This study delves into the determinants of commuting and residential migration decisions among French-born workers exposed to Luxembourg's economy. We rely on micro-data from two different sources to study two choices of the population: that of residing in France or Luxembourg, and that of working in France or Luxembourg. The study investigates the interactions of house prices and wages with education levels, shedding light on the complex dynamics driving cross-border labor supply. Our research explores the implications of these decisions on the size and selection of individuals on both sides of the borders. As each micro-dataset only allows us to observe partial segments of the population, we propose a method to derive joint decisions, based on the combination of estimation results from the conditional datasets. Initial results reveal significant influences of wage differentials and housing market disparities on migration patterns, that vary by skill group. Further analyses explore counterfactual scenarios to understand how changes in economic conditions impact the actual selection and distribution of cross-border workers and foreign residents in Luxembourg.

We introduce the Breakpoint Exact Algorithm with Capacities (BEAC), based on the state-of-the-art Breakpoint Exact Algorithm (BEA) to address the choice-based pricing problem (CPP) with capacity constraints, together with the Breakpoint Heuristic Algorithm (BHA) for both uncapacitated and capacitated instances. We furthermore develop valid inequalities for the MILP formulation of the CPP, allowing us to use the heuristic solution to speed up the exact Branch \& Benders Decomposition (B\&BD) approach. When including capacity, an approach based on an exogenous priority queue, as well as a supplier-controlled queuing strategy to generate maximal or minimal profit for robust optimization is developed. The BHA leverages a coordinate descent method, which produces high-quality solutions in a short time. Results show that when optimizing two prices simultaneously, in the capacitated case, the BEAC reports runtimes up to 20 times faster than the state-of-the-art mixed-integer linear programming (MILP) approach, while the BHA performs from 100 to 5000 times faster than the MILP. For the uncapacitated case, the BHA outspeeds the BEA as well as the B\&BD approach by multiple orders of magnitude, especially for high-dimensional instances. Preliminary results show significant improvements in computational time for the exact method (B\&BD) when using the heuristic solution to guide the algorithm.

In this paper, we extend the hybrid simulator from the individual to the household level by including a broader set of simulated demographic events affecting households and redefining a resampling procedure using the Gibbs Sampler. Usually, projection methods use historical demographic rates that may not account for sudden events like COVID-19, potentially hindering the accuracy of transportation models that rely on these projections. To test the resilience of projection methods to unforeseen events, we project synthetic samples from 2010 to 2021 using dynamic projection and a hybrid simulator. We test two scenarios based on pre-pandemic and post-pandemic demographic rates using Swiss Mobility and Transport Microcensus data. The results show that the hybrid simulator is more robust and less dependent on rates when it comes to unforeseen events than dynamic projection as it includes an intermediate resampling update that helps reduce the errors of dynamic projection.

TBA

This paper investigates the ride-acceptance behavior of drivers on ride-sourcing platforms, considering drivers’ freedom to accept or reject ride requests. Understanding drivers’ preferences is vital for ride-sourcing services to improve the matching of requests to drivers. To this end, we obtained a unique dataset from a reputable ride-sourcing platform in Iran. This dataset provides comprehensive details of driver and ride characteristics for both successful and unsuccessful matchings. We investigate the ride-acceptance behavior of drivers using a hierarchical drift-diffusion model, which captures the dependency between drivers’ choices and response times. This dependency implies that response time carries information about drivers’ preferences which allows us to better comprehend drivers’ ride-acceptance behaviors. Furthermore, we conduct a thorough comparison between the drift-diffusion model and the logit model, considering their predictive ability, parameter estimates, and elasticities. Within the drift-diffusion model framework, we also derive time-dependent elasticities of acceptance probability and elasticity of drivers’ response times. Our results demonstrate that ride fare, ride duration to request origin, and rainfall volume have the most impact on drivers’ ride-acceptance decisions. The insights derived from this study can be utilized to enhance platform matching algorithms and strategies, thereby improving the efficiency of ride-sourcing platforms.

Assortment optimization problems are revenue-maximizing problems, which involve the selection of a subset of products to be offered to customers. We present the assortment optimization problem under the mixed logit model demand (AOP-MXL). To reduce the computational effort in the sample average approximation process due to large samples, we discuss multiple variance reduction techniques for this problem. We derive theoretical bounds of the predictive errors based on variance reduction methods, the random parameters distribution, and the number of draws (realizations). We conduct numerical experiments to investigate the impact of the mentioned factors on the computational effort to solve AOP-MXL.

A large literature has documented the incentive e ect of emigration prospects in terms of human capital accumulation in origin countries. Much less attention has been paid to the impact on speci c educational choices. We provide some evidence from the behaviour of students of the University of Lorraine located in the North-East of France and close to Luxembourg, a booming economy with attractive work conditions. We  nd that students who paid attention to the foreign labour market at the time of enrolment tend to choose topics that lead to occupations that are highly valued in Luxembourg. These results hold when accounting for heterogeneous substitution patterns across study  elds through the estimation of advanced discrete choice models. Incentive e ects of emigra- tion prospects are also found when accounting for the potential endogeneity of the interest for the foreign labour market using a control function approach based on the initial locations of these students at the time of enrolment. Con- sistently, students showing no attention to the foreign labour market are not subject to the incentive e ect of emigration prospects.

Enrolment decisions at the university level are influenced by many factors, such as the number of credits, the expected difficulty and the schedule. Scheduling decisions made by universities typically take into account the availabilities (and preferences) of teachers, but disregard student preferences. Research has shown the impact of university course schedules in student absence, which in turn can negatively impact academic performance. Thus, student preferences should be taken into account to support enrolment decisions. To this end, we start this research by conducting an exploratory data analysis on historical data to assess the extent of such impact. This historical data contains information on the course offer of the last six academic years as well as information on the courses enrolled by students during those years.