Roberta Di Pace

Signal Setting Design at a Single Junction Through the Application of Genetic Algorithms

The purpose of this chapter is the application of Genetic Algorithms to solve the Signal Setting Design at a single junction. Two methods are compared: the monocriteria and the multicriteria optimisations. In the former case, three different objectives functions were considered: the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation; in the latter case, two combinations of criteria were investigated: the total delay minimisation and the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation. Furthermore, two multicriteria genetic algorithms were compared: the Goldberg’s Pareto Ranking (GPR) and the Non Dominated Sorting Genetic Algorithms (NSGA-II). Conclusions discuss the effectiveness of multicriteria optimisation with respect to monocriteria optimisation, and the effectiveness of NSGA-II with respect to the GPR.

Comparing paradigms for strategy learning of route choice with traffic information under uncertainty

Travellers route choice dynamics with endogenous reliability of ATIS are modelled.Joint strategy fictitious play model best describes route choice learning process.In case of low accuracy of information, choice behaviour tends towards randomness. This paper aims to model the travellers day-to-day route choice in the case of an Advanced Traveller Information System (ATIS) through two learning paradigms: reinforcement-based and belief-based. The reinforcement learning approach is adopted in both a basic version and an extended one. Similarly, the belief-learning approach is adopted in both a Joint Strategy Fictitious Play version and in a Bayesian-learning one. All the models are compared and validated based on data collected by means of a stated preference experiment. The models explicitly account for the reliability of the information system, as this interacts with the inherent dispersion of network travel times and determines the overall level of uncertainty affecting the travellers adaptive learning behaviour. The experiment is then designed to simulate different levels of reliability for the ATIS. Results show that for intermediate and high levels of information accuracy, joint strategy fictitious play best predicts the respondents route choice behaviour under information provision, suggesting that a best-reply strategy is used by travellers for their route choices. In low information accuracy, the result suggests the payoff variability moves the choice behaviour toward randomness. The proposed approach provides useful tools to model travellers adaptive route choice behaviour and contributes to the support of effective ATIS design.

Application of Data Fusion for Route Choice Modelling by Route Choice Driving Simulator

Modelling route choices is one of the most significant tasks in transportation models. Route choice models under Advanced Traveller Information Systems (ATIS) are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. Different types of SP approaches can be adopted, alternatively based on Travel Simulators (TSs) or Driving Simulators (DSs). Here a pilot study is presented, aimed at setting up an SP-tool based on driving simulator developed at the Technical University of Bari. The obtained results are analysed in order to check the accordance with expectations in particular the results of application of data fusion technique are shown in order to explain how data collected by DSs, can be used to reduce the effect of choice of behaviour in unrealistic scenarios in TSs.

Simulating Container Terminal Performances: Microscopic vs. Macroscopic Modelling Approaches

Most of the existing contributions for container terminal analysis follow approaches based on optimization models, that are mainly useful to support strategic decisions about terminal container configuration. Many others contributions follow the simulation approach, which allows a detailed analysis but may lead to computational problems and is rather computer demanding, especially when resulting models are used to support optimization. This paper investigated the prediction reliability of two different approaches to container terminal simulation: microscopic and macroscopic. The former simulating single container movement, the latter simulating container flows movement. The microscopic model was a discrete event simulation model, the macroscopic model was a dynamic discrete time based (space-time) network assignment model. Both modelling approaches were implemented and compared taking advantage of some significant investment made by the Salerno Container Terminal (Italy) between 2005 and 2011. In particular, disaggregate (microscopic) and an aggregate (macroscopic) simulation models implemented in 2005 were validated with a large set of data acquired in 2011 after some structural and functional terminal modifications. Through this analysis it was possible to analyze the prediction reliability of both simulation approaches and to draw some operational guidelines.

Matching macro- and micro-scopic approaches for the evaluation of traffic management impacts

The paper focuses on the evaluation of the combined effect of Traffic Signal Control Strategy (TSC) and Variable Message Sign (VMS). With reference to the TSC a dynamic selection strategy based on macroscopic flow variables was considered for off-line traffic signal plans design. The combination of two ITS solutions, TSC and VMS, was tested through microscopic approach by SUMO traffic simulator which allows to directly reproduce the pollutant emissions and fuel consumptions.

Traditional random utility models vs hybrid choice models for assessing environmental impacts of a new technology: The HySolaKit case study

The aim of the present paper is to investigate if behavioural models which are more accurate and effective (latent variables hybrid choice models) in simulating new automotive technology adoption, may significantly affect market forecasts and environmental impacts estimation. Since the time and the cost to develop each modelling solution are significantly different and require different types of surveys, the main goal of the paper is to give some insights into the costs and the benefits of each solution. Moreover, different models are compared in terms of sensitivity to different market scenarios and in terms of estimated environmental impacts on a real case study (the city of Salerno — southern Italy).

Transportation systems with connected and non-connected vehicles: Optimal traffic control

The paper aims to develop a consistent framework for traffic management allowing for the joint optimization of connected vehicle paths and departure times and of signal control. The procedure is based on the communication between connected vehicles and signal controller (Vehicle to Infrastructure communications). Thus, the optimization procedure is characterized by two steps: the first refers to the adaptive traffic signal optimization, whilst the second refers to the optimization of routes and departure times of connected vehicles. In particular, as regards the adaptive traffic signal control, stage durations and sequences, as well as the node offsets, are considered as decision variables optimized with a scheduled synchronization method based on a meta-heuristic algorithm. On the contrary, the optimization of connected vehicle paths and departure times were considered as decision variables through a Mixed Integer Mathematical Program. Finally, as regards the traffic flow model, a further development of the Cell Transmission Model was considered. The whole framework was tested on a toy network.