Giulio Erberto Cantarella

Heuristics for urban road network design: Lane layout and signal settings

This paper concerns the urban road network design problem. In urban areas supply has usually been unable to keep pace with increasing demand: the only possibility is often to reorganise the current supply configuration in order to use existing resources efficiently. Thus, in urban areas signal settings and network topology (in particular lane layout) are the two major factors that can be handled by design models. Methods for the combined design of signal settings and topology are proposed in this paper. All the methods proceed in two stages: the first deals with integer variables (topology), while the second deals with continuous variables (signal settings). Some metaheuristics (Hill Climbing, Simulated Annealing, Tabu Search, Genetic Algorithms and Path Relinking) are specified for the topology design stage, and they are used singularly or jointly. The continuous part of the solution, with fixed topology is optimized through an algorithm for asymmetrical deterministic equilibrium assignment. This paper focuses on evaluating performances obtained by all the different algorithms proposed for the topology design stage. The algorithms are compared by applications to real networks, and some conclusions are drawn about their efficiency.

Modelling sources of variation in transportation systems: theoretical foundations of day-to-day dynamic models

The last 20 years has seen a growing interest in models of transportation networks which explicitly represent the epoch-to-epoch adaptive behaviour of travellers, such as the day-to-day dynamics of drivers’ route choices. These models may represent the system as either a stochastic or deterministic process (DP). A body of theoretical literature now exists on this topic, and the purpose of the present paper is to both synthesise and advance this theory. To provide a focus to the work we analyse such models in terms of their ability to capture various contributory sources of variance in transportation systems. Dealing separately with the cases of uncongested and congested networks, we examine how moment-based deterministic dynamical systems may be exactly or approximately derived from some underlying stochastic process (SP). This opens up such problems to the tools of both deterministic dynamical systems (e.g. stability analysis) and SPs (e.g. Monte Carlo methods, statistical inference). In analysing these sources of variation, we also make several new advances to the existing body of theory, in terms of: extending the model assumptions (e.g. randomly varying choice probabilities and stochastic demand); deriving exact, explicit connections between stochastic and DPs in uncongested networks; applying stability analysis in novel ways to moment characterisations; and last, but not least, providing new limit theorems for asymptotic (large demand) analysis of the dynamics of SP models in congested networks.

The long term behaviour of day-to-day traffic assignment models

The dynamical behaviour of deterministic process, day-to-day traffic assignment models is sometimes characterised by convergence to a variety of different fixed equilibrium points dependent upon the initial flow pattern, even though individual trajectories are unique for a given start point. This non-uniqueness is seemingly in sharp contrast to the evolution of stochastic process, day-to-day models; under certain assumptions these converge in law to a unique stationary distribution, irrespective of the start point. In this article, we show how models may be constructed which exhibit characteristics of both deterministic models and stochastic models, and illustrate the ideas by using a simple example network.

CAPACITY FACTOR OR CYCLE TIME OPTIMIZATION FOR SIGNALIZED JUNCTIONS: A GRAPH THEORY APPROACH

In this paper a method for setting traffic signals of individual signalized junctions is presented. Capacity factor maximization and cycle time minimization are considered as objective functions. The correspondence between cycle time and capacity factor is discussed. The influence of minimum green and maximum red constraints is analyzed. Once this correspondence is known, an efficient problem-oriented algorithm, based on a PERT-like technique, is proposed for the solution of the problem.

Stochastic equilibrium assignment with variable demand: Theoretical and implementation issues

Recently, it has been pointed out that transport models should reflect all significant traveler choice behavior. In particular, trip generation, trip distribution, modal split as well as route choice should be modeled in a consistent process based on the equilibrium between transport supply and travel demand. In this paper a general fixed-point approach that allows dealing with multi-user stochastic equilibrium assignment with variable demand is presented. The main focus was on investigating the effectiveness of internal and external approaches and of different algorithmic specifications based on the method of successive averages within the internal approach. The vector demand function was assumed non-separable, non-symmetric cost functions were adopted and implementation issues, such updating step and convergence criterion, were investigated. In particular the aim was threefold: (i) compare the internal and the external approaches; (ii) investigate the effectiveness of different algorithmic specifications to solve the variable demand equilibrium assignment problem through the internal approach; (iii) investigate the incidence of the number of the links with non-separable and/or asymmetrical cost functions. The proposed analyses were carried out with respect to two real-scale urban networks regarding medium-size urban contexts in Italy.

Design of Signal Setting and Advanced Traveler Information Systems

This article analyzes the role of advanced traveler information systems (ATIS) in conjunction with signal setting (SS) design. ATIS and SS are considered as planning options for network optimization under recurrent traffic conditions. The traffic network is considered in within-day static, as is usual for planning-oriented models. Both day-to-day static (equilibrium) and dynamic states are investigated. Notably, equilibrium and stability can influence the feasibility of SS solutions. In addition, the role of ATIS is assessed with regard to its suitability in equilibrating SS optima and/or stabilizing SS equilibria. Travelers’ compliance with information, which plays a crucial role in both optimization and stabilization, is explicitly modeled. A formal modeling framework is introduced, allowing SS and ATIS to be represented, and several benefits and drawbacks of SS and ATIS options are explored. The model is then used for simulations on a hypothetical network. The results suggest the potential of the modeling framework and lead to nontrivial findings about the role of ATIS.

Stochastic equilibrium assignment with variable demand: literature review, comparisons and research needs

The aim of this paper is threefold: (1) propose a detailed literature review; (2) compare existing approaches; and (3) discuss the main research needs. The literature review is structured with respect to (a) solution approaches: optimization, variational inequalities and fixed-point (internal and external approaches), (b) choice dimensions taken into account, and (c) solution algorithms. The comparison is aimed at highlighting the transportation problems that each approach is able to address and to highlight the most effective approach within a specific problem and/or the most performing solution algorithm within the same approach. Finally, a sort of research “road-map” is drawn in order to emphasize research needs and research perspectives.

Modeling transportation mode choice through artificial neural networks

We aim at showing that artificial neural networks (ANN) can be an effective tool for travel demand analysis. Exiting literature show that ANNs can outperform commonly adopted models, derived from random utility theory, but they are based on hypothesis on user behavior, thus their parameters cannot clearly be interpreted. A new architecture, which one extra layer for perceived utility, will be analyzed to address those main drawbacks. This way an explicit utility function is introduced allowing to an interpretation of input variables as well as elasticity analysis

Stochastic Assignment to Transportation Networks: Models and Algorithms

Traffic assignment to transportation networks expresses the relation between origin-destination demand flows and link flows on a transportation network, and resulting performances, such as travel times, saturation degrees, etc. Traffic assignment models are one of the basic tools for the analysis and design of transportation systems. As such, they are currently used in many different contexts and are implemented in several software packages.

Approaches for solving the stochastic equilibrium assignment with variable demand: internal vs. external solution algorithms

This paper proposes and compares different approaches within the general fixed-point framework that allows to deal with multi-user (stochastic) equilibrium assignment with variable demand (VD). The aim was threefold: (i) compare the efficiency and the effectiveness of the internal and the external approaches to stochastic equilibrium assignment with VD; (ii) investigate the efficiency and the effectiveness of different algorithms based on the method of successive averages and its extensions; (iii) investigate the effects of different averaging schemes, different convergence criteria and different path choice models, such as Multinomial Logit model, C-Logit model and Multinomial Probit model. Analyses were carried out with respect to a real network and considering different indicators of both efficiency and effectiveness.