Mariano Gallo

Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment models

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.

A meta-heuristic approach for solving the Urban Network Design Problem

This paper proposes an optimisation model and a meta-heuristic algorithm for solving the urban network design problem. The problem consists in optimising the layout of an urban road network by designing directions of existing roads and signal settings at intersections. A non-linear constrained optimisation model for solving this problem is formulated, adopting a bi-level approach in order to reduce the complexity of solution methods and the computation times. A Scatter Search algorithm based on a random descent method is proposed and tested on a real dimension network. Initial results show that the proposed approach allows local optimal solutions to be obtained in reasonable computation times.

An Ant Colony Optimisation algorithm for solving the asymmetric traffic assignment problem

In this paper we propose an Ant Colony Optimisation (ACO) algorithm for defining the signal settings on urban networks following a local approach. This consists in optimising the signal settings of each intersection of an urban network as a function only of traffic flows at the accesses to the same intersection, taking account of the effects of signal settings on costs and on user route choices. This problem, also known as Local Optimisation of Signal Settings (LOSS), has been widely studied in the literature and can be formulated as an asymmetric assignment problem. The proposed ACO algorithm is based on two kinds of behaviour of artificial ants which allow the LOSS problem to be solved: traditional behaviour based on the response to pheromones for simulating user route choice, and innovative behaviour based on the pressure of an ant stream for solving the signal setting definition problem. Our results on real-scale networks show that the proposed approach allows the solution to be obtained in less time but with the same accuracy as in traditional MSA (Method of Successive Averages) approaches.

Analysis of the interaction between travel demand and rail capacity constraints

In urban contexts, the adoption of policies to promote the use of public transport systems represents a useful tool for decision-makers to reduce the environmental impact of private car use. Especially in high-density contexts most travel demand can be satisfied efficiently by means of high-quality rail systems. However, in the event of breakdowns, since faulty trains cannot usually be overtaken and their removal could pose extreme difficulties especially in metropolitan systems with two separate tunnels, re-establishing the regular service could involve inconveniently long travel times. Hence, emergency management has to take into account effects on travel demand. In this framework, we analyse such effects for different levels of degraded services in order to define the best strategy to adopt to minimise user discomfort. We extend ideas proposed elsewhere in the literature by introducing capacity constraints of rail vehicles in order to provide more realistic simulated effects. Finally, we describe the application of the proposed approach in the case of the Naples metro system.

Estimating the benefits of energy-efficient train driving strategies: a model calibration with real data

This paper describes the first results of a research project where the main focus is to implement a Decision Support System (DSS) to optimise energy consumption of rail systems. In order to achieve this objective, the authors implement an optimisation module for the design of energy-efficient driving strategies, in terms of speed profiles, that requires a railway simulation model as a subroutine. Here the authors focus on the general framework of the optimisation module and on the calibration of the railway simulation model. All elaborations are implemented in a MatLab environment, aiming at defining possible energy-efficient speed profiles, in accordance with energy-saving strategies, through optimised speed profile parameters, in terms of acceleration, target speed, deceleration, coasting phase, and driving behaviour, represented by the jerk. The model is calibrated on real data recorded on a double track section of a railway line in the city of Naples (Italy). Initial results show that consumption is very variable with the speed profile and with driver behaviour, but the model is able to reproduce the average consumption of each driving strategy and should be able, within the DSS, to suggest the best driving strategies for each rail section.

Multimodal network design problems

In this paper we study the Multimodal Network Design Problem (MNDP); this problem arises when a decision maker (e.g. traffic authority) can operate on different transportation modes simultaneously. For this problem we formulate a general multimodal network design model from which descend multimodal and monomodal network design submodels. Possible solution approaches and research perspectives are identified. This paper, therefore, provides a general framework on multimodal and monomodal network design problems and their modeling.

A Multimodal Approach to Bus Frequency Design

This paper proposes a model for optimizing bus frequencies under the assumption of elastic demand considering explicitly the effects of changes in transit supply on modal split. Neglecting demand elasticity may lead to solutions that may not represent actual design objectives. Using an objective function that is a weighted sum of user costs on all transportation systems (car and bus), operation costs and external costs, the paper proposes a heuristic solution algorithm that is able to solve the problem in acceptable computing times and for real-scale problems. The model and the algorithm are tested on a large urban multimodal network.

Replanning public transport services in the case of budget reductions

The planning of an efficient and effective public transport system is a key element in managing modern mobility both in densely populated urban areas and in peripheral and/or rural areas where the population densities are considerably lower. Indeed, the presence of public transport designed to meet travel demand could allow a reduction in negative externalities produced by private cars without excessively penalising user travel times or accessibility to different zones. However, the recent financial crisis has forced many public administrations to reduce resources allocated to public services and replan related services in order to mitigate negative effects on users. In this context, the authors propose two methods for replanning public transport services in the case of budget reductions. The first approach, indicated as CLP (Change the Least Possible), can be adopted when the initial services are actually able to satisfy user needs and in some time slots are probably surplus to requirements. The second approach, CFR (Change the FRamework), instead, should be used when the initial services are already inadequate or barely sufficient to serve users in the study area. Indeed, in the latter case, it is very difficult to eliminate some runs without producing a significant reduction in levels of service. Finally, the proposed methodology has been applied by our research group when replanning bus services in the provinces of Naples and Avellino, in southern Italy, where we implemented respectively the CLP and CFR approaches by solving some problems related to interference with pre-existing planning tools.

An artificial neural network approach for spatially extending road traffic monitoring measures

This paper focuses on road traffic monitoring and proposes a method based on artificial neural networks for extending data collected on some road links to others. The method may be used to reduce the costs of monitoring equipment since it can estimate the data to be monitored on road segments where there is no equipment installed. The approach is tested on a small network, assuming different neural network frameworks. The numerical results show that the approach is promising, being able in most cases to estimate traffic flows with acceptable errors.

Towards a Simulation-based Framework for Evaluating Energy-efficient Solutions in Train Operation

In this paper the authors propose a simulation-based framework for evaluating energy efficient solutions in train operation. The general framework is composed of an optimisation system able to generate energy-efficient station-to-station speed profiles, looped with a micro-simulation tool for simulating railway traffic conditions, in order to evaluate the impacts on railway systems (delays, conflicts) and energy savings. The optimisation system is a subroutine consisting of a Genetic Algorithm for optimal speed profile parameters optimisation, a speed profile generator, and an energy consumption model. The micro-simulation tool allows the evaluation of the impact of energy efficient speed profiles on rail operation. The framework operates on a database composed of 4 subsets: timetable, rolling stock characteristics, signalling system, infrastructure features; the first subset can be considered as the result of scheduling or rescheduling procedures, while the others can be assumed to be fixed. The proposed framework has been applied on a real-scale case of an Italian suburban railway system.Volume 1: common research on transport computer applications in Torontos rapid transit expansion program computer-based planning techniques and the appraisal of an underground railway extension integration of power feeding and train dispatching subsystems to increase railway service capability validation of software for railway applications modelling and simulation of electric railway traction, track signalling and power systems. Volume 2: improvement in railway safety and train density by using continuous train control system high-speed, high-density train allocation timetable data communication to signal control systems the problems of assessing the safety of ATP systems which have been developed under different national standards a new train overspeed protection system based on multi-microprocessors and distributed configuration integrated railway traffic management and control a microprocessor implementation of a stochastic anti-skidding device oriented to electrical traction drives 3D analysis of losses in the shields of SC coils in EDS-MAGLEV transport systems train control method for high speed and high density railway systems.