An intelligent location and state reorganization of traffic signal
In all geo-database related to traffic, beside storing roads data, the information associated to traffic signals such as location, types of traffic signals, side street name, and so on are also stored in that database. In reality, the reason of defining traffic signals for road is the situations and conditions which the roads have. So the existence of traffic signals in the network is related to the parameters of the road. In this paper, instead of storing traffic signal data in the database, a novel method is introduced which implemented on the road network. As a result, the spatial and non-spatial information of traffic signals in the network are extracted based on the location and attribute of the road network. The proposed method is implemented on the network; the result of the intelligent method is compared with the traffic signals information which stored in the database. By comparing the locations and states of proposed traffic signals and the real ones, the overall accuracy for recognizing locations of traffic signal is obtained 94% and the overall accuracy for recognizing states of traffic signal is obtained 89%.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Al Mufti, M., Al Hadhrami, E., Taha, B., & Werghi, N. (2018). SAR automatic target recognition using transfer learning approach. In 2018 International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore. IEEE. https://doi.org/10.1109/ICoIAS.2018.8494149
Annunziato, M., Bertini, I., Pannicelli, A., & Pizzuti, S. (2003). Evolutionary feed-forward neural networks for traffic prediction. In Proc. of EUROGEN2003, Barcelona, Spain.
Behzadi, S., & Alesheikh, A. A. (2014). Cellular automata vs. object-automata in traffic simulation. International Journal of Remote Sensing Applications, 4(1), 61–69.
Clay, M., & Johnston, R. (2006). Multivariate uncertainty analysis of an integrated land use and transportation model: MEPLAN. Transportation Research Part D, 11(3), 191–203. https://doi.org/10.1016/j.trd.2006.02.001
Fraile, A., Larrodé, E., Magreñán, Á. A., & Sicilia, J. A. (2016). Decision model for siting transport and logistic facilities in urban environments: A methodological approach. Journal of Computational and Applied Mathematics, 291, 478–487. https://doi.org/10.1016/j.cam.2014.12.012
Fu, M., Kelly, J. A., & Clinch, J. P. (2017). Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results. Journal of Transport Geography, 58, 186–195. https://doi.org/10.1016/j.jtrangeo.2016.12.002
Jabbari, M., & Behzadi, S. (2019). Modelling effects of land use changes on traffic based on proposed traffic simulator. Computational Engineering and Physical Modeling, 2(3), 61–70. https://doi.org/10.22115/cepm.2020.207903.1073
Li, D., Cova, T. J., & Dennison, P. E. (2019). Setting wildfire evacuation triggers by coupling fire and traffic simulation models: a spatiotemporal GIS approach. Fire Technology, 55(2), 617–642. https://doi.org/10.1007/s10694-018-0771-6
Mao, Y., Wang, Y., & Zhang, L. (2008). Research on method of subsection learning of double- layers BP neural network in prediction of traffic volume. Jisuanji Gongcheng yu Yingyong (Computer Engineering and Applications), 44(13), 203–205.
Maoh, H., Kanaroglou, P., Scott, D., Paez, A., & Newbold, B. (2009). IMPACT: An integrated GIS-based model for simulating the consequences of demographic changes and population ageing on transportation. Computers, Environment and Urban Systems, 33(3), 200–210. https://doi.org/10.1016/j.compenvurbsys.2008.10.004
Maria, C., & Gleriani, J. (2005). Cellular automata and neural networks as a modeling framework for the simulation of urban land use change. In Anais XII Simpósio Brasileiro de Sensoriamento Remoto (pp. 3697–3705).
Ocalir-Akunal, E. V. (2016). Decision support systems in transport planning. Procedia Engineering, 161, 1119–1126. https://doi.org/10.1016/j.proeng.2016.08.518
Ozkurt, C., Camci, F., & Stanbul, T. (2009). Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Mathematical and Computational Applications, 14(3), 187–196. https://doi.org/10.3390/mca14030187
Shaw, S., & Xin, X. (2003). Integrated land use and transportation interaction: a temporal GIS exploratory data analysis approach. Journal of Transport Geography, 11(2), 103–115. https://doi.org/10.1016/S0966-6923(02)00070-4
Srinivasan, D., Choy, M., & Cheu, R. (2006). Neural networks for real-time traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 7(3), 261–272. https://doi.org/10.1109/TITS.2006.874716
Wang, X. (2005). Integrating GIS, simulation models, and visualization in traffic impact analysis. Computers, Environment and Urban Systems, 29(4), 471–496. https://doi.org/10.1016/j.compenvurbsys.2004.01.002
Ying, J. (2007). Continuous optimization method for integrated land use/transportation models. Journal of Transportation Systems Engineering and Information Technology, 7(3), 64–72. https://doi.org/10.1016/S1570-6672(07)60022-1