4 research outputs found
Infrastructure Modeling and Optimization to Solve Real-time Railway Traffic Management Problems
Rail transportation helps to reach the global climate targets because it is characterized by low emission. The passenger and freight volumes on the railway increase yearly in line with EU targets. However, delays of passenger and freight trains decrease the punctuality and the reliability of the railway sector and the development of the infrastructure is not enough to increase the average speed of trains. Delays mean cost to the passengers, railway operators, infrastructure managers, and all railway undertakers. Therefore, the reason for the most significant optimization target is to minimize delays. In this paper, a possible solution has been described to solve the real-time railway traffic management problems by applying a mixed-integer linear programming approach. For validation of the research result, one simplified case study has been presented. Based on the result, the presented solution can provide effective support to dispatchers in solving real-time traffic management problems
Runtime Performance Analysis of a MILP-Based Real-Time Railway Traffic Management Algorithm
The real-time railway traffic management problem occurs when the trains get off schedule due to different traffic perturbations. In this case, they must be rerouted, reordered, and rescheduled to resolve the possible conflicts. Nowadays, this problem is usually handled by human dispatchers. There are lots of algorithms aiming to support human dispatchers in making an optimal decision that minimizes delays. However, due to the real-time nature of the problem, the response time of these algorithms is crucial. In this paper, the runtime performance of a state-of-the-art mixed-integer linear programming model is analyzed in different solvers. The analysis is performed via Monte Carlo simulation, generating various realistic scenarios in an infrastructure model of a Hungarian railway control area
GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System
Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception
system. The requirements of a frontal environment perception
system cannot be satisfied by either of the existing automotive
sensors. A commonly used sensor cluster for these functions
consists of a mono-vision smart camera and automotive radar.
The sensor fusion is intended to combine the data of these
sensors to perform a robust environment perception. Multi-object
tracking algorithms have a suitable software architecture for
sensor data fusion. Several multi-object tracking algorithms,
such as JPDAF or MHT, have good tracking performance;
however, the computational requirements of these algorithms
are significant according to their combinatorial complexity. The
GM-PHD filter is a straightforward algorithm with favorable
runtime characteristics that can track an unknown and timevarying number of objects. However, the conventional GM-PHD\ud
filter has a poor performance in object cardinality estimation.
This paper proposes a method that extends the GM-PHD filter
with an object birth model that relies on the sensor detections and
a robust object extraction module, including Bayesian estimation
of objects’ existence probability to compensate for drawbacks of
the conventional algorithm