Andrea Gemma

An Adaptive Bi-Level Gradient Procedure for the Estimation of Dynamic Traffic Demand

This paper presents an in-depth analysis of the bi-level gradient approximation approach for dynamic traffic demand adjustment and the development of new adaptive approaches. Initially, a comparison between the simultaneous perturbation stochastic approximation (SPSA), asymmetric design (AD), polynomial interpolation (PI) method, which was first proposed by authors in 2010-2011, and its second-order development is presented; then, a sensitivity analysis of the parameters of the SPSA AD-PI is reported; finally, some new advances of the estimation method based on an adaptive approach are proposed and evaluated on a real test network.

A bi-level gradient approximation method for dynamic traffic demand estimation: Sensitivity analysis and adaptive approach

The paper presents an in-depth analysis of the bi-level gradient approximation approach for dynamic traffic demand estimation. Initially, a sensitivity analysis of the parameters of Simultaneous Perturbation Stochastic Approximation method (SPSA) with Asymmetric Design (AD) and Polynomial Interpolation (PI), firstly proposed by authors in 2011, is presented. Then, an adaptive method based on the second order SPSA AD-PI approach, is explored; finally, some new advances of the estimation method are proposed in order to keep under control traffic phenomena during the estimation.

Dynamic O-D Demand Estimation: Application of SPSA AD-PI Method in Conjunction with Different Assignment Strategies

This paper examines the impact of applying dynamic traffic assignment (DTA) and quasi-dynamic traffic assignment (QDTA) models, which apply different route choice approaches (shortest paths based on current travel times, User Equilibrium: UE, and system optimum: SO), on the accuracy of the solution of the offline dynamic demand estimation problem. The evaluation scheme is based on the adoption of a bilevel approach, where the upper level consists of the adjustment of a starting demand using traffic measures and the lower level of the solution of the traffic network assignment problem. The SPSA AD-PI (Simultaneous Perturbation Stochastic Approximation Asymmetric Design Polynomial Interpolation) is adopted as a solution algorithm. A comparative analysis is conducted on a test network and the results highlight the importance of route choice model and information for the stability and the quality of the offline dynamic demand estimations.

A Road Network Design Model for Large-Scale Urban Network

The aim of this work is to propose and test a methodology to support the design of urban road transport systems, highlighting the sustainability of the proposed measures. Finally, a tool for public administration support is provided. The problem is formulated as a road network design problem (NDP) with fixed demand, with design variables representing street direction and lane addition on links of the road network; the proposed methodology is based on two main phases: (1) a first phase aiming at reducing the solution’s search space; (2) a second phase concerning the optimization procedure. The latter consists in a heuristic method based on a genetic algorithm. The procedure has been initially tested on a sub-network of the city of Rome (Eur network), and subsequently applied to the city of Brindisi (Southern Italy).