The influence of activity-travel patterns on the success of VTBC

ABSTRACT

The objective of this work is to verify how the complexity of activity-travel patterns may influence the propensity to change travel behaviour in the context of a Voluntary Travel Behaviour Change (VTBC) programme. Data used in this work was drawn from a VTBC programme implemented in Cagliari, Italy between 2011 and 2012, for promoting the use of an underutilised Light Rail service (LR). A descriptive comparative analysis of activity–travel patterns recorded before and after the delivery of a personalised travel plan was reported. In addition to the descriptive analysis, a panel Probit model is proposed to further understand the influence of complex trip-chaining behaviours on the propensity to change travel behaviours. The results indicate that when individuals are presented with a convenient transport alternative that allows them to flexibly chain their activities, the propensity to use a sustainable mode of transport increases.

Abbreviations: ABA: activity – based analysis; AW: after work tour; BW: before work tour; CMS: casteddu mobility styles; CW: complex working day; HWC: home to work commute tour; LR: light rail; NHB: non home based tour; NNW: non work tour; NW: non working day; P&R: park and rider; PP&R: prospective park and rider; PTP: personalised travel plan; SW: simple working day; VTBC: voluntary travel behaviour change; WB: work based tour

KEYWORDS:

• Activity-based analysis
• voluntary travel behaviour change
• activity-travel behaviour
• travel patterns
• tour analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The sample of 109 individuals involved in the VTCB programme was recruited through two surveys. The first survey (‘Who uses Metrocagliari?’) was carried out in order recruit actual light rail users, so as to identify a specific target to be promoted (P&R). At the end of the first survey, 23 P&Rs were included in the two week programme. Once the target was identified, a second survey (‘What are your travel habits?’) was conducted to recruit car users pertaining to the target audience (prospective park and riders - PP&Rs). In this second survey, 1579 questionnaires were filled in. At the end of the selection, 130 PP&Rs were involved in the strategy. In particular, 86 individuals took part in the personalised travel plan programme (PTP) and 44 car users, not involved in the PTP, were kept as the control group. These 44 individuals were selected so as to have the same daily travel pattern characteristics as the PP&Rs involved in the PTP, but they did not take part in the two-week programme (Meloni et al., 2016; Sanjust et al., 2015).

2 One week's observation, after the Personalised Travel Plan delivery, may not be sufficient to detect whether any sustained change in travel behaviour has occurred. From our perspective, the post-measurement that immediately followed the PTP provision (Week 2) allowed us to identify those individuals who decided to try out the suggested alternatives. However, in order to check for lasting changes in behaviour, a monitoring survey was carried out three months after the completion of the two-week programme by means of a web survey for the purpose of tracking behaviour changes and accompanying the dynamic process of change (Meloni et al., 2016; Sanjust et al., 2015).

3 For the P&Rs the personalised travel plan was used to confirm the personal benefits achieved using the light rail in park-and-ride mode instead of private car.

4 This simplification was made because the focus of the analysis was to assess the propensity of car users to try the light rail after the delivery of a personalised travel plan. ‘Car as drivers’ were selected on the basis of their residential locations, work locations and actual travel modes form home to work (the car as driver mode), checking beforehand whether the light rail could offer a convenient alternative means of transport. This is why we did not deeply analyze here all the non-working day trip chains.

5 Please note that the park and ride mode is displayed only for the light metro, that is classified as ‘light metro and combined’.

6 Three months after completion of PTP the survey ‘What are your travel habits after Casteddu Mobility Styles?’, data revealed that 36% of the 86 prospective P&Rs (31 individuals) had begun to put the suggestions provided in the PTP into practice, replacing car-use with the P&R mode. Consequently, this percentage had increased in the three months after the conclusion of the programme.> (Sanjust et al., 2015).

7 Compared to the estimation results reported in Sanjust et al., 2014, the inclusion of the tour variables results in some change of significance for certain variables. However, in some cases, we have retained variables that are not statistically significant at a 0.05 significance level (see t-stat column) (see asterisks in Table 4) because of their intuitive effects. The effect of PTP itself is not significantly different from zero. Indeed, once the interactions of the PTP effects with exogenous variables are incorporated into the model, there is no statistically significant shift effect that increases the individual propensity to use the LR mode (because of the PTP).

Additional information

Funding

The Authors are grateful to the Sardinian Government for funding the project [grant number CRP3_146].