Bruno Montella

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.

Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System

The aim of this paper is to develop an Information Extension Model (IEM) which uses location data of bus fleets (AVL data) to estimate road traffic conditions and provide input for implementing control strategies. The IEM consists of three sub-models: the Link Traffic Condition Model (LTCM), the AVL Adaptation Model (AVLAM) and the Network Traffic Condition Model (NTCM). The first provides road traffic conditions as a function of mass-transit traffic conditions in the case of shared lanes, the second provides mass-transit traffic conditions as a function of AVL data, and the last provides road traffic conditions over the whole road network as a function of mass-transit traffic conditions. The IEM (and its sub-models) were developed and calibrated in the case of real dimension networks and some tests were performed on a trial network. Numerical results show the effectiveness of the proposed method since it allows a reduction in travel demand estimation errors.

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.

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.

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).

An integrated approach for availability and QoS evaluation in railway systems

Prediction of service availability in railway systems requires an increasing attention by designers and operators in order to satisfy acceptable service quality levels offered to passengers. For this reason it is necessary to reach high availability standards, relying on high-dependable system components or identifying effective operational strategies addressed to mitigate failure effects. To this purpose, in this paper an innovative architecture is proposed to simulate railway operation in order to conduct different kinds of analysis. This architecture encompasses a set of components considering, in an integrated way, several system features. Finally an application to a first case study demonstrates the impact on quality of service and service availability of different recovery strategies. Complexity of a railway system requires a heterogeneous working group composed of experts in transport and in computer science areas, with the support of industry.