7,074 research outputs found
Mixed network design using hybrid scatter search
This research proposes a bi-level model for the mixed network design problem (MNDP). The upper level problem involves redesigning the current road links’ directions, expanding their capacity, and determining signal settings at intersections to optimize the reserve capacity of the whole system. The lower level problem is the user equilibrium traffic assignment problem. By proving that the optimal arc flow solution of the bi-level problem must exist in the boundary of capacity constraints, an exact line search method called golden section search is embedded in a scatter search method for solving this complicated MNDP. The algorithm is then applied to some real cases and finally, some conclusions are drawn on the model's efficiency.postprin
Minimization Problems in Signalized Road Networks
In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth's network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization. C1 [Baskan, Ozgur; Ceylan, Huseyin] Pamukkale Univ, Dept Civil Engn, Fac Engn, TR-20160 Denizli, Turkey. [Ozan, Cenk] Adnan Menderes Univ, Dept Civil Engn, Fac Engn, TR-09100 Aydin, Turkey.
Document type: Articl
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
Optimizing capacity of signalized road network with reversible lanes
This paper studies the network capacity problem on signalized road network with reversible lanes. A Mixed Network Design Problem (MDNP) is formulated to describe the problem where the upper-level problem is a mixed integer non-linear program designed to maximize the network capacity by optimizing the input parameters (e.g. the signal splits, circles, reassigned number of lanes and O–D demands), while the lower-level problem is the common Deterministic User Equilibrium (DUE) assignment problem formulated to model the drivers’ route choices. According to whether one way strategy is permitted in practice, two strategies for implementing reversible roadway are considered. In the first strategy, not all lanes are reversible and the reversible roadways always hold its ability to accommodate the two-way traffic flow. In the second strategy, one-way road is allowed, which means that all the lanes are reversible and could be assigned to one flow direction if the traffic flow in both directions is severally unsymmetrical. Genetic Algorithm (GA) is detailedly presented to solve the bi-level network capacity problem. The application of the proposed method on a numerical example denotes that Strategy 2 can make more use of the physical capacity of key links (signal controlled links), thus, the corresponding network capacity outperforms it is of Strategy 1 considerably.
First published online 14 January 201
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
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