Impact of Intelligent Networking on Vehicles Exiting at Urban Intersections
Author:
Liu Jun,Li Shu-Bin
Publisher
Springer Singapore
Reference25 articles.
1. Baek J, Sohn K (2016) Deep-learning architectures to forecast bus ridership at the stop and stop-to-stop levels for dense and crowded bus networks. Appl Artif Intell 30(9):861–885. https://doi.org/10.1080/08839514.2016.1277291
2. Bedard M, Guyatt GH, Stones MJ, Hirdes JP (2002) The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accid Anal Prev. https://doi.org/10.1093/geront/41.6.751
3. Chan KY (2012) Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm. IEEE Trans Intell Transp Syst 13(2):644–654. https://doi.org/10.1109/tits.2011.2174051
4. Chang H, Lee Y, Yoon B, Baek S (2012) Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences. IET Intell Transp Syst 6(3):292. https://doi.org/10.1049/iet-its.2011.0123
5. Chen C, Zhang D, Li N, Zhou ZH (2014) B-planner: planning bidirectional night bus routes using large-scale taxi GPS traces. IEEE Trans Intell Transp Syst 15(4):1451–1465. https://doi.org/10.1109/TITS.2014.2298892