Analysis of Selecting Gated Community as Opening Its Micro-Inter-Road Network
- 1 Engineering Mechanical Engineering School, Shanghai University of Engineering Science, Shanghai, China
- 2 Department of Mechanical Systems Engineering, Yamagata University, Yamagata, Japan
- 3 Engineering Mechanical Engineering School, Shanghai University of Engineering Science, Shanghai, China
Abstract
The opening of gated community to expand the micro-road network in the urban traffic system is a hot topic on the urban congestion. To satisfy the demand of opening early choosing case, this paper proposed a comprehensive selecting framework on qualified communities and its appropriate opening time. Firstly, the static influential factors on internal road structure, boundary road structure and traffic flow are qualitatively analyzed. Then, an evaluation opening state index system based on describing accurately traffic flow state is obtained, which takes the opening factors into account at the boundary road network. In this structure, the modified fuzzy C-means (FCM) method calculates the fuzzy entropy weight and range of each opening states index. Finally, the simulation results show that the proposed method is capable of selecting qualified community and the optimum opening time.
- Bar-Gera, H. (2007) Evaluation of a Cellular Phone-Based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel. Transportation Research Part C: Emerging Technologies, 15, 380. https://doi.org/10.1016/j.trc.2007.06.003
- National Research Council (2000) Transportation Research Board, Highway Capacity Manual. TRB, National Research Council, Washington DC.
- Shao, Y.-H., Cheng, L. and Wang, W. (2005) Application of Entropy-Maximizing (EM) Model in Traffic Distribution Forecast. Journal of Transportation Systems Engineering and Information Technology, 5, 83-87.
- Guo, Y.-M., Zhao, Y., Zhou, Y.-M., Xiao, Z.-B. and Yang, X.-J. (2017) On the local Fractional LWR Model in Fractal Traffic Flows in the Entropy Condition. Mathematical Methods in Applied Science, 40, 6127-6132. https://doi.org/10.1002/mma.3808
- Lozano, A., Manfredi, G. and Nieddu, L. (2009) An Algorithm for the Recognition of Levels of Congestion in Road Traffic Problems. Mathematics and Computers in Simulation, 79, 1926. https://doi.org/10.1016/j.matcom.2007.06.008
- Sun, X.L. (2013) Urban Road Traffic State Evaluation and Prediction: A New Scheme with Applications. Ph.D. Thesis, Beijing Jiaotong University, Beijing.
- Antoniou, H., Koutsopoulos, N. and Yannis, G. (2013) Dynamic Data-Driven Local Traffic State Estimation and Prediction. Transportation Research Part C: Emerging Technologies, 34, 89-107. https://doi.org/10.1016/j.trc.2013.05.012
- Feng, K.Y. (1992) Entropy and Component Properties of Mixed Traffie. Journal of Funan University, 19, No. 2.
- Min, W. and Wynter, L. (2011) Real-Time Road Traffic Prediction with Spatio-Temporal Correlations. Transportation Research Part C: Emerging Technologies, 19, 606. https://doi.org/10.1016/j.trc.2010.10.002
- Yuan, J. and Mills, K.A. (2005) A Cross-Correlation-Based Method for Spatial-Temporal Traffic Analysis. Performance Evaluation, 61, 163. https://doi.org/10.1016/j.peva.2004.11.003
- Bhattacharya, S. and Bhatnagar, V. (2012) Fuzzy Data Mining: A Literature Survey and Classification Framework. International Journal of Networking and Virtual Organisations, 11, 382. https://doi.org/10.1504/IJNVO.2012.048925