45 research outputs found
On the Short-term Prediction of Traffic State: An Application on Urban Freeways in ROME
No full textAbstract This paper explores the traffic state estimation on freeways in urban areas
combining point-based and route-based data in order to properly feed a second order traffic
flow model, recursively corrected by an Extended Kalman Filter. In order to overcome the
possible lack of real-time information, authors propose to use simulation-based data in order
to improve the accuracy of the traffic state estimation. This model was tested on a urban
freeway stretch in Rome, for which a set of real-time data during the morning of a typica
The Car Following Model with Relative Speed in Front on the Three-Lane Road
No full textAn 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
Dynamic demand estimation and prediction for traffic urban networks adopting new data sources
No full textTraffic state estimation based on data fusion techniques
No full text"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 clustering first – route second method for the solution of many-tomany Dial a Ride problem
No full textFCD data for on-street parking search time estimation
No full textThis paper investigates the problem of estimating on-street parking search time employing Floating Car Data (FCD). The parking search path is modelled as a spiral around the destination. Model calibration is based only on data detected by tracked vehicles. The proposed methodology can be used both in real-time to support user information and off-line to assess transport plans. In order to demonstrate its effectiveness for advanced transport modelling in urban areas, the results of a real-size application to the city of Rome are presente
