Luca D’Acierno

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.

A stochastic traffic assignment algorithm based on ant colony optimisation

In this paper we propose a Stochastic User Equilibrium (SUE) algorithm that can be adopted as a model, known as a simulation model, that imitates the behaviour of transportation systems. Indeed, analyses of real dimension networks need simulation algorithms that allow network conditions and performances to be rapidly determined. Hence, we developed an MSA (Method of Successive Averages) algorithm based on the Ant Colony Optimisation paradigm that allows transportation systems to be simulated in less time but with the same accuracy as traditional MSA algorithms. Finally, by means of Blum’s theorem, we stated theoretically the convergence of the proposed ACO-based algorithm.

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.

The Use of Microsimulation Models for the Planning and Management of Metro Systems

The management of public transport for rebalancing the use of transportation systems is a useful tool for reducing negative externalities without excessively affecting zone accessibility. In this context, a rail or metro system can be a key element for producing a high-quality supply of public transport. Obviously, due to the great vulnerability of rail technology to system failures, it is necessary to develop suitable tools to identify rapidly, even with off-line procedures, the best operational strategies which minimise user discomfort produced by such failures. Hence, the authors proposal is to extend previous models proposed in the literature by considering travel demand as an outcome of a random variable and not only in terms of average values. The proposed approach is applied in the case of a real dimension metro network, considering a wider class of failure contexts.

A simulation-based approach for evaluating train operating costs under different signalling systems

In this paper, the authors propose a model framework for providing optimal driving strategies and related speed profiles which minimise the energy consumption of rail convoys. Previous models were extended and applied to evaluate the effects of different signalling systems upon rail operating costs. The proposed method was tested on a real rail network, the Cumana suburban railway (Italy).

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.

Analysis Of The Interaction Between TravelDemand 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.

Ant Colony Optimisation Approaches for the Transportation Assignment Problem

This paper will propose an ant colony optimization (ACO)-based algorithm that can be used to simulate mass-transit networks; this algorithm imitates the behavior of public transport users. In particular, the authors show that the proposed algorithm, which is an extension of that proposed by D’Acierno et al. (A stochastic traffic assignment algorithm based on ACO, Lecture Notes in Computer Science 4150, pp. 25–36, 2006), allows mass-transit systems to be simulated in less time but with the same accuracy compared with traditional assignment algorithms. Finally, the authors state theoretically the perfect equivalence in terms of hyperpath choice behavior between artificial ants (simulated with the proposed algorithm) and mass-transit users (simulated with traditional assignment algorithms).