Manon Seppecher’s research while affiliated with Gustave Eiffel University and other places

Accurately estimating traffic variables across unequipped portions of a network remains a significant challenge due to the limited coverage of sensor-equipped links, such as loop detectors and probe vehicles. A common approach is to apply uniform scaling, treating unequipped links as equivalent to equipped ones. This study introduces a novel framework to improve traffic variable estimation by integrating statistical scaling methods with geospatial imputation techniques. Two main approaches are proposed: (1) Statistical Scaling, which includes hierarchical and non-hierarchical network approaches, and (2) Geospatial Imputation, based on variogram modeling. The hierarchical scaling method categorizes the network into several levels according to spatial and functional characteristics, applying tailored scaling factors to each category. In contrast, the non-hierarchical method uses a uniform scaling factor across all links, ignoring network heterogeneity. The variogram-based geospatial imputation leverages spatial correlations to estimate traffic variables for unequipped links, capturing spatial dependencies in urban road networks. Validation results indicate that the hierarchical scaling approach provides the most accurate estimates, achieving reliable performance even with as low as 5% uniform detector coverage. Although the variogram-based method yields strong results, it is slightly less effective than the hierarchical scaling approach but outperforms the non-hierarchical method.

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project.

Road traffic contributes significantly to atmospheric emissions in urban areas, a major issue in the fight against climate change. Therefore, joint monitoring of road traffic and related emissions is essential for urban public decision-making. And beyond this kind of procedure, public authorities need methods for evaluating transport policies according to environmental criteria.Coupling traffic models with traffic-related emission models is a suitable response to this need. However, integrating this solution into decision support tools requires a refined and dynamic char-acterization of urban mobility. Cell phone data, particularly Call Detail Records, are an interesting alternative to traditional data to estimate this mobility. They are rich, massive, and available worldwide. However, their use in literature for systematic traffic characterization has remained limited. It is due to low spatial resolution and temporal sampling rates sensitive to communication behaviors.This Ph.D. thesis investigates the estimation of traffic variables necessary for calculating air emis-sions (total distances traveled and average traffic speeds) from such data, despite their biases. The first significant contribution is to articulate methods of classification of individuals with two distinct approaches of mobility reconstruction. A second contribution is developing a method for estimating traffic speeds based on the fusion of large amounts of travel data. Finally, we present a complete methodological process of modeling and data processing. It relates the methods proposed in this thesis coherently.

Over the past few decades, the digitalization of services and infrastructures has led to the emergence of a broad set of new information sources to characterize human mobility. These sources usually offer valuable significant population penetration rates but may also suffer from important temporal sparsity. Data generated by user activity, such as social networks or mobile phone data, especially fit this description. Although this temporal sparsity might prevent estimating individual travel speeds, we state that such low-frequency positioning data enable estimating the average urban traffic speed dynamics when considering an adequate network partitioning. In this sense, this article proposes a new method, based on the division of the urban area of a given city into regions and on the analysis of a limited set of basic characteristics of individual vehicle trips, such as the regional path. Our solution first involves estimating robust travel times from travelers sharing similar trip features and then jointly analyzing these travel times to deduce the underlying regional traffic speeds, using regression analysis. We apply this methodology on a set of trips derived from a large GPS dataset of vehicle tracks covering the city of Lyon. These data are purposely downsampled to reduce the sampling rate and reproduce bias and temporal features that are proper to sparser but larger-scale, mobility data sources dependent on user’s communication activities. Controlling the data downsampling process allows us to evaluate the impacts of the progressive information loss on the speed estimation, while the raw GPS data provide the ground truth speed reference against which to compare our results. Provided that the amount of observed individual trips is sufficient, the analysis returns satisfying speed estimation results, both at low and high downsampling levels. Thus, we successfully demonstrate that it is possible to estimate zonal traffic speeds from degraded trip data without evaluating individual travel speeds.

One of the main challenges for multi-regional application of the aggregated traffic models based on the Macroscopic Fundamental Diagram, lies in the identification and characterization of the most prevailing paths chosen by drivers. In this paper, we propose a methodological framework, based on two distinct methods, to determine these prevailing paths. The first method requires the information about travel patterns in the urban network as well as the information about the city network partitioning. The second method is more parsimonious, and consists on the direct calculation of shortest-cost paths on the aggregated network. For this, we propose four impedance functions that utilize topological features of the urban network and its partitioning. We test the performance of this methodological framework for determining the most prevailing paths on a network representing the metropolitan area of Lyon (France). We consider a set of real trajectories (i.e. GPS data) of drivers in this network as a benchmark. We show that the proposed methods are able to identify the most prevailing paths as the ones chosen by drivers, as evidenced by a large similarity value between the sets of paths. Based on a maximum likelihood estimation, we also show that the Weibull distribution is the one that better reproduces the functional form of the network-wide distribution of travel distances. However, the characterization of the functional form of such distributions characteristic to each region defining a path is not trivial, and depends on the complex topological features of the urban network concerning the definition of its partitioning. We also show that the Euclidean distance metrics provides good estimates of the average travel distances. Interestingly, we also show that the most prevailing paths are not necessarily the ones that have the lowest average travel distances.