Author
Mariano Gallo
Bio: Mariano Gallo is an academic researcher from University of Sannio. The author has contributed to research in topics: Energy consumption & Public transport. The author has an hindex of 17, co-authored 82 publications receiving 1041 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors examined the transit network design problem under the assumption of elastic demand, focusing on the problem of designing the frequencies of a regional metro, and proposed four different objective functions that can be adopted to assume demand as elastic, considering the costs of all transportation systems (car, bus and rail) as well as the external costs.
Abstract: In this paper we examine the transit network design problem under the assumption of elastic demand, focusing on the problem of designing the frequencies of a regional metro. In this problem, investments in transit services have appreciable effects on modal split. Neglecting demand elasticity can lead to solutions that may not represent the actual objectives of the design. We propose four different objective functions that can be adopted to assume demand as elastic, considering the costs of all transportation systems (car, bus and rail) as well as the external costs, and we define the constraints of the problem. Heuristic and meta-heuristic solution algorithms are also proposed. The models and algorithms are tested on a small network and on a real-scale network.
117 citations
TL;DR: An assignment model on urban networks to simulate parking choices and the impact of cruising for parking upon road congestion is proposed, where each layer simulates a trip phase (on-car trip between the origin and destination zone and walking egress trip).
109 citations
TL;DR: For each approach a different optimization model and some solution algorithms are proposed; both models and algorithms are based on the assumptions of within-day static system and stochastic user equilibrium assignment models.
Abstract: In this paper models and algorithms for the optimization of signal settings on urban networks are proposed. Two different approaches to the solution of the problem may be identified: a global approach (optimization of intersection signal settings on the whole network) and a local approach (optimization of signal settings intersection by intersection). For each approach a different optimization model and some solution algorithms are proposed; both models and algorithms are based on the assumptions of within-day static system and stochastic user equilibrium assignment models. The paper includes numerical results on test networks and a comparison between the two approaches.
98 citations
TL;DR: This paper proposes an optimisation model and a meta-heuristic algorithm for solving the urban network design problem and initial results show that the proposed approach allows local optimal solutions to be obtained in reasonable computation times.
78 citations
TL;DR: Parking pricing strategies are important tools for rebalancing the modal split between private car and transit systems in urban areas as discussed by the authors, since they can be managed without adopting advanced technologies.
78 citations
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TL;DR: A comprehensive review on bilevel optimization from the basic principles to solution strategies is provided in this paper, where a number of potential application problems are also discussed and an automated text-analysis of an extended list of papers has been performed.
Abstract: Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.
588 citations
TL;DR: In this paper, the authors used factorial analysis to find independent measures to describe the dimensions of urban driving patterns and investigate which properties have main effect on emissions and fuel-use, and found that nine of the driving pattern factors had considerable environmental effects.
Abstract: This study is aimed at finding independent measures to describe the dimensions of urban driving patterns and to investigate which properties have main effect on emissions and fuel-use. 62 driving pattern parameters were calculated for each of 19 230 driving patterns collected in real traffic. These included traditional driving pattern parameters of speed and acceleration and new parameters of engine speed and gear-changing behaviour. By using factorial analysis the initial 62 parameters were reduced to 16 independent driving pattern factors. Fuel-use and emission factors were estimated for a subset of 5217 cases using two different mechanistic instantaneous emission models. Regression analysis on the relation between driving pattern factors and fuel-use and emission factors showed that nine of the driving pattern factors had considerable environmental effects. Four of these are associated with different aspects of power demand and acceleration, three describe aspects of gear-changing behaviour and two factors describe the effect of certain speed intervals.
580 citations
TL;DR: This review intends to provide a bigger picture of transportation network design problems, allow comparisons of formulation approaches and solution methods of different problems in various classes of UTNDP, and encourage cross-fertilization between the RNDP and PTNDP research.
573 citations
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TL;DR: An automated text-analysis of an extended list of papers published on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary is performed.
Abstract: Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.
268 citations
TL;DR: Compared with uncontrolled parking processes or state-of-the-art guidance-based systems, this system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized.
Abstract: We propose a novel “smart parking” system for an urban environment. The system assigns and reserves an optimal parking space based on the driver's cost function that combines proximity to destination and parking cost. Our approach solves a mixed-integer linear programming (MILP) problem at each decision point defined in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a cost function higher than this driver's current cost function value. Based on simulation results, compared with uncontrolled parking processes or state-of-the-art guidance-based systems, our system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized. We also describe full implementation in a garage to test this system, where a new light system scheme is proposed to guarantee user reservations.
262 citations