7,246 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning
An efficient urban bus control system has the potential to significantly
reduce travel delays and streamline the allocation of transportation resources,
thereby offering enhanced and user-friendly transit services to passengers.
However, bus operation efficiency can be impacted by bus bunching. This problem
is notably exacerbated when the bus system operates along a signalized corridor
with unpredictable travel demand. To mitigate this challenge, we introduce a
multi-strategy fusion approach for the longitudinal control of connected and
automated buses. The approach is driven by a physics-informed deep
reinforcement learning (DRL) algorithm and takes into account a variety of
traffic conditions along urban signalized corridors. Taking advantage of
connected and autonomous vehicle (CAV) technology, the proposed approach can
leverage real-time information regarding bus operating conditions and road
traffic environment. By integrating the aforementioned information into the
DRL-based bus control framework, our designed physics-informed DRL state fusion
approach and reward function efficiently embed prior physics and leverage the
merits of equilibrium and consensus concepts from control theory. This
integration enables the framework to learn and adapt multiple control
strategies to effectively manage complex traffic conditions and fluctuating
passenger demands. Three control variables, i.e., dwell time at stops, speed
between stations, and signal priority, are formulated to minimize travel
duration and ensure bus stability with the aim of avoiding bus bunching. We
present simulation results to validate the effectiveness of the proposed
approach, underlining its superior performance when subjected to sensitivity
analysis, specifically considering factors such as traffic volume, desired
speed, and traffic signal conditions
Transit Assignment Modeling Approaches based on Interval Uncertainty of Urban Public Transit Net Impedance
The data of the regular bus in Shenzhen during October 2019 was taken as an example. The improved model for the public transportation assignment was established based on considering the interval uncertainty theory and the basic algorithm of interval value, and the interval value acquisition method of bus impedance is established, the Method of Successive Averages ( MSA) algorithm is used to solve the problem. Finally, the error analysis of bus passenger flow assignment before and after the improvement of the model is carried out. It is found that the average absolute percentage error of the improved assignment model is 8.7% compared with the real value, while the average absolute percentage error is 10.9% when the impedance is invariant value, The result of passenger flow assignment under interval impedance is obviously better than that under certain impedance. On non-working days, when the bus passenger flow changes greatly, the bus passenger flow assignment result under interval impedance is better
Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm
[EN]The use of electric bikes (e-bikes) has grown in popularity, especially in large cities
where overcrowding and traffic congestion are common. This paper proposes an intelligent engine
management system for e-bikes which uses the information collected from sensors to optimize battery
energy and time. The intelligent engine management system consists of a built-in network of sensors
in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused
and on the basis of this information the system can provide the user with optimal and personalized
assistance. The user is given recommendations related to battery consumption, sensors, and other
parameters associated with the route travelled, such as duration, speed, or variation in altitude. To
provide a user with these recommendations, artificial neural networks are used to estimate speed and
consumption for each of the segments of a route. These estimates are incorporated into evolutionary
algorithms in order to make the optimizations. A comparative analysis of the results obtained has
been conducted for when routes were travelled with and without the optimization system. From
the experiments, it is evident that the use of an engine management system results in significant
energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to
user behavior and the characteristics of the route
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