Mario Aleksic

Traffic state detection with floating car data in road networks

A method for a reporting behavior at optimal costs of single vehicles (FCD: floating car data) in road networks with the aim of a high quality of traffic state recognition is presented. It is shown that based on minimum two FCD messages the substantial information of a typical traffic incident in a traffic center can be recognized. The two relevant periods of such an obstruction of traffic in road networks are the periods, in which either a travel time increase takes place due to congestion emergence or a travel time decrease because of congestion dissolution. A statistic analysis already shows the high quality of the reconstruction of the actual travel times in the net with 1.5% equipped FCD vehicles and a reduction of the FCD message sending of the vehicles by suppression of redundant incident information. Incidents with at least 20 min duration can be recognized with a probability of 65% with an penetration rate of 1.5% FCD vehicles within the whole amount of vehicles, whereby the FCD vehicles send only in each incident case two messages per event.

Traffic prediction systems in vehicles

A goal of the contribution is the presentation of both autonomous and center-based traffic prediction conceptions in vehicles. Components of software that have been developed based on these conceptions are conceived thereby system-oriented flexibly between vehicle and service center. Additionally requirements at system components are considered such as travel time curves, digital map, and routing software to traffic prediction. Systems for traffic prediction developed in a vehicle are demonstrated including a visualisation design.

Forecasting of Traffic Congestion

Results of investigations of a recent method for the automatic tracing of moving traffic jams and of the prediction of time-dependent vehicle trip times are presented using different levels of data inputs. The method is based on the previous findings that moving jams possess some characteristic parameters, i e., the parameters are unique, coherent, predictable and reproducible. Based on available data it is found that the method, which performs without any validation of the parameters of a model under different infrastructures of a highway, weather, etc., can be applied for a reliable forecasting of traffic congestions on a highway.

Automatic Tracing and Forecasting of Moving Traffic Jams Using Predictable Features of Congested Traffic Flow

Abstract The results of investigations of a recent model (Kerner, et al ., 1997) for the automatic tracing of moving traffic jams and of the prediction of the jam propagation and time-dependent vehicle trip times are presented. It is found that the model which performs without any validation of the parameters of a model under different infrastructures of a highway, weather, etc. and which is based on the previous findings that moving traffic jams possess some characteristic parameters (i.e., the parameters of moving jams are unique, reproducible and predictable) can be applied for a reliable traffic forecasting on a highway.

Methods for Automatic Tracing and Forecasting of Spatial-Temporal Congested Patterns: A Review

A review of an application of the models ASDA and FOTO for reconstruction, tracing and forecasting, of spatial-temporal congested patterns on highways based on local traffic measurements proposed in 1996–1999 is presented. Some non-linear features of spatial-temporal congested patterns which are linked with the individual driver behaviour are considered. The model ASDA (Automatische Staudynamikanalyse: Automatic Tracing of Moving Traffic Jams) is devoted to the tracing and prediction of the propagation of moving traffic jams. The model FOTO (Forecasting of Traffic Objects) is devoted to the identification of traffic phases “synchronized flow” and “wide moving jam” and to the tracing and prediction of the patterns of “synchronized flow”. A short introduction to the three-phase traffic theory by Kerner as the basis of the models ASDA and FOTO is made. It is stressed that the models ASDA and FOTO perform without any validation of model parameters in different environmental and traffic conditions. First results of the application of ASDA and FOTO for the online automatic tracing of traffic flow patterns at the TCC (Traffic Control Center) Rodelheim near Frankfurt (Germany) are discussed.