Using 5 parallel passive data streams to report on a wide range of mobility options

Morency, Catherine; Trépanier, Martin; Saunier, Nicolas; Verreault, Hubert; Bourdeau, Jean-Simon

doi:10.1016/j.trpro.2018.10.014

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Furthermore, multimodal travel behavior can be analyzed at the systemic level using passive data streams and data mining methods ( 12 , 13 ). The cross-analyses of the cited authors highlighted correlations of use between several transport modes in space and time.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Changes in Multimodal Travel Behavior Resulting from Transport Supply Improvement

Deschaintres

Morency

Trépanier

2021

Transportation Research Record

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Despite the desired transition toward sustainable and multimodal mobility, few tools have been developed either to quantify mode use diversity or to assess the effects of transportation system enhancements on multimodal travel behaviors. This paper attempts to fill this gap by proposing a methodology to appraise the causal impact of transport supply improvement on the evolution of multimodality levels between 2013 and 2018 in Montreal (Quebec, Canada). First, the participants of two household travel surveys were clustered into types of people (PeTys) to overcome the cross-sectional nature of the data. This allowed changes in travel behavior per type over a five-year period to be evaluated. A variant of the Dalton index was then applied on a series of aggregated (weighted) intensities of use of several modes to measure multimodality. Various sensitivity analyses were carried out to determine the parameters of this indicator (sensitivity to the least used modes, intensity metric, and mode independency). Finally, a difference-in-differences causal inference approach was explored to model the influence of the improvement of three alternative transport services (transit, bikesharing, and station-based carsharing) on the evolution of modal variability by type of people. The results revealed that, after controlling for different socio-demographic and spatial attributes, increasing transport supply had a significant and positive impact on multimodality. This outcome is therefore good news for the mobility of the future as alternative modes of transport emerge.

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Section: Introductionmentioning

confidence: 99%

“…Mode choice variability can also be explained by exogenous temporal factors such as seasonality, social activities, service disruptions, or weather conditions ( 13 , 20 ). On the space dimension, multimodality tends to be higher in densely populated urban centers, especially where access to a subway system is provided ( 9 , 10 , 14 ).…”

Section: Introductionmentioning

confidence: 99%