Download urban traffic data mining and neural network models

URBAN TRAFFIC DATA MINING AND NEURAL
NETWORK MODELS
Maurizio Bielli (*) and Alessandro Bielli (**)
(*) Institute of Systems Analysis and Informatics – National Research Council
Viale Manzoni 30 –00185 Rome, Italy
E-mail : [email protected]
(**) Doctor in Mathematics –University of Rome 3 -Italy
ABSTRACT
1
URBAN TRAFFIC DATA MINING
The development of Intelligent Transportation Systems and the diffusion of urban traffic
control and monitoring by Autonomous Agents have created very large databases of realtime and historic traffic data. Therefore, data mining approaches are necessary to design
effective Decision Support Systems for traffic management and user information generation.
In particular, the completion of urban traffic data in a spatial-temporal context represents a
relevant problem particularly suited for traffic states estimation and forecasting, real-time
traffic control, dynamic OD matrices updating and so on. As regards the traffic data
completion, a constraints propagation approach has been proposed and implemented in the
framework of the KITS European Research Project. However, more efficient and flexible
methods have to be investigated and tested in order to support traffic data mining problems.
2
MULTILAYER NEURAL NETWORKS
Neural Networks have been widely applied for traffic forecasting and they can be used for
spatial data completion as well. To this aim, mobile detectors are able to collect necessary
traffic data relative to those variables involved in the extrapolation. In this way it is possible
to correlate traffic flows collected by fixed and mobile detectors for the training phase of the
neural network model. Then, it will provide in output flow values relative to arcs no
monitored on the basis of real data in input, relative to arcs with fixed detectors.
The aim of this paper is to investigate and demonstrate the capabilities of neural network
models for the estimation and completion of traffic data by the correlation among some
variables of the traffic process, i.e. the arc flows but also speed and queue variables.
Training methods based on optimization techniques, both in terms of accuracy (memory and
generalization) and training time (cpu and epocs number) will be discussed.
Further promising trends concern the implementation of new methods based on fast training
algorithms (quasi-Newton) by testing different neural network architectures such as those
developed with back-propagation techniques.
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3
CASE-STUDY APPLICATION
From an application point of view, a neural network model has been designed and tested on
a small road network of the Rome city. In particular, a main itinerary has been selected and
the traffic data completion on some arcs has been considered. A back-propagation algorithm
with momentum terms has been implemented and the results obtained show a good match
among simulated and real traffic data.
REFERENCES
Bielli M., Ambrosino G, Boero M. (Eds), Artificial Intelligence applications to traffic
engineering, VSP, 1994
Bielli M., Caramia M., Carotenuto P., Genetic algorithms in bus network optimization,
Transportation Research C, Vol. 10C, n.1, 19-34, 2002
Dougherty M.S., Applications of Neural Networks in Transportation, Transportation
Research C, Vol.5C, n.5, 1997
Bielli A., Un modello di reti neurali per la simulazione di traffico stradale, Doctoral Thesis
in Mathematics, Roma Tre University, Italy, November 2001
Bielli M., Reverberi P., New Operations Research and Artificial Intelligence approaches to
traffic engineering problems, EJOR, n.92, pp.550-572,1996
Faghri A., Hua J. Evaluation of Artificial Neural Network applications in transportation
engineering, Transportation Research Record 1358, TRB, 1992
Proceedings of the 11th Mini-EURO Conference on Artificial Intelligence in Transportation
Systems and Science, and the 7th EURO Working Group Transportation, Helsinki, August
1999
Bielli M., Carotenuto P. (Eds), Proceedings of the Rome Jubilee 2000 Conference on
Transportation, Rome, September 2000
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