EETTlib—Energy‐efficient train timetabling library

Abstract

We introduce EETTlib, an instance library for the Energy‐Efficient Train Timetabling problem. The task in this problem is to adjust a given timetable draft such that the energy consumption of the resulting railway traffic is minimized. To this end, the departure times of the trains can be slightly, and their velocity profiles on each trip can be modified. We provide real‐world data originating from two research projects in this field, one with Deutsche Bahn AG, the most important railway company in Germany, the other with VAG Verkehrs‐Aktiengesellschaft, the operator of public transport in the city of Nürnberg, Germany. In both cases, our library contains representative data on the relevant operational constraints and supports various possible choices for the objective function with respect to energy‐efficiency. The resulting benchmark instances can be used by the scheduling and timetabling community to improve their models and algorithms. They are available under https://www.eettlib.fau.de.

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

“…To optimize the tram's operation, a "quickest curve" has been identified as the fastest possible operating curve that minimizes travel time [22]. Previous studies have developed an ideal tram control curve by combining maximum traction, coast, cruise, and maximum braking, but the running social vehicles conditions within intersections must also be taken into account [23]. As a result, the traction and braking forces do not have to be at maximum levels, and the trams' running duration in the section can be flexibly adjusted by modifying these forces.…”

“…In this research, the trams' traction acceleration, braking acceleration, coast acceleration, and basic resistance are computed by examining their traction, coast, and braking states. The simulation employs a traditional discrete technique, dividing the entire running process into numerous segments of uniformly accelerated rectilinear motion having a step length set at 0.1 s. At each time step, the initial speed, distance, and acceleration are considered to calculate the tram's discrete speed and distance, given the tram's fully loaded mass and coefficients for maximum traction and braking forces, rolling resistance, and air resistance [21,23]. This approach enables a more accurate assessment of the trams' performance in complex variable acceleration motions, allowing for the identification of optimal operating conditions for the transit system.…”