Abstract

EEEIC 2016 - 17th IEEE International Conference on Environment and Electrical Engineering , Florence, Italie, 06-/06/2016 - 08/06/2016; The estimation of running times is essential for the railway timetable definition. The trains travel duration must respect some constraints, which depend on the railway network. Usually, the running times minimize the trains travel duration on the railway network. They are typically calculated without considering the energy consumption. The green transportation has recently gained significant importance. In this paper, the energy consumption in the running times estimation is taken into account by defining an optimization problem to calculate the running times for the railway timetable definition. This optimization problem finds the running times which minimize the energy consumption. Precisely, an algorithm to handle the optimization problem with the integration platform for multiobjective and multi-disciplinary optimization modeFRONTIER is defined. The algorithm is tested on a case study and the results are validated with the OpenTrack Railway Simulation Tool.

Abstract

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; In recent years, intelligent transportation systems made it possible for operators to adapt in real-time the transportation supply to travel demand via new mobility services. Among these services, ride-sharing is becoming popular. The dynamic ride-sharing problem involves two sub-problems: (1) How to serve the upcoming trips (Optimal fleet management) and (2) How to accurately predict the travel times to determine vehicles availability and pick up/drop off times. In this paper, we express the optimal fleet management problem as a constrained multi objective integer linear programming. Our aim is to find the global optimal solution for the ride-sharing problem without uncertainty in demand to have a vision of these services performance in optimal situation. Then we compare the results and experiments with the exact optimal condition. We have designed an algorithm to find the exact solution for matching problem based on the branch and bound algorithm. To solve the second sub-problem, we define two different models to assess the impact of traffic conditions on the dynamic ride-sharing system performance for large-scale problems.