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 spat
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Abstract. A review of an application of the models ASDA and FOTO for reconstruction, tracing and forecasting, of spatial-temporal congested pattems on highways based on local traffic measurements proposed in 1996- l 999 is presented. Some non-linear features of spatial-temporal congested pattems 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 ofthe propagation ofmoving 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 pattems of "synchronized flow". A short introduction to the three-phase traffic theory by Kemer as the basis ofthe models ASDA and FOTO is made. It is stressed that the models AS DA and FOTO perform without any validation of model parameters in different environmental and traffic conditions. First results of the application of AS DA and FOTO for the online automatic tracing of traffic flow pattems at the TCC (Traffic Control Center) R5delheim near Frankfurt (Germany) are discussed.
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Introduction
Traffic on highways can be either "free" or "congested" (e.g. [1]). The congested regime of traffic shows very complex spatial-temporal traffic patterns. Therefore, the online automatic recognition, tracing and forecasting of traffic patterns in the congested regime is one of the main problems for traffic control centres where measurements of traffic flow are collected and interpreted. The individual human behaviour in dense highway traffic leads to the emergence and existence of macroscopic spatial-temporal structures of congested traffic, which are very stable and can hinder free driving for a long time. Therefore, a study of empirical spatial-temporal pattern features is one of the methods to understand the effects of human behaviour in traffic. The detection and tracing of the spatial-temporal patterns is also an important research field in traffic technology for making an efficient highway management. On the other hand, analogously to the weather forecast where people decide their behaviour based on predicted information, the actual and predicted traffic patterns influence the individual driver's reactions and decisions in a similar manner. In the application field of telematics, the concept of co-operative driving [2] is an approach to increase safety and efficiency in congested traffic: based on technical improvements in M. Schreckenberg et al. (eds.), Human Behaviour and Traffic Networks © Springer-Verlag Berlin Heidelberg 2004
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Boris S. Kemer, Hubert Rehbom, Mario Aleksic, and Andreas Haug
comrnunication and sensor technology the individual vehic1e can both serve as sensor and actuator. With co-operative driving the traffic adaptive behaviour and harmonious driving may influence the congested traffic pattems in a positive way, i.e. delete or reduce actual congested pattems and/or hinder the emerg
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