Abstract
In this study, in the purpose of providing a dynamic procedure for reliable travel time specification, the performance of a neural functional approximation method is analysed. The numerical analyses are carried out on the succeeding sections of a freeway segment inputting data obtained from microwave radar sensor units located successively at the cross-sections of a freeway segment of approximately 4 km. Measurements on traffic variables, i.e., vehicle counts, speed, and occupancy, for the reference time periods are processed. The structure of the employed radial basis function neural networks are configured considering the data of a three-lane freeway segment obtained by succeeding sensors located in side-fired position. Travel time measures approximated by the neural models are compared with the corresponding field measurements obtained by probe vehicle. Results prove neural model's performance in representing spatiotemporal variation of flow dynamics as well as travel times. Adaptability of the proposed travel time specification procedure to real-time intelligent control systems is a possible future extension.
Original language | English |
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Pages (from-to) | 613-620 |
Number of pages | 8 |
Journal | Procedia - Social and Behavioral Sciences |
Volume | 20 |
DOIs | |
Publication status | Published - 2011 |
Event | 14th Meeting of the Euro Working Group on Transp. - In Quest for Adv. Models, Tools, and Methods for Transp. and Logist., EWGT, 26th Mini-EURO Conf. - Intelligent Decis. Making in Transp. and Logist., MEC, 1st Eur. Sci. Conf. on Air Transp. - RH - Poznan, Poland Duration: 6 Sept 2011 → 9 Sept 2011 |
Keywords
- Intelligent Transportation System
- Neural networks
- Traffic flow
- Travel time