Livia Mannini

Traffic state estimation based on data fusion techniques

The capability to detect and/or forecast traffic conditions is of utmost importance in road management applications. Recent advances in technology have made available numerous new monitoring systems exploiting larger fleet of probe vehicles. Together with traditional volume and time mean speed measurements relative to a local section monitored continuously in time, probe vehicles provide additional type of data, such as space mean speed and travel time, relative to road segments monitored in specific time intervals. Therefore, the aim of this paper is to study how to exploit available information detected by new monitoring devices in the estimation of traffic flow conditions. Different types of data fusion techniques have been analyzed, namely measurement data fusion and state vector fusion, in several simulations carried out on a simple test network, traveled by probe vehicles and composed of 9 cells with an on ramp and an off ramp and with two fixed traffic sensors located in two different cells. Test results are promising and indicate higher accuracy of estimates obtained with new methods, particularly in the case of measurement data fusion.

A dynamic mesoscopic emission model for signalized intersections

The paper develops a mesoscopic emission model for signalized intersections that takes into account the dynamic variability of traffic conditions. It starts from an analytical model based on Akcelic theory and it permits to distinguish between vehicles in queue and vehicles entering/exiting the queue (deceleration and acceleration phases) using data derived from a Dynamic Traffic Assignment (DTA).

A procedure for urban route travel time forecast based on advanced traffic data: Case study of Rome

In this paper, authors report a procedure to forecast the route travel time, based on different advanced traffic data, both historical and current.

The Car Following Model with Relative Speed in Front on the Three-Lane Road

An improved car following model on one road with three lanes is presented in this paper, which considers the relative velocity in front on the main lane and the left and the right adjacent lanes. The stability criterion and neutral stability curve are obtained by linear stability theory. The nonlinear stability analysis is investigated further to get the solution of the modified Korteweg-de Vries (mKdV) equation and get the three areas of stability, metastability, and unstability. The new LRVD model (left and right lane velocity difference model) with bigger stable area can stabilize middle lane traffic flow better, which is proved by the linear theory, nonlinear theory, and the simulation. The LRVD model shows if drivers on the middle lane pay more attention to more cars in front on the two side lanes on the three-lane road, the middle lane traffic flow is certain to be more stable in real life. On the complex three-lane road, if intelligent traffic management system based on the huge traffic data for drivers is applied in real life, it is very helpful to ensure traffic safety, which is also the trend of transportation development in future.

The value of information for the accessibility to concurrent transit system services

The accessibility to different transport systems is an essential variable for modal choice, and not only for the choice between public and private transport, but also between concurrent transit systems. The study would like to identify and quantify the value of information (VOI) in the accessibility to concurrent transit services. In particular, the type of information considered and assessed is both en-route and pre-trip information provided to a user who has to choose between the concurrent transit services available. A methodology will be presented in order to identify and quantify the VOI, based on the development of behavioral models that make use of random utility theory. These models have been calibrated and validated using two different samples of data collected by a Revealed Preference (RP) survey to 200 users choosing between the different transit services available connecting Fiumicino Airport to the city center of Rome (Termini Station) in Italy. The proposed methodology is based on the development of behavioral models that make use of random utility theory. In particular two types of discrete choice models belonging to the family of random utility models are proposed: Multinomial Logit model (ML) and Nested Logit model (NL).