Using a GPS Active Logger to Implement Travel Behaviour Change Programs

Meloni, Italo; Sanjust, Benedetta

doi:10.4018/978-1-4666-6170-7.ch020

Cited by 5 publications

(1 citation statement)

References 21 publications

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Section: Data Description and Methodologymentioning

confidence: 99%

Understanding Route Switch Behavior: An Analysis Using GPS Based Data

Vacca

Meloni

2015

Transportation Research Procedia

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The objective of this paper is to study route switch behavior to detect which trip and individual characteristics most influence the choice of multiple routes for the same origin-destination (OD) trip. In this study we used a database of 361 morning commute trips, regarding 66 users, collected in the metropolitan area of Cagliari (Italy) during the "Casteddu Mobility Styles" survey. Data were collected for a 14 days period through a personal probe system called Activity Locator , a smartphone that integrates a GPS logger for the acquisition of the routes and an activity/travel diary. Mixed logit models are estimated, in order to take into account the variability of user perception. Results show that route switch behavior is influenced by the number of traffic lights per km, percent of highways, time perception, gender, age, individual income and driving experience in relation with the minutes per km.

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Section: Data Description and Methodologymentioning

confidence: 99%

Understanding Route Switch Behavior: An Analysis Using GPS Based Data

Vacca

Meloni

2015

Transportation Research Procedia

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Lessons learned from a personalized travel planning (PTP) research program to reduce car dependence

2016

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Voluntary travel behavior change programs have been implemented worldwide\ud since the late 1990s at a personal and community level. Most of the now completed\ud programs were commissioned by local authorities to commercial firms, in an attempt to\ud reduce private car use. In this context, the evaluation and review of the reliability of these\ud policy measures have been at the core of most of the debates and studies in this field. In this\ud paper, we describe the lessons learned from a research program funded by the Sardinian\ud Government (Italy), aimed at testing a soft transport policy measure for reducing car\ud dependence. In particular, the work reviews in detail the methodological approach and\ud participants’ feedback on a personalized travel plan (PTP). After implementation of the\ud soft measure, the PTP participants were divided into two groups depending on whether\ud they had reduced car use or not, and separate analyses were conducted to highlight the\ud factors underlying different behavior change decisions. General conclusions regarding the\ud effectiveness of the PTP are beyond the scope of the present study

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Should I stay or should I go? Investigating route switching behavior from revealed preferences data

2017

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A large variety of factors influence the route choice decisions of road users, and modelers consider these factors within the perceived utility that road users are assumed to maximize. However, this perceived utility may be different even for the same origin–destination pair and this leads road users to choose different routes for different trips. In this study, we focus on this particular phenomenon of route switching behavior by estimating discrete choice models with the aim of understanding the key factors at its foundation. The estimated route choice models account for route characteristics, socioeconomic information, activity based data, inertial mechanism and learning effects, and they are applied to revealed preference data consisting of 677 actual day by day route choices (referred to 77 road users) collected by GPS in Cagliari (Italy). Route switching models were estimated with both fixed and random coefficient models. The model estimation results show that the variables referred to habit and learning have an important relevance on explaining the route switching phenomenon. Specifically, the higher is the travel habit, the less is the propensity of the road users to switch their route. Moreover, the learning effect shows that the accumulation of past experiences has more influence on the choice than the most recent ones

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Using a GPS Active Logger to Implement Travel Behaviour Change Programs

Cited by 5 publications

References 21 publications

Understanding Route Switch Behavior: An Analysis Using GPS Based Data

Understanding Route Switch Behavior: An Analysis Using GPS Based Data

Lessons learned from a personalized travel planning (PTP) research program to reduce car dependence

Should I stay or should I go? Investigating route switching behavior from revealed preferences data

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