Nos publications et articles scientifiques !
Articles scientifiques
Abstract: Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy. To this end, we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers, travel diaries and link travel times on the overall network. Though such combined data sets exist, these have not yet been used to investigate route choice behaviour. A dynamic network in which travel times change over time has been used for the estimation of both recursive logit and nested models. Prediction and estimation results are compared to those obtained for a static network. The interpretation of parameter estimates and prediction accuracy differ substantially between dynamic and static networks as well as between models with correlated and uncorrelated utilities. Contrary to the static results, for the dynamic, where travel times are modelled more accurately, travel information does not have a significant impact on route choice behaviour. However, having travel information increases the travel comfort, as interviews with participants have shown.
Abstract: Continuous household travel surveys have been identified as a potential replacement for traditional one-off cross-sectional surveys. Many regions around the world have either replaced their traditional cross-sectional survey with its continuous counterpart, or are weighing the option of doing so. The main claimed advantage of continuous surveys is the availability of data over a continuous spectrum of time, thus allowing for the investigation of the temporal variation in trip behavior. The objective of this paper is to put this claim to the test: Can continuous household travel surveys capture the temporal variation in trip behavior? This claim can be put to the test by estimating mixed effects models on the individual, household, spatial and modal level using date stemming from the Montreal Continuous Survey (2009–2012). A mixed effects model (also know as a hierarchical or multilevel model) respects the hierarchical design of a household survey by nesting or crossing entities where necessary. The use of a mixed effects econometric framework allows for partitioning the variance of the dependent variable to a set of grouping factors, strengthening the understanding of the underlying causes of variation in travel behavior. The findings of the paper conclude that the temporal variability in trip behavior is only observed when modelling on the regional level. Further, the study suggests that a large proportion of the variance of trip behavior is attributed to different grouping factors, such as region or municipal sector for regional trip behavior models.