THE EFFECTS OF ROAD PRICING ON TRAVEL BEHAVIOUR. THE CASE OF MILAN Paolo Beria Ilaria Mariotti Ila Maltese Flavio Boscacci DAStU, Politecnico di Milano SIET 2013 Venezia, September 18th-20th, 2013

STRUCTURE Aim of the work Literature review on Road Pricing Data and methodology Descriptive statistics Multinomial logit results Conclusions

AIM OF THE WORK Investigating the road pricing impact on travel behaviour at the urban scale. 1,198 Milan citizens have been surveyed (Green Move project).

STRUCTURE Aim of the work Literature review on Road Pricing Data and methodology Descriptive statistics Multinomial logit results Conclusions

Road pricing theoretical literature AcceptabilityEffectiveness WHAT: level of acceptability of the regulation and its determinants ( Psychological and personal factors; fairness and clarity of the measure; certainty about the use of revenues; alternative travel modes ) WHEN: ex-ante for testing the feasibility of a toll introduction WHAT: effectiveness of the toll in terms of congestion ( congestion charge ) and/or pollution ( pollution charge ) decreasing. WHEN: ex-post in the few cities where it has been introduced: Singapore (1975); Bergen (1986), Oslo (1990) Trondheim (1991) and Stavanger (2001); London (2003); Stockholm (2006) and Gothenburg (2013); Milan (2008 pc, 2011 cc).

Road pricing empirical literature AcceptabilityTravel changes Explanatory variables Gender+s Kids- Education+ Car number- Income-ns Time value+ Environmental concern+ Place of residence+/- Commuting- Explanatory variables Gender+/-ns Kids+/- Age-s Car number+/- Income+/- Fixed activities- Flexibility+ Place of residence (far)- Commuting-

STRUCTURE Aim of the work Literature review on Road Pricing Data and methodology Descriptive statistics Multinomial logit results Conclusions

Methodology 1. Descriptive statistics 2. Multinomial logit model AnswersMultinomial logit Yes, I reduced the use of the car to enter Area C zone 1 Yes, I use less the car for all my trips 2 Yes, I do not use the car anymore for my trips No, I pay the ticket and I did not change my travel behaviour at all 0 No, I’m limitedly affected by Area C Three kind of explanatory variables: ▫ Socio-demographic (individual and car fleet); ▫ Travel behaviour; ▫ Green Attitude. YearSpatial scopeSample 2012*Milan (pop. About 1,400,000)1,129 respondents (living in Milan, with driving licence)

Explanatory variables VariableDescription GenderDummy variable: 1 “ if male, 0 “ if female. AgeAge of the respondent. Continuous variable EducationDummy variable: 1 “ if the respondent achieved a bachelor degree (ISCED 6 at least), “0 otherwise Skilled workerDummy variable: 1 “ if the respondent is a skilled worker, 0 “ otherwise Car changeChange in the number of owned cars in the last five years. Dummy variable: 1 “ if increase, 0“ if decrease or steady. Oil priceDummy variable: 1“ if the respondent has changed his/her travel patterns due to the oil price’s increase, 0“ otherwise. District of residenceRepresents the district where the respondent lives. Dummy variables. Modal choice: -LPT, Bike, Foot, Motorcycle, Car (driver), Car (passenger) Six dummy variables suggesting the main modal choice adopted by the respondent. Daily travel by car for: -reaching the workplace,or the LPT stop -moving within the neighbourhood or outside -leisure in the city, other motives (i.e. tourism outside the city) Six dummy variables underlying why the respondent uses the car daily or very often. Car useDummy variable: 1“ if the respondent uses the car not often, 0“ otherwise Car sharing memberDummy variable: 1“ if the respondent is or has been member of car sharing services in (Guidami and E-Vai), 0 “ otherwise. Peer-to-peerDummy variable: 1 “ if the respondent is favourable to become a member of a future peer-to-peer car sharing service, 0“ otherwise Share LEVShare of low emission vehicles owned by the respondent over the total number of owned cars. Continuous variable Socio demographic Travel behaviour Green Attitude

STRUCTURE Aim of the work Literature review on Road Pricing Data and methodology Descriptive statistics Multinomial logit results Conclusions

Road pricing (AreaC) impact in MILAN Source: LEV0€

Socio – demographic variables Age Oil price sensitiveness Milan neighbourhoods

Socio – demographic variables Oil price sensitiveness Milan neighbourhoods

Travel behaviour Car use frequency Travel motivation/matter Travel modes

Green attitude Car sharing membership Car sharing peer-to-peer (attitude towards) Next car choice Car fleet fuel

STRUCTURE Aim of the work Literature review on Road Pricing Data and methodology Descriptive statistics Multinomial logit results Conclusions

Results Group 1 GROUP 1 reduced the use of the car to enter Area C zone GROUP 0 Those who have not reduced the use of their cars because: they are not affected by AreaC; they pay the toll. GA SD TB

Results Group 2 GROUP 2 reduced the use of the car GROUP 0 Those who have not reduced the use of their cars because: they are not affected by AreaC; they pay the toll. GA SD TB

COMMENTS Gender is not always significant Permanent job (proxied by using the car to reach the work place) makes respondents less flexible Age proved to be significant Groups 1 and 2 tend to prefer LPT, and to use the car to reach the LPT stop The two groups share some features like price- sensitiveness, travel behavior, and green attitude. Owning Low Emission Vehicles is always negative and significant.

STRUCTURE Aim of the work Literature review on Road Pricing Data and methodology Descriptive statistics Multinomial logit results Conclusions

CONCLUSIONS EFFECTIVENESS of the Area C program in car use reduction. The impact is not homogeneously distributed among users:  Weaker groups tend to be more affected due to their price sensitiveness.  LPT users as well are more likely to reduce their car use. EXOGENOUS factors (not investigated) A clear communication of ▫ the policy goals ▫ the use of the toll revenues The presence of a good LPT service The involvement of the citizens.

Questions and suggestions are welcome. Ila Maltese DAStU – Politecnico di Milano

Appendix Q29. Le sue abitudini di mobilità sono state influenzate dall’introduzione a Milano dell’Area C? Si, uso meno l’auto per entrare nell’area C Si, uso meno l’auto per tutti gli spostamenti Si, non uso più l’auto per i miei spostamenti No, pago il ticket e non ho modificato per nulla le mie abitudini di spostamento no, non sono influenzato se non marginalmente dall’area C Q29.1 Lei ha detto che utilizza meno l’auto/ non utilizza più l’auto. Come si muove in città? Indichi una sola risposta Mi muovo con i mezzi pubblici Mi muovo con la bici Mi muovo con la moto Utilizzo una combinazione tra mezzi privati (bici-moto) e mezzi pubblici Mi muovo a piedi