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Abstract: : Long-distance (LD) or intercity travel is getting less attention by researchers than usual daily trips. There is no specific definition for this kind of trip at the national, provincial, and inter-regional levels. At the same time, it has a high contribution to transportation in terms of distance travelled. This paper presents a model comparison method for (LD) trip generation model for LD trips performed by residents of Canada. The terms of « long-distance » trip based on Travel Survey for Residents in Canada (TSRC) survey is considered as non-frequent overnight and day trip. This study compared several machine learning methods for the trip generation model. Since LD trip is relatively rare, the data set of TSRC is considered imbalanced data, three different techniques on the data preparation level as part of rare event modelling (over, under, and synthetically oversampling) employed to handle the issue of imbalanced data. TSRC data from 2012 to 2017 was used for model estimation. Among the random forest, CART, CTree, and logit models, it was found that the random forest has the best performance in prediction, and decision tree models have the best overall accuracy. Also, Income level and educational level play an essential role in the occurrence of an intercity trip. The paper highlights the importance of improvement in intercity travel survey methods and other data collection methods.

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Abstract: : Evolutionary algorithms have been used extensively over the past 2 decades to provide solutions to the Transit Network Design Problem and the Transit Network and Frequencies Setting Problem. Genetic algorithms in particular have been used to solve the multi-objective problem of minimizing transit users’ and operational costs. By finding better routes geometry and frequencies, evolutionary algorithms proposed more efficient networks in a timely manner. However, to the knowledge of the authors, no experimentation included precise and complete pedestrian network data for access, egress and transfer routing. Moreover, the accuracy and representativeness of the transit demand data (Origin Destination matrices) are usually generated from fictitious data or survey data with very low coverage and/or representativity. In this paper, experiments conducted with three medium-sized cities in Quebec demonstrate that performing genetic algorithm optimizations using precise local road network data and representative public transit demand data can generate plausible scenarios that are between 10 and 20% more efficient than existing networks, using the same parameters and similar fleet sizes.

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Abstract: : Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggregating individual trips and then perform forecasting based on the values in the past several steps. However, this approach essentially overlooks the fact that passenger flow consists of trips from each individual traveler. For example, a traveler’s work trip in the morning can help predict his/her home trip in the evening, while this causal structure cannot be explicitly encoded in standard time series models. In this paper, we propose a new forecasting framework for boarding flow by incorporating the generative mechanism into standard time series models and leveraging the strong regularity rooted in travel behavior. In doing so, we introduce returning flow from previous alighting trips as a new covariate, which captures the causal structure and long-range dependencies in passenger flow data based on travel behavior. We develop the return probability parallelogram (RPP) to summarize the causal relationships and estimate the return flow. The proposed framework is evaluated using real-world passenger flow data, and the results confirm that the returning flow—a single covariate—can substantially and consistently improve various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. And the proposed method is more effective for business-type stations with most passengers come and return within the same day. This study can be extended to other modes of transport, and it also sheds new light on general demand time series forecasting problems, in which causal structure and long-range dependencies are generated by the user behavior.

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Abstract: : Inferring trip destination in smart card data with only tap-in control is an important application. Most existing methods estimate trip destinations based on the continuity of trip chains, while the destinations of isolated/unlinked trips cannot be properly handled. We address this problem with a probabilistic topic model. A three-dimensional latent dirichlet allocation model is developed to extract latent topics of departure time, origin, and destination among the population; each passenger’s travel behavior is characterized by a latent topic distribution defined on a three-dimensional simplex. Given the origin station and departure time, the most likely destination can be obtained by statistical inference. Furthermore, we propose to represent stations by their rank of visiting frequency, which transforms divergent spatial patterns into similar behavioral regularities. The proposed destination estimation framework is tested on Guangzhou Metro smart card data, in which the ground-truth is available. Compared with benchmark models, the topic model not only shows increased accuracy but also captures essential latent patterns in passengers’ travel behavior. The proposed topic model can be used to infer the destination of unlinked trips, analyze travel patterns, and passenger clustering.

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Abstract: : 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. Mobility as a Service (MaaS) and multimodality (or intermodality) are increasingly fashionable concepts in urban transport research. They are generally presented as essential in the move toward more sustainable mobility, even more now that alternatives to the exclusive use of a private car are emerging. Consequently, many projects focus on the development of technologies (such as mobile applications) to integrate several transportation modes. However, there is a lack of knowledge about multimodal behaviors among travelers, their associated attributes, and motivations. Few tools have been proposed to quantify and explain mode use variability. It, therefore, remains difficult for practitioners to measure the impacts of strategies implemented to promote a more diversified mobility. This paper aims at assessing the influence transport supply may have on the multimodality level of travel behaviors over a five-year period. This required first defining an indicator to summarize the intensities of use of several modes of transport. This indicator was calculated by type of people (aggregated by socio-demographic and spatial attributes) using the last two (2013 and 2018) Montreal household surveys. The evolution by type of people between the two surveys was then cross-analyzed with changes in transport supply. To this end, a difference-in-differences approach that allows causal inference was applied. As a result, this paper provides a means to guide interventions to increase multimodality. This paper is organized as follows. First, some former studies into analyzing and quantifying multimodal mobility, as well as factors affecting multimodal travel behavior, are reviewed. The household surveys used in this paper are then described, and some methodological elements are exposed. The results are reported in three parts: the typology of people is presented; a multimodality indicator is then applied by type; and the causal effect of transport supply on the evolution of multimodality between 2013 and 2018 is modeled. Finally, the paper is concluded and the discussion feeds into the programming of future work.

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Abstract: : Revenue management is important for carriers (e.g., airlines and railroads). In this paper, we focus on cargo capacity management, which has received less attention in the literature than its passenger counterpart. More precisely, we focus on the problem of controlling booking accept/reject decisions: Given a limited capacity, accept a booking request or reject it to reserve capacity for future bookings with potentially higher revenue. We formulate the problem as a finite-horizon stochastic dynamic program. The cost of fulfilling the accepted bookings, incurred at the end of the horizon, depends on the packing and routing of the cargo. This is a computationally challenging aspect as the latter are solutions to an operational decision-making problem, in our application a vehicle routing problem (VRP). Seeking a balance between online and offline computation, we propose to train a predictor of the solution costs to the VRPs using supervised learning. In turn, we use the predictions online in approximate dynamic programming and reinforcement learning algorithms to solve the booking control problem. We compare the results to an existing approach in the literature and show that we are able to obtain control policies that provide increased profit at a reduced evaluation time. This is achieved thanks to accurate approximation of the operational costs and negligible computing time in comparison to solving the VRPs.

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Abstract: : Mobility is an important tool for the social inclusion of PWDs (people with disabilities). Unfortunately, the number of paratransit users, in most cities, is rapidly growing while resources remain limited. Paratransit is one of the rare public transport services that are adapted to the needs of users with disabilities. Still, it may be possible to shift some paratransit trips to the RTN (regular transit network). The aim of this paper is to develop and test criteria to identify paratransit trips that could be shifted to the RTN with the best potential of success. Four criteria are proposed to identify these trips. 1) The autonomy level of paratransit users: they must be able to use the RTN by themselves. 2) The universal design of each metro station, bus stop and vehicle. 3) The spatial location of both origin and destination of the trips and their proximity to the RTN, to avoid long walking/rolling distances. 4) The frequency of the trips: how many times over a year does a user performs a specific trip. We propose a method to evaluate these criteria. These four criteria allow prioritizing paratransit trips that could be shifted the RTN with the most potential of success. With these criteria, the safety and the satisfaction of users could be maintained while reducing operation costs of paratransit services. The method has been applied to the paratransit trips of the STM (Montreal Transit Authority) and 15% (400,000 trips) of them could be shifted to the RTN in short term. This research is a step forward to shift paratransit trips to RTN. Still, the proposed criteria need to be refined and more criteria could be added to improve the prioritization of most promising paratransit trips. Nevertheless, the application of this methodology can assist stakeholders in making choices about the mobility services for PWDs.

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Abstract: : Car ownership is linked to higher car use, which leads to important environmental, social and health consequences. As car ownership keeps increasing in most countries, it remains relevant to examine what factors and policies can help contain this growth. This paper uses an advanced spatial econometric modeling framework to investigate spatial dependences in household car ownership rates measured at fine geographical scales using administrative data of registered vehicles and census data of household counts for the Island of Montreal, Canada. The use of a finer level of spatial resolution allows for the use of more explanatory variables than previous aggregate models of car ownership. Theoretical considerations and formal testing suggested the choice of the Spatial Durbin Error Model (SDEM) as an appropriate modeling option. The final model specification includes sociodemographic and built environment variables supported by theory and achieves a Nagelkerke pseudo-R2 of 0.93. Despite the inclusion of those variables the spatial linear models with and without lagged explanatory variables still exhibit residual spatial dependence. This indicates the presence of unobserved autocorrelated factors influencing car ownership rates. Model results indicate that sociodemographic variables explain much of the variance, but that built environment characteristics, including transit level of service and local commercial accessibility (e.g., to grocery stores) are strongly and negatively associated with neighborhood car ownership rates. Comparison of estimates between the SDEM and a non-spatial model indicates that failing to control for spatial dependence leads to an overestimation of the strength of the direct influence of built environment variables.

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Abstract: : Streets have long been designed to maximize motor vehicle throughput, ignoring other street users. Many cities are now reversing this trend and implementing policies to design more equitable streets. However, few existing tools and metrics enable widescale assessment, evaluation, and longitudinal tracking of these street space rebalancing efforts, i.e., assessing how equitable the current street design is, how it can be improved, and how much progress has been made. This paper develops a needs-gap methodology for assessing the discrepancy between transportation supply and demand in urban streets using existing datasets and automated methods. The share of street space allocated to different street users is measured in 11 boroughs of Montréal, Canada. Travel survey data is used to estimate the observed and potential travel demand in each borough in the AM peak period. A needs-gap analysis is then carried out. It is found that bus riders and cyclists face the greatest needs-gap across the study area, especially in central boroughs. The needs-gap also increases if only trips produced or attracted by a borough are considered. This shows the potential of applying an equity-based framework to the automated assessment of street space allocation in cities using large datasets.

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Abstract: : Sedentary lifestyle is an important public health issue. To prevent this problem, major health organizations promote the inclusion of physical activity in daily life. Active modes are therefore a well-known way of achieving the health recommendations but walking to transit has also been studied recently. The goal of this study is to assess the level of physical activity achieved by using transit, to verify its contribution in reaching the recommendations. The paper aims to assess the energy expenditure associated with transit use by analyzing the related Metabolic Equivalent of Task. This allows us to express trips as physical activity expenditures and to integrate them in the daily pool of physical activities. For this study, only the main variables affecting the intensity of physical activity are considered. These are the walking time and slope encountered during the walking portion of transit trips. This estimation allows us to estimate the level of physical activity reached by transit users and assess the potential physical activity drivers could achieve if they switched to transit. Finally, the method is also applied to a current transportation issue in Montreal. Results show that transit users living in the Montreal area can achieve 54% of their recommended daily physical activity just by using transit. Current users of motorized modes, if they were to change to transit for their daily travels, could achieve 85% of the recommended daily physical activity. Sedentary lifestyle is an important public health issue. To prevent this problem, major health organizations, namely the World Health Organization and U.S. Department of Health and Human Services, promote the inclusion of physical activity in daily life. They suggest accomplishing 150 min of moderate physical activity weekly, or in other words, 30 min of activity five times a week. This physical activity can be part of leisure activities, but also productive ones such as walking for transportation. Active modes are therefore a well-known way of achieving the health recommendations but walking to transit has also been studied recently. Therefore, the goal of this study is to assess the physical activity level accomplished by using transit to verify the contribution of this mode to public and personal health. A literature review is first conducted to understand the issues of sedentary lifestyle and the contribution of transit for health. The method to assess physical activity in a public transportation context is also explored using the 2013 Montreal Origin–Destination Survey and elevation information for the transit stops and activity locations. The walking distances are calculated using a platform named Transition (1) and the slopes are derived using estimated paths and elevations. An estimation of the walking distances inside the metro and train stations is also performed. The application of this method using data from the Montreal area leads to an overview of the level of physical activity for the transit users and estimation of the potential level of physical activity for non-transit users under a transfer to transit assumption. Finally, a possible application of the method is also exposed on a current transportation issue in Montreal and some perspectives are proposed.

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Abstract: : Traffic flow predictions are central to a wealth of problems in transportation. Path choice models can be used for this purpose, and in state-of-the-art models—so-called recursive path choice (RPC) models—the choice of a path is formulated as a sequential arc choice process using undiscounted Markov decision process (MDP) with an absorbing state. The MDP has a utility maximization objective with unknown parameters that are estimated based on data. The estimation and prediction using RPC models require repeatedly solving value functions that are solutions to the Bellman equation. Although there are several examples of successful applications of RPC models in the literature, the convergence of the value iteration method has not been studied. We aim to address this gap. For the two closed-form models in the literature—recursive logit (RL) and nested recursive logit (NRL)—we study the convergence properties of the value iteration method. In the case of the RL model, we show that the operator associated with the Bellman equation is a contraction under certain assumptions on the parameter values. On the contrary, the operator in the NRL case is not a contraction. Focusing on the latter, we study two algorithms designed to improve upon the basic value iteration method. Extensive numerical results based on two real data sets show that the least squares approach we propose outperforms two value iteration methods.

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Abstract: : In the context of sustainable mobility policies, carsharing services have gained importance as an alternative to personal vehicles. In an effort to increase the adherence to and use of such services, several studies have explored the key factors that determine use and membership. Although the ease with which individuals can access shared vehicles appears to be a central determinant, few studies have specifically investigated how to measure station and vehicle accessibility. To fill this gap, this study seeks to systematically assess and compare the contribution of different accessibility indicators to modeling carsharing membership rate, using 2016 data from the Montreal carsharing company Communauto and from the Canadian census. Three indicators of accessibility to in-station vehicles are generated: walking only, public transport only, and multimodal accessibility (walking and public transport), considering a variety of travel time thresholds and cost functions. A linear regression model is then generated to assess the contribution of the different indicators to modeling membership rates, while controlling for socio-economic and commuting characteristics. The results show that walking accessibility, within 20 minutes, and public transport accessibility, within 40 minutes, are both key determinants of membership rate and in a complementary manner. The influence of public transport accessibility is positive and highest when walking accessibility is low. The results also demonstrate that the use of a cumulative or weighted-opportunity indicator is equally sound from an empirical perspective. The study is of relevance to researchers and planners wishing to better understand and model the influence of vehicle accessibility.

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Abstract: : The transportation sector is a major contributor to greenhouse gas (GHG) emissions, accounting for 14% of global emissions in 2010 according to the United States Environmental Protection Agency. In Quebec, this share amounts to 43%, of which 80% is caused by road transport according to the Ministére de l’Environnement et de la Lutte contre les changements climatiques of Québec. It is therefore essential to support the actions taken to reduce GHGs emissions from this sector and to quantify the impact of these actions. To do so, accurate and reliable emission models are needed. Driving cycles are defined as speed profiles over time and they are a key element of emission models. They represent driving behaviors specific to various road types in each region. The most widely used method to construct driving cycles is based on Markov chains and consists of concatenating small sections of speed profiles, called microtrips, following a transition matrix. Two of the main steps involved in the development of driving cycles are microtrip segmentation and microtrip classification. In this study, several combinations of segmentation and clustering methods are compared to generate the most reliable driving cycle. Results show that segmentation of microtrips with a fixed distance of 250 m and clustering of the microtrips by applying a principal component analysis on many key parameters related to their speed and acceleration provide the most accurate driving cycles. Climate change has become a global concern and its impacts are well known: rising sea levels, extreme weather conditions, mass extinction, and so forth. Because of the urgent need to address this problem effectively, it is necessary to promote measures that cost the least to implement while having the greatest impact. For example, in Quebec in 2016, 43% of greenhouse gas (GHG) emissions causing global warming came from the transportation sector (2). Therefore, GHG reduction measures targeting this sector have the potential to have a significant impact. To accurately measure these impacts, it is necessary to have tools such as driving cycles to measure GHG emissions and the fuel consumption of motorized vehicles. Driving cycles are speed profiles over time that represent driving behaviors and they are a key element in modeling emissions precisely. The objective of this study is to evaluate the sensitivity of the driving cycles to variations in the construction process focusing on microtrip definitions and clustering methods using a Markov-chains approach to identify the best method. The paper is structured as follows. First, a synthesis of practices for developing driving cycles is provided. This is followed by the presentation of the selected methods to develop driving cycles. Then, the methodology used to compare the driving cycles outputted from the various methods is detailed. The performance of the different methods is then presented and discussed. Contributions, limitations, and perspectives conclude the paper.

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Abstract: As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models.

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Abstract: To date, there is no developed and validated way to assess urban smartness. When evaluating smart city mobility systems, different authors distinguish different indicators. After analysing the evaluation indicators of the transport system presented in the scientific articles, the most relevant and influential indicators were selected. This article develops a hierarchical evaluation model for evaluating a smart city transportation system. The indicators are divided into five groups called “factors”. Several indicators are assigned to each of the listed groups. A hybrid multi-criteria decision-making (MCDM) method was used to calculate the significance of the selected indicators and to compare urban mobility systems. The applied multi-criteria evaluation methods were simple additive weighting (SAW), complex proportional assessment (COPRAS), and technique for order preference by similiarity to ideal solution (TOPSIS). The significance of factors and indicators was determined by expert evaluation methods: the analytic hierarchy process (AHP), direct, when experts evaluate the criteria as a percentage (sum of evaluations of all criteria 100%) and ranking (prioritisation). The evaluation and comparison of mobility systems were performed in two stages: when the multi-criteria evaluation is performed according to the indicators of each factor separately and when performing a comprehensive assessment of the smart mobility system according to the integrated significance of the indicators. A leading city is identified and ranked according to the smartness level. The aim of this article is to create a hierarchical evaluation model of the smart mobility systems, to compare the smartness level of Vilnius, Montreal, and Weimar mobility systems, and to create a ranking.

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Abstract: In order to predict the monthly usage frequency of members of a car-sharing scheme by analysing the gradual change of behaviour over time, a new model is proposed based on the Markov Chains model with latent stages. The model accounts for changing patterns of frequency from soon after signing up to later stages by including five latent user ‘life stages’. In applying the model to panel data from Montreal’s free-floating carsharing service the authors calculate each user’s ’lifetime’ applied to ‘system operation time’, the time period since the start of the scheme. Three-fold validation reveals effective performance of the model for both lifetime and system operation time dimensions. The model is further applied to illustrate how previous carsharing experience and the extension of the scheme to a larger area can affect usage frequency changes. We conclude that this approach is effective for usage prediction for novel transport schemes.

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Abstract: In the realm of traffic assignment over a network involving rigid arc capacities, the aim of the present work is to generalize the model of Marcotte, Nguyen, and Schoeb [Marcotte P, Nguyen S, Schoeb A strategic flow model of traffic assignment in static capacitated networks. by casting it within a stochastic user equilibrium framework. The strength of the proposed model is to incorporate two sources of stochasticity stemming, respectively, from the users’ imperfect knowledge regarding arc costs (represented by a discrete choice model) and the probability of not accessing saturated arcs. Moreover, the arc-based formulation extends the Markovian traffic equilibrium model of Baillon and Cominetti (2008) through the explicit consideration of capacities. This paper is restricted to the case of acyclic networks, for which we present solution algorithms and numerical experiments.

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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.

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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.

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Abstract: Paratransit (door-to-door public transit services for people with disabilities) is a key element of the public transit system. This type of service can be very costly to operate, yet it is essential for social inclusion. The aim of this study was to develop a quantitative approach to estimate paratransit dwell times and improve trip scheduling. Dwell time is defined as the time required for a vehicle to stop to board or alight passengers. Data collected by the paratransit department of the Société de transport de Montréal (STM), the Montreal, Canada, public transit agency, between September 2014 and May 2018 was used to estimate a dwell time model. Over 5 million data points were analyzed using a multiple linear regression model. The model takes into consideration the type of vehicle used, passenger characteristics (ambulatory or wheelchair passenger, support person), the activity performed at the stop (boarding or alighting), the stop location, the time, day and month the trip took place, and the type of place (residential or non-residential) served. The results reveal all these variables have a significant impact on dwell times. Using these results, a method was developed to improve estimated dwell times in STM’s paratransit scheduling system. The new method was implemented on August 1, 2018. The difference between planned and actual travel times was measured, before and after the implementation of the new method. The results show the on-time performance of the service was improved which helped optimize routes and reduce associated operational costs.

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Abstract: Before thinking about implementing new transportation services, it is essential to assess the performances of the available ones and to develop an objective diagnosis of the adequacy between transportation supply and demand. This paper focuses on the refinement of a spatial–temporal clustering process able to encapsulate the spatial distribution of travel demand and supply. It illustrates the potential of such process to assist in the development of an objective diagnosis of the quality of the configuration of transit services. The two tools composing this process are presented in this paper, Traclus_DL and Grille_CR. A literature review is conducted on the main concepts such as corridors and grids, which will give a better understanding of the contributions proposed in this paper. Traclus_DL is a spatial clustering algorithm for desire lines (direct line from origin to destination) developed by Bahbouh. This paper will explain how this algorithm works and will also present improvements that were implemented to facilitate its usage and to give a better representation of the reality. Grille_CR is an automated smoothing tool which facilitates the visualization and the interpretation of the results produced by Traclus-DL. This paper explains how this process can be implemented and illustrates its relevance for public transport analysis and design. The major contribution of this paper is the implementation of a tool which helps better understand the spatial configuration of the demand in transport.

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Abstract: The phenomenon of “peak-car”, the growth in the use of active and collective modes and a renewed interest in more dense, mixed and human scale urban developments, all raise the question of the decline of car mobilities. A three-perspective analysis framework is proposed to assess, on the one hand, whether this decline is real and, on the other hand, whether it is accompanied by a paradigm shift in transport and urban planning that would indicate the end of automobility. The question is applied specifically for the province of Quebec and its two metropolitan areas, Montreal and Quebec. As a first perspective, the analysis of motorization and automobile use indicators reveals a sustained increase in car mobility over the past two decades. As a second perspective, the analysis of official planning documents and framework policies for mobility and urban development reveals an adequate understanding of mobility issues, but an uneven recognition of dependence on the automobile. In addition, none of the municipal and metropolitan documents presents specific objectives for reducing car use or car ownership. Finally, from a third perspective, the priority given to some infrastructure projects are not consistent with the objectives and visions of planning documents. Indeed, the benefits expected from ambitious public transit projects are compromised by highway development projects in Montreal as well as in Quebec City. The justifications for these road projects come from a classic planning paradigm widely shown to be outdated and inadequate. The priority given to them seems to stem from political resistance to a paradigm shift. Taken together, these three perspectives tend to show that despite certain positive signs, the decline in automobile mobility, which would be based on a real shift of paradigm in transportation and urban planning, does not seem to have started in Quebec.

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Abstract: This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

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Abstract: This chapter analyses how mode usage varies in the surrounding of bikesharing stations in the Montreal Area. Mobility interaction analysis zones are defined and used to construct vectors describing the daily patterns of usage of each mode as well as its intensity level. The analysis relies on the processing of streams of passive data from 6 modes of transportation (bikesharing, carsharing (1 station-based system and 2 free-floating services), transit (subway and bus) and taxi) to develop typical daily patterns of usage and visualize variability of usage from the perspective of time (one year), space (492 mobility interaction analysis zones in the surrounding of bikesharing stations) and transportation mode. Clustering methods are used to identify typical days of usage for all modes. Illustration of the insights gain by the developed typology is illustrated using various visualization views.

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Abstract: Land use and transportation scenarios can help evaluate the potential impacts of urban compact or transit-oriented development (TOD). Future scenarios have been based on hypothetical developments or strategic planning but both have rarely been compared. We developed scenarios for an entire metropolitan area (Montreal, Canada) based on current strategic planning documents and contrasted their potential impacts on car use and active transportation with those of hypothetical scenarios. We collected and analyzed available urban planning documents and obtained key stakeholders’ appreciation of transportation projects on their likelihood of implementation. We allocated 2006-2031 population growth according to recent trends (Business As Usual, BAU) or alternative scenarios (current planning; all in TOD areas; all in central zone). A large-scale and representative Origin-Destination Household Travel Survey was used to measure travel behavior. To estimate distances travelled by mode, in 2031, we used a mode choice model and a simpler method based on the 2008 modal share across population strata. Compared to the BAU, the scenario that allocated all the new population in already dense areas and that also included numerous public transit projects (unlikely to be implemented in 2031), was associated with greatest impacts. Nonetheless such major changes had relatively minor impacts, inducing at most a 15% reduction in distances travel by car and a 28% increase in distances walked, compared to a BAU. Strategies that directly target the reduction of car use, not considered in the scenarios assessed, may be necessary to induce substantial changes in a metropolitan area.

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Abstract: As part of strategic plans, we often see car dependency reduction vision along with strategies to reduce car use and vehicle-kilometers traveled while promoting alternatives such as transit and active modes. It is less common to see strategies to generate more structural changes, even if such change can have much more important and sustainable impacts. Whereas it is well known that home location is one of the key drivers of travel behaviors, it is much less frequent to have planners put forward strategies to encourage people to move and choose their locations more wisely with respect to their needs. This research aims to assess the potential collective gain of an optimal allocation of households to available dwellings. It aims to estimate how inefficient the current distribution is of households among the dwellings with respect to where all household members need to travel. Results show that the household relocations reduce the distances for work and study by 37.9%. This reduction saves an average of 13.8 km per household per day or 4.9 km per work or study trip. If the mode choice remains constant despite the new trip conditions following the household relocations, the total mileage for work and study trips would decrease by 42.8% for car drivers, by 35.2% for car passenger, by 13.3% for school bus, and 34.2% for public transport. As a result of the household relocations, walking and cycling latent trips increased, respectively, from 2.6% to 15.5% and 26.1% to 39.9% of motorized trips.

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Abstract: Many cities adopt strategies to increase the modal share of walking and cycling, aiming to reduce the negative impacts of car trips. Despite such projects, strategies and infrastructure promoting active modes, modal shares of walking (10.1%) and cycling (1.6%) remain relatively small compared to the car (54.3%) in the Greater Montreal Area. In this context, it seems relevant to access the upper bound of the potential of cycling and walking. This paper proposes a methodology to estimate the latent walking and cycling trips in an urban area using large scale Origin-Destination (OD) data. The method builds on previous research and accounts for the distance overlapping zone for walking and cycling trips to obtain a pooled estimation of active transportation latent trips. The methodology is mainly based on a sequential process using trips reported during the 2013 OD survey in Montreal. Results show that 5.2% of daily motorized trips (427,813 trips) could be made by walking and 19.4% (1,605,244 trips) by cycling. From these, 57.1% were made as car drivers. 2.8% of motorized trips could be transferred to both walking and cycling. These trips were allocated to either walking or cycling using an overlapping process based on trip distance: 45.9% of them (1.3% of total trips) are transferred to latent walking trips and 54.1% (1.5% of total trips) to latent cycling trips. When we consider latent trips, modal share of walking and cycling would respectively increase from 10.1% to 14.7% and from 1.6% to 18.7% while share of car driver would decrease from 54.3% to 42.5%.

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Abstract: The transportation sector is a major contributor to greenhouse gas (GHG) emissions, accounting for 14% of global emissions in 2010 according to the United States Environmental Protection Agency. In Québec, this share amounts to 43%, of which 80% is caused by road transport according to the Ministére de l’Environnement et de la Lutte contre les changements climatiques of Québec. It is therefore essential to support the actions taken to reduce GHGs emissions from this sector and to quantify the impact of these actions. To do so, accurate and reliable emission models are needed. Driving cycles are defined as speed profiles over time and they are a key element of emission models. They represent driving behaviors specific to various road types in each region. The most widely used method to construct driving cycles is based on Markov chains and consists of concatenating small sections of speed profiles, called microtrips, following a transition matrix. Two of the main steps involved in the development of driving cycles are microtrip segmentation and microtrip classification. In this study, several combinations of segmentation and clustering methods are compared to generate the most reliable driving cycle. Results show that segmentation of microtrips with a fixed distance of 250 m and clustering of the microtrips by applying a principal component analysis on many key parameters related to their speed and acceleration provide the most accurate driving cycles.

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Abstract: We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76%–62% in suburbs; 55%–34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061. Emissions of nitrogen dioxide (NO2) and carbon dioxide (CO2) were estimated and NO2 used with dispersion modeling to estimate concentrations. Walking and Public Transit (PT) use and corresponding distances walked in Y2061 were >70% higher for the Ideal scenario vs the BAU, while car share and distances were <40% lower. NO2 levels were slightly lower in the Ideal scenario vs the BAU, but always higher in the urban core. Health impacts, summarized with disability adjusted life years (DALY), differed between urban and suburb areas but globally, the Ideal scenario reduced the impacts of the Y2061 BAU by 33% DALY. Percentages of car and PT trips were similar for the Y2031 and Y2061 BAU but kms travelled by car, CO2 and NO2 increased, due to increased populations. Drastic measures to decrease car share appear necessary to substantially reduce impacts of transportation.

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Abstract: This study proposes a methodological framework to understand the behavior of bikeshare-metro-bikeshare (BMB) users and assess the complementarity of bikeshare and transit. This analysis was conducted using Montreal’s Bixi bikeshare data collected over an 8-year period. A k-medoid clustering analysis was performed using three variables describing users’ travel behavior: BMB rate, most frequent BMB trip share, and rate of use of different metro stations. It reveals six groups of BMB users: (1) regular commuters, (2) irregular commuters, (3) occasional commuters, (4) mixed users, (5) leisure users, and (6) utility users. Each group’s share of trips is stable over time. BMB users represent an increasing, yet still marginal, share of 1.8% of Bixi’s annual members. The bikeshare segments of BMB trips averaged 1,180 m, with a standard deviation of 830 m. This confirms bikeshare is useful to complete the first and last kilometer of transit trips. Moreover, BMB trips increased with the expansion of Montreal’s bikeshare network to suburban areas serviced by the metro. This study concludes that bikeshare-metro integration allows bikeshare users to cover greater distances and can thus increase both systems’ ridership.

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Abstract: The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals’ choice of paths are typically predicted using discrete choice models. This article is a tutorial on a specific category of discrete choice models called recursive, and it makes three main contributions: First, for the purpose of assisting future research on route choice, we provide a comprehensive background on the problem, linking it to different fields including inverse optimization and inverse reinforcement learning. Second, we formally introduce the problem and the recursive modeling idea along with an overview of existing models, their properties and applications. Third, we extensively analyze illustrative examples from different angles so that a novice reader can gain intuition on the problem and the advantages provided by recursive models in comparison to path-based ones.

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Abstract: Evolutionary algorithms have been used extensively over the past 2 decades to provide solutions to the Transit Network Design Problem and the Transit Network and Frequencies Setting Problem. Genetic algorithms in particular have been used to solve the multi-objective problem of minimizing transit users’ and operational costs. By finding better routes geometry and frequencies, evolutionary algorithms proposed more efficient networks in a timely manner. However, to the knowledge of the authors, no experimentation included precise and complete pedestrian network data for access, egress and transfer routing. Moreover, the accuracy and representativeness of the transit demand data (Origin Destination matrices) are usually generated from fictitious data or survey data with very low coverage and/or representativity. In this paper, experiments conducted with three medium-sized cities in Quebec demonstrate that performing genetic algorithm optimizations using precise local road network data and representative public transit demand data can generate plausible scenarios that are between 10 and 20% more efficient than existing networks, using the same parameters and similar fleet sizes.

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Abstract: A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

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Abstract: We use data from the Swiss national household travel survey to 1. analyze the socioeconomic determinants for intermodal travel in Switzerland and 2. estimate a first large-scale multimodal recursive logit route choice model for urban trip making. We show that intermodal travel is mostly associated with ownership of transit subscriptions, which allow free at the point-of-use public transportation. We also present a framework using open-source data to generate the multimodal network for the recursive logit model estimation. The fact that the model only needs a multimodal network to model the transport supply makes it independent of path sampling algorithms for the choice-set construction and it thus provides an alternative to classic mode and route choice models, since it can estimate mode and route choice parameters with directly observed routes, removing the sampling bias. By eliminating the need to sample alternative paths for estimation, it also simplifies the estimation process, making it a viable choice as an integral solution for joint route and mode choice modelling.

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Abstract: Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.

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Mémoires et thèses

Résumé : Cette thèse porte sur la modélisation du trafic dans les réseaux routiers et comment celle-ci est intégrée dans des modèles d’optimisation. Ces deux sujets ont évolué de manière plutôt disjointe: le trafic est prédit par des modèles mathématiques de plus en plus complexes, mais ce progrès n’a pas été incorporé dans les modèles de design de réseau dans lesquels les usagers de la route jouent un rôle crucial. Le but de cet ouvrage est d’intégrer des modèles d’utilités aléatoires calibrés avec de vraies données dans certains modèles biniveaux d’optimisation et ce, par une décomposition de Benders efficace. Cette décomposition particulière s’avère être généralisable par rapport à une grande classe de problèmes communs dans la littérature et permet d’en résoudre des exemples de grande taille. Le premier article présente une méthodologie générale pour utiliser des données GPS d’une flotte de véhicules afin d’estimer les paramètres d’un modèle de demande dit recursive logit. Les traces GPS sont d’abord associées aux liens d’un réseau à l’aide d’un algorithme tenant compte de plusieurs facteurs. Les chemins formés par ces suites de liens et leurs caractéristiques sont utilisés afin d’estimer les paramètres d’un modèle de choix. Ces paramètres représentent la perception qu’ont les usagers de chacune de ces caractéristiques par rapport au choix de leur chemin. Les données utilisées dans cet article proviennent des véhicules appartenant à plusieurs compagnies de transport opérant principalement dans la région de Montréal. Le deuxième article aborde l’intégration d’un modèle de choix de chemin avec utilités aléatoires dans une nouvelle formulation biniveau pour le problème de capture de flot de trafic. Le modèle proposé permet de représenter différents comportements des usagers par rapport à leur choix de chemin en définissant les utilités d’arcs appropriées. Ces utilités sont stochastiques ce qui contribue d’autant plus à capturer un comportement réaliste des usagers. Le modèle biniveau est rendu linéaire à travers l’ajout d’un terme lagrangien basé sur la dualité forte et ceci mène à une décomposition de Benders particulièrement efficace. Les expériences numériques sont principalement menées sur un réseau représentant la ville de Winnipeg ce qui démontre la possibilité de résoudre des problèmes de taille relativement grande.

Le troisième article démontre que l’approche du second article peut s’appliquer à une forme particulière de modèles biniveaux qui comprennent plusieurs problèmes différents. La décomposition est d’abord présentée dans un cadre général, puis dans un contexte où le second niveau du modèle biniveau est un problème de plus courts chemins. Afin d’établir que ce contexte inclut plusieurs applications, deux applications distinctes sont adaptées à la forme requise: le transport de matières dangereuses et la capture de flot de trafic déterministe. Une troisième application, la conception et l’établissement de prix de réseau simultanés, est aussi présentée de manière similaire à l’Annexe B de cette thèse.

Mots-clés : données GPS, choix de chemin, modèles récursifs de choix, terminaux inter-modaux, capture de flot, décomposition de Benders, optimisation biniveau, maximisation d’utilité aléatoire.

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Résumé : La livraison de marchandises est en enjeu important dans le développement des économies actuelles (Transport Canada, 2019). La demande pour le transport de marchandises ne cesse d’augmenter et le transport routier reste le moyen de transport le plus flexible afin de satisfaire cette demande. Néanmoins, le transport routier est responsable de 20,3% des émissions de gaz à effet de serre (GES) (Statistiques Canada, 2017) et les entreprises doivent s’adapter pour maintenir leur compétitivité tout en prenant en compte les externalités négatives liées à leurs opérations. Ce mémoire se concentre sur la planification du transport pour une société dont une partie des opérations consiste en la distribution de produits au Québec. Le réseau de distribution de l’entreprise est composé de succursales et de clients privés. La planification proposée se base sur l’étude de scénarios opérationnels basés sur différents paramètres (seuil de commande des clients, quantités commandées et fréquence de livraison notamment). L’ensemble de ce mémoire est divisé en plusieurs aspects. Le premier aspect couvert est la préparation de la base de données et des fichiers de traitement pour la création des tournées. Deuxièmement, un plan d’expérience complet sur 216 expériences est mis en place et les résultats obtenus sont présentés. Le plan d’expérience prend en compte des paramètres environnementaux (nombre de clients privés ou de succursales à livrer) et opérationnels (seuil de demande, quantité à livrer ainsi que la fréquence de livraison). Afin de pouvoir comparer les différents jeux de paramètres, des mesures de performance sont mises en place (marge de profit liée à la livraison, quantité de GES et distance totale parcourue). Les résultats obtenus par l’analyse des résultats du plan d’expérience démontrent que l’importance relative des paramètres opérationnels varie selon l’objectif poursuivi. Dans le cas où le planificateur cherche à minimiser la distance parcourue par la flotte de camions, aucun des paramètres opérationnels étudié ne démontre d’importance significative. Cependant l’étude de la composition des instances pour lesquelles la distance est minimisée retourne un profil de client distinct (seuil de commande compris entre 1400 et 1800 $), notamment composé d’un équilibre en succursales et clients privés, avec des tournées de véhicules marquées par un faible kilométrage (inférieures à 200 km) mais une haute densité de livraison. Dans le cas où l’objectif est de maximiser la marge de profit liée à la livraison, le paramètre prépondérant obtenu est la fréquence de livraison. On retire aussi de cette analyse que les clients qui maximisent la marge de profit de l’entreprise sont majoritairement situés dans des centres urbains et qu’il est préférable que ceux-ci présentent une fréquence de livraison moyenne plutôt que haute. En conclusion, plusieurs scénarios opérationnels sont développés afin d’aider à la prise de décisions. Le mémoire se conclut sur les limitations rencontrées par les outils mis en place et la méthodologie appliquée au long de cette étude.

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Résumé : Le transport au Québec est responsable en grande partie des émissions de gaz à effet de serre dans l’atmosphère. Une meilleure planification des services de transport en commun et actifs pourrait permettre la diminution de ceux-ci en favorisant un transfert modal au détriment de l’automobile. Cependant, la première étape consiste à effectuer un diagnostic de la situation et comprendre où se situent les lacunes du système. Dans la littérature, il existe de nombreuses méthodes telles des enquêtes ou des études d’indicateurs directs de service ou encore des études spatiales pour effectuer un bilan des services fournis. Un outil prometteur pour comprendre la structure spatio-temporelle de l’offre et de la demande de transport est Traclus_DL, « Trajectory Clustering for Desired lines » qui s’appuie sur le concept de corridors.

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Résumé : Que ce soit pour aller au travail, faire du magasinage ou participer à des activités sociales, la mobilité fait partie intégrante de la vie quotidienne. Nous bénéficions à cet égard d’un nombre grandissant de moyens de transports, ce qui contribue tant à notre qualité de vie qu’au développement économique. Néanmoins, la demande croissante de mobilité, à laquelle s’ajoutent l’expansion urbaine et l’accroissement du parc automobile, a également des répercussions négatives locales et globales, telles que le trafic, les nuisances sonores, et la dégradation de l’environnement. Afin d’atténuer ces effets néfastes, les autorités cherchent à mettre en oeuvre des politiques de gestion de la demande avec le meilleur résultat possible pour la société. Pour ce faire, ces dernières ont besoin d’évaluer l’impact de différentes mesures. Cette perspective est ce qui motive le problème de l’analyse et la prédiction du comportement des usagers du système de transport, et plus précisément quand, comment et par quel itinéraire les individus décident de se déplacer. Cette thèse a pour but de développer et d’appliquer des modèles permettant de prédire les flux de personnes et/ou de véhicules dans des réseaux urbains comportant plusieurs modes de transport. Il importe que de tels modèles soient supportés par des données, génèrent des prédictions exactes, et soient applicables à des réseaux réels. Dans la pratique, le problème de prédiction de flux se résout en deux étapes. La première, l’analyse de choix d’itinéraire, a pour but d’identifier le chemin que prendrait un voyageur dans un réseau pour effectuer un trajet entre un point A et un point B. Pour ce faire, on estime à partir de données les paramètres d’une fonction de coût multi-attribut représentant le comportement des usagers du réseau. La seconde étape est celle de l’affectation de trafic, qui distribue la demande totale dans le réseau de façon à obtenir un équilibre, c.-à-d. un état dans lequel aucun utilisateur ne souhaite changer d’itinéraire. La difficulté de cette étape consiste à modéliser la congestion du réseau, qui dépend du choix de route de tous les voyageurs et affecte simultanément la fonction de coût de chacun. Cette thèse se compose de quatre articles soumis à des journaux internationaux et d’un chapitre additionnel. Dans tous les articles, nous modélisons le choix d’itinéraire d’un individu comme une séquence de choix d’arcs dans le réseau, selon une approche appelée modèle de choix d’itinéraire récursif. Cette méthodologie possède d’avantageuses propriétés, comme un estimateur non biaisé et des procédures d’affectation rapides, en évitant de générer des ensembles de chemins. Néanmoins, l’estimation de tels modèles pose une difficulté additionnelle puisqu’elle nécessite de résoudre un problème de programmation dynamique imbriqué, ce qui explique que cette approche ne soit pas encore largement utilisée dans le domaine de la recherche en transport. Or, l’objectif principal de cette thèse est de répondre des défis liés à l’application de cette méthodologie à des réseaux multi-modaux. La force de cette thèse consiste en des applications à échelle réelle qui soulèvent des défis computationnels, ainsi que des contributions méthodologiques. Le premier article est un tutoriel sur l’analyse de choix d’itinéraire à travers les modèles récursifs susmentionnés. Les contributions principales sont de familiariser les chercheur.e.s avec cette méthodologie, de donner une certaine intuition sur les propriétés du modèle, d’illustrer ses avantages sur de petits réseaux, et finalement de placer ce problème dans un contexte plus large en tissant des liens avec des travaux dans les domaines de l’optimisation inverse et de l’apprentissage automatique. Deux articles et un chapitre additionnel appartiennent à la catégorie de travaux appliquant la méthodologie précédemment décrite sur des réseaux réels, de grande taille et multi-modaux. Ces applications vont au-delà des précédentes études dans ce contexte, qui ont été menées sur des réseaux routiers simples. Premièrement, nous estimons des modèles de choix d’itinéraire récursifs pour les trajets de cyclistes, et nous soulignons certains avantages de cette méthodologie dans le cadre de la prédiction. Nous étendons ensuite ce premier travail afin de traiter le cas d’un réseau de transport public comportant plusieurs modes. Enfin, nous considérons un problème de prédiction de demande plus large, où l’on cherche à prédire simultanément l’enchaînement des trajets quotidiens des voyageurs et leur participation aux activités qui motivent ces déplacements. Finalement, l’article concluant cette thèse concerne la modélisation d’affectation de trafic. Plus précisément, nous nous intéressons au calcul d’un équilibre dans un réseau où chaque arc peut posséder une capacité finie, ce qui est typiquement le cas des réseaux de transport public. Cet article apporte d’importantes contributions méthodologiques. Nous proposons un modèle markovien d’équilibre de trafic dit stratégique, qui permet d’affecter la demande sur les arcs du réseau sans en excéder la capacité, tout en modélisant comment la probabilité qu’un arc atteigne sa capacité modifie le choix de route des usagers.

Mots-clés : Modèles de choix d’itinéraire récursifs, Modèle markovien d’équilibre de trafic, Estimation par maximum de vraisemblance, Programmation dynamique, Réseaux multi-modaux, Recursive route choice models, Maximum likelihood estimation, Dynamic programming, Multi-modal route choice, Markovian traffic assignment model, Activity-based travel demand.

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Résumé : L’achalandage des réseaux de transport en commun varie en fonction de nombreux paramètres. Des facteurs exogènes tels que la météo sont souvent rapportés dans la littérature mais il existe aussi des facteurs individuels : en effet, chaque usager a une utilisation temporelle et spatiale du transport en commun qui lui est propre. D’une part, des différences sont visibles entre les individus. Cette variabilité interpersonnelle est particulièrement prononcée dans les réseaux qui desservent un grand nombre de personnes du fait de la grande hétérogénéité des comportements observés.

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Pirie S., Dandres T., Trépanier M., Gendron B. (2020). Examen des potentialités d’utilisation des infrastructures de transport collectif à des fins de transport de marchandises en milieu urbain. Rapport à CargoM.

Pedroli F., Mousseau N. (2020). La mobilité comme service au Québec. Institut de l’énergie Trottier.

Pedroli F., Mousseau N. (2020). Rapport sur les entretiens effectués aux îles-de-la-Madeleine. Institut de l’énergie Trottier. Les rapports de l’accélérateur de transition, volume 2, numéro 1. Juin 2020.

Pedroli F., Mousseau N. (2020). Proposition de scénarios pour une évolution des modes de transport aux Îles-de-la-Madeleine. Institut de l’énergie Trottier. Les rapports de l’accélérateur de transition, volume 2, numéro 2. Juin 2020.

Rohmer S., Gendron B. (2020). A guide to parcel lockers in last mile distribution – Highlighting changes and opportunities from an OR perspective. CIRRELT 2020-11. Avril 2020.

Sarrazin F., Gendron B., Trépanier M. (2020). Recherche opérationnelle et IA en transport de marchandises : une solution pour la crise climatique (2020), Vecteur Environnement, Vol 53(4), 16-19. Décembre 2020.

Frejinger, E. et Zimmermann, M. (2020). Route Choice and Network Modeling. Encyclopedia of Transportation, Elsevier. Accepted, 2020 (book chapter).

Gendron, B., Metnani, A. (2019). Étude exploratoire d’innovation logistique : Impact environnemental de l’exploitation du réseau piétonnier souterrain pour la livraison de colis au centre-ville. Document confidentiel présenté à Transition énergétique Québec.

Morin, L., Bastin, F., Frejinger, E., Trépanier, M. (2019). Modelling Truck Route Choices in an Urban Area Using a Recursive Logit Model and GPS Data, Sustainable City Logistics, NovaScience publishers (book chapter).

Pedroli, F. et Mousseau, N. (2019). La mobilité comme service au Québec. Polytechnique Montréal, Université de Montréal et Institut de l’énergie Trottier.

Bourbonnais P.L., Morency, C., Trépanier, M. (2018). Approche nouvelle et moderne de planification des réseaux de transport collectifs et alternatifs, Routes et Transports, 47(1), 44-49, 2018.

Gendron, B., Metnani, A. (2018). Impact environnemental de nouvelles stratégies logistiques pour la livraison de colis au centre-ville. Document confidentiel présenté à Transition énergétique Québec.

Da Silva, S., Déméné, C., Lessard, I., Laviolette, J. (2018). Obstacles et leviers aux changements de comportements des Québécois.