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

The Merits of Sharing a Ride

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads

Time-dependent stochastic shortest path(s) algorithms for a scheduled transportation network

Following on from our work concerning travellers’ preferences in public transportation networks (Wu and Hartley, 2004), we introduce the concept of stochasticity to our algorithms. Stochasticity greatly increases the complexity of the route finding problem, so greater algorithmic efficiency becomes imperative. Public transportation networks (buses, trains) have two important features: edges can only be traversed at certain points in time and the weights of these edges change in a day and have an uncertainty associated with them. These features determine that a public transportation network is a stochastic and time-dependent network. Finding multiple shortest paths in a both stochastic and time-dependent network is currently regarded as the most difficult task in the route finding problems (Loui, 1983). This paper discusses the use of k-shortest-paths (KSP) algorithms to find optimal route(s) through a network in which the edge weights are defined by probability distributions. A comprehensive review of shortest path(s) algorithms with probabilistic graphs was conducted

An Agent-based Route Choice Model

Travel demand emerges from individual decisions. These decisions, depending on individual objectives, preferences, experiences and spatial knowledge about travel, are both heterogeneous and evolutionary. Research emerging from fields such as road pricing and ATIS requires travel demand models that are able to consider travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) in a differentiated market (by tolls and the level of service). Traditional trip-based models have difficulty in dealing with the aforementioned heterogeneity and issues such as equity. Moreover, the role of spatial information, which has significant influence on decision-making and travel behavior, has not been fully addressed in existing models. To bridge the gap, this paper proposes to explicitly model the formation and spread- ing of spatial knowledge among travelers. An Agent-based Route Choice (ARC) model was developed to track choices of each decision-maker on a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied on both SiouxFalls network and Chicago sketch network. Comparison between ARC and existing models (UE and SUE) on both networks shows ARC is valid and computationally tractable. To be brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of travel demand under an integrated framework.Agent-based model, route choice, traffic assignment, travel demand modeling