Ann Modeling For Predicting Car Travel Time using Bus As Probe
Akram S. Kotb
Akram S. Kotb , Construction and Building Eng. Department, Faculty of Engineering and Technology, Arab Academy for Science & Technology & Maritime Transport, Cairo, Egypt.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3260-3265 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36121081219/2019©BEIESP | DOI: 10.35940/ijitee.L3612.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The critical issue of Intelligent Transportation Systems (ITS) applications is obtaining the near real time information of travel times. This paper proposes a dependable model for predicting car travel time on urban roads in Greater Cairo using buses as probes. The GPS receivers, which are installed on test vehicles and buses, used to collect real travel time data along the urban roads. The travel times of bus and car are compared in order to recognize similarities and differences between the trip profiles of test vehicles and buses. According to the comparison results, the model is developed and validated using Artificial Neural Network (ANN) for estimating car travel time using buses’ travel time with acceptable level of accuracy equals 10.53%.
Keywords: ANN, Travel Time, Urban Roads, Bus as Probe
Scope of the Article: Urban Engineering