107,742 research outputs found
Finding Multiple New Optimal Locations in a Road Network
We study the problem of optimal location querying for location based services
in road networks, which aims to find locations for new servers or facilities.
The existing optimal solutions on this problem consider only the cases with one
new server. When two or more new servers are to be set up, the problem with
minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some
useful properties about the potential locations for the new servers, from which
we derive a novel algorithm for MinMax, and show that it is efficient when the
number of new servers is small. When the number of new servers is large, we
propose an efficient 3-approximate algorithm. We verify with experiments on
real road networks that our solutions are effective and attains significantly
better result quality compared to the existing greedy algorithms
An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System
With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time
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
Multi-concentric optimal charging cordon design
The performance of a road pricing scheme varies greatly by its actual design and implementation. The design
of the scheme is also normally constrained by several practicality requirements. One of the practicality
requirements which is tackled in this paper is the topology of the charging scheme. The cordon shape of the
pricing scheme is preferred due to its user-friendliness (i.e. the scheme can be understood easily). This has
been the design concept for several real world cases (e.g. the schemes in London, Singapore, and Norway).
The paper develops a methodology for defining an optimal location of a multi-concentric charging cordons
scheme using Genetic Algorithm (GA). The branch-tree structure is developed to represent a valid charging
cordon scheme which can be coded using two strings of node numbers and number of descend nodes. This
branch-tree structure for a single cordon is then extended to the case with multi-concentric charging cordons.
GA is then used to evolve the design of a multi-concentric charging cordons scheme encapsulated in the twostring
chromosome. The algorithm developed, called GA-AS, is then tested with the network of the Edinburgh
city in UK. The results suggest substantial improvements of the benefit from the optimised charging cordon
schemes as compared to the judgemental ones which illustrate the potential of this algorithm
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
Quantifying the benefits of vehicle pooling with shareability networks
Taxi services are a vital part of urban transportation, and a considerable
contributor to traffic congestion and air pollution causing substantial adverse
effects on human health. Sharing taxi trips is a possible way of reducing the
negative impact of taxi services on cities, but this comes at the expense of
passenger discomfort quantifiable in terms of a longer travel time. Due to
computational challenges, taxi sharing has traditionally been approached on
small scales, such as within airport perimeters, or with dynamical ad-hoc
heuristics. However, a mathematical framework for the systematic understanding
of the tradeoff between collective benefits of sharing and individual passenger
discomfort is lacking. Here we introduce the notion of shareability network
which allows us to model the collective benefits of sharing as a function of
passenger inconvenience, and to efficiently compute optimal sharing strategies
on massive datasets. We apply this framework to a dataset of millions of taxi
trips taken in New York City, showing that with increasing but still relatively
low passenger discomfort, cumulative trip length can be cut by 40% or more.
This benefit comes with reductions in service cost, emissions, and with split
fares, hinting towards a wide passenger acceptance of such a shared service.
Simulation of a realistic online system demonstrates the feasibility of a
shareable taxi service in New York City. Shareability as a function of trip
density saturates fast, suggesting effectiveness of the taxi sharing system
also in cities with much sparser taxi fleets or when willingness to share is
low.Comment: Main text: 6 pages, 3 figures, SI: 24 page
Optimal Time-dependent Sequenced Route Queries in Road Networks
In this paper we present an algorithm for optimal processing of
time-dependent sequenced route queries in road networks, i.e., given a road
network where the travel time over an edge is time-dependent and a given
ordered list of categories of interest, we find the fastest route between an
origin and destination that passes through a sequence of points of interest
belonging to each of the specified categories of interest. For instance,
considering a city road network at a given departure time, one can find the
fastest route between one's work and his/her home, passing through a bank, a
supermarket and a restaurant, in this order. The main contribution of our work
is the consideration of the time dependency of the network, a realistic
characteristic of urban road networks, which has not been considered previously
when addressing the optimal sequenced route query. Our approach uses the A*
search paradigm that is equipped with an admissible heuristic function, thus
guaranteed to yield the optimal solution, along with a pruning scheme for
further reducing the search space. In order to compare our proposal we extended
a previously proposed solution aimed at non-time dependent sequenced route
queries, enabling it to deal with the time-dependency. Our experiments using
real and synthetic data sets have shown our proposed solution to be up to two
orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM
SIGSPATIA
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