. A×B×C) a set of subdesigns (reduced forms of the full-factorial, e.g. A×B, A×C and B×C). Based on these ratings, the algebraic policy combination rule can be inferred by means of analysis of variance and pattern analysis. Extended empirical research over the last 30-40 years showed three algebraic combination rules approximate the internal integration functions for most judgment tasks: an addition rule, a multiplication rule and an averaging rule (for an overview see Anderson, 1996, 2009). When an averaging combination rule is established, empirical weights can be calculated using numerical estimation procedures (Anderson & Zalinski, 1991). Subsequently, when combined to price elasticities the technique permits the precise quantification of the responsiveness to the different individual policy strategies and their relative impact on travel and purchase behavior. Ultimately, these results allow for the formulation of realistic policy scenarios, which may be subjected to a multi-actor multi criteria analysis (MAMCA; Macharis, 2004) for evaluation and implementation.

* Anderson, N.H. (1981). Foundations of Information Integration Theory. London: Academic Press.

* Anderson, N.H. (1982). Methods of Information Integration Theory. London: Academic Press.

* Anderson, N.H. (1996). A functional theory of cognition. New Jersey: Lawrence Erlbaum Associates.

* Anderson, N. H. (2009). Unified social cognition. NY: Psychology Press.

* Anderson, N.H. and Zalinski, J. (1991). Functional measurement approach to self-estimation in multiattribute evaluation. In N. H. Anderson (Ed.), Contributions to Information Integration Theory Volume I: Cognition (pp. 145-186). NJ: Lawrence Erlbaum Associates.

* EPA - United States Environmental Protection (1998). Technical methods for analyzing pricing measures to reduce transportation emissions.

* Louviere, J.J. & Levin, I.P. (1978). Functional Measurement Analysis of Spatial and Travel Behavior. In Hunt, K. (Ed.) Advances in Consumer Research Vol. 5 (pp. 435-439) Chicago: Association for Consumer Research.

* Louviere, J.J. (1984). Hierarchical Information Integration: a new method for the design and analysis of complex multiattribute judgement problems. Advances in consumer Research, 11, 148-155.

* Macharis, C. (2004). The importance of stakeholder analysis in freight transport : The MAMCA methodology. European Transport \ Transporti Europei, 25/26, 114-126.

* Mairesse, O. and Macharis, C. (2009). Functional integration of environmental aspects in subjective car purchase probability. 1st Transatlantic NECTAR meeting, Arlington, VA, USA.

While single pricing measures may positively impact consumer behavior and modal shifting to a certain extent, a combination of policy measures is usually recommended for a more valuable outcome. However, in order to effectively evaluate the potential impact policy scenarios, the relative importance of each measure is required.

In this paper we present applications of an experimental procedure (Functional Measurement (FM); Anderson, 1981, 1982) to define the relative importance of single measures when combined into scenarios to promote green car purchase and modal shifting. Functional Measurement allows for the indirect establishment of importance weights, as opposed to self-estimation techniques such as trade-off, part-worth or point-allocation. Moreover, FM has shown to produce theoretically and empirically valid attribute weights in various areas of cognitive information processing and has successfully been applied in transport economics (Louvière and Levin, 1978, Louvière, 1984, Mairesse and Macharis, 2009). A typical FM judgment task consists in the evaluation of a set of different stimulus-combinations by producing a response on a continuous rating scale. In this case, participants are required to rate the probability of buying a car or switching modes given a combination of levels of pricing measures in a full-factorial design and (e.g. A×B×C) a set of subdesigns (reduced forms of the full-factorial, e.g. A×B, A×C and B×C). Based on these ratings, the algebraic policy combination rule can be inferred by means of analysis of variance and pattern analysis. Extended empirical research over the last 30-40 years showed three algebraic combination rules approximate the internal integration functions for most judgment tasks: an addition rule, a multiplication rule and an averaging rule (for an overview see Anderson, 1996, 2009). When an averaging combination rule is established, empirical weights can be calculated using numerical estimation procedures (Anderson & Zalinski, 1991). Subsequently, when combined to price elasticities the technique permits the precise quantification of the responsiveness to the different individual policy strategies and their relative impact on travel and purchase behavior. Ultimately, these results allow for the formulation of realistic policy scenarios, which may be subjected to a multi-actor multi criteria analysis (MAMCA; Macharis, 2004) for evaluation and implementation.

* Anderson, N.H. (1981). Foundations of Information Integration Theory. London: Academic Press.

* Anderson, N.H. (1982). Methods of Information Integration Theory. London: Academic Press.

* Anderson, N.H. (1996). A functional theory of cognition. New Jersey: Lawrence Erlbaum Associates.

* Anderson, N. H. (2009). Unified social cognition. NY: Psychology Press.

* Anderson, N.H. and Zalinski, J. (1991). Functional measurement approach to self-estimation in multiattribute evaluation. In N. H. Anderson (Ed.), Contributions to Information Integration Theory Volume I: Cognition (pp. 145-186). NJ: Lawrence Erlbaum Associates.

* EPA - United States Environmental Protection (1998). Technical methods for analyzing pricing measures to reduce transportation emissions.

* Louviere, J.J. & Levin, I.P. (1978). Functional Measurement Analysis of Spatial and Travel Behavior. In Hunt, K. (Ed.) Advances in Consumer Research Vol. 5 (pp. 435-439) Chicago: Association for Consumer Research.

* Louviere, J.J. (1984). Hierarchical Information Integration: a new method for the design and analysis of complex multiattribute judgement problems. Advances in consumer Research, 11, 148-155.

* Macharis, C. (2004). The importance of stakeholder analysis in freight transport : The MAMCA methodology. European Transport \ Transporti Europei, 25/26, 114-126.

* Mairesse, O. and Macharis, C. (2009). Functional integration of environmental aspects in subjective car purchase probability. 1st Transatlantic NECTAR meeting, Arlington, VA, USA.

Originele taal-2 | English |
---|---|

Titel | Proceedings of the World Conference on Transport Research, Lisboa, Portugal |

Status | Published - 2010 |

Evenement | Unknown - Duur: 1 jan 2010 → … |

### Conference

Conference | Unknown |
---|---|

Periode | 1/01/10 → … |