This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
MALEANU, M. A., Contributions on the use of neural networks in traffic engineering, PhD. Thesis, Technical University of Civil Engineering Bucharest, 2023.Search in Google Scholar
Transit Capacity and Quality of Service Manual, Third Edition, https://nap.nationalacademies.org/catalog/24766/transit-capacity-and-quality-of-service-manual-third-edition, 26.02.2024.Search in Google Scholar
Multimodal Level of Service Analysis for Urban Streets, https://nap.nationalacademies.org/catalog/14175/multimodal-level-of-service-analysis-for-urban-streets, 26.02.2024.Search in Google Scholar
XIN, Y., FU, L., SACCOMANNO, F. F.: Assessing Transit Level of Service along Travel Corridors: Case Study Using the Transit Capacity and Quality of Service Manual, Transportation Research Record, 1927 (2005) 1, pp. 258-267, doi:10.1177/0361198105192700129Search in Google Scholar
ZUNIGA-GARCIA, N., ROSS, H. W., MACHEMEHL, R. B.: Multimodal Level of Service Methodologies: Evaluation of the Multimodal Performance of Arterial Corridors, Transportation Research Record, 2672 (2018) 15, pp. 142-154, doi:10.1177/0361198118776112Search in Google Scholar
FU, L., XIN, Y.: A New Performance Index for Evaluating Transit Quality of Service, Journal of Public Transportation, 10 (2007) 3, pp. 47-69, doi:10.5038/2375-0901.10.3.4.Search in Google Scholar
HENSHER, D.A., STOPHER, P., BULLOCK, P.: Service quality––developing a service quality index in the provision of commercial bus contracts, Transportation Research Part A-policy and Practice, 37 (2003), pp. 499-517, doi: 10.1016/S0965-8564(02)00075-7Search in Google Scholar
FURTH, P. G., MULLER, T. H. J.: Service Reliability and Hidden Waiting Time: Insights from Automatic Vehicle Location Data, Transportation Research Record, 1955 (2006) 1, pp. 79-87, doi.:10.1177/0361198106195500110Search in Google Scholar
Urban Street Design Guidelines. Adopted by Charlotte City Council October 22, 2007, https://www.charlottenc.gov/files/sharedassets/city/v/1/growth-and-development/documents/dev-center-fees/manual/usdg-full-document.pdf, 26.02.2024.Search in Google Scholar
KHOSRAVI, A., MAZLOUMI, E., NAHAVANDI, S., CREIGHTON, D.C., VAN LINT, J.: A genetic algorithm-based method for improving quality of travel time prediction intervals, Transportation Research Part C-emerging Technologies, 19 (2011), pp. 1364-1376, doi:10.1016/J.TRC.2011.04.002.Search in Google Scholar
JEONG, R., RILETT, R.: Bus arrival time prediction using artificial neural network model, The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) 2004, pp. 988-993, Washington, USA, 3-6 October 2004Search in Google Scholar
MAZLOUMI, E., ROSE, G., CURRIE, G., SARVI, M.: An Integrated Framework to Predict Bus Travel Time and Its Variability Using Traffic Flow Data, Journal of Intelligent Transportation Systems, 15 (2011), pp. 75 – 90, doi: 10.1080/15472450.2011.570109Search in Google Scholar
CELIKOGLU.,H.M.: Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling, Mathematical and Computer Modelling: An International Journal, 44 (2006) 7-8, pp. 640–658, doi:10.1016/j.mcm.2006.02.002Search in Google Scholar
XIE, M., LI, X., ZHOU, W., FU, Y.: Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks, Computational Intelligence and Neuroscience, vol. 2014 (2014,), Article ID 375487, 8 pages, doi: 10.1155/2014/375487Search in Google Scholar
PEI, Y., ZHOU, K., PENG, T.: Prediction Model of Passenger Waiting Time in High-Speed Rail Hub Based on BP Neural Network, Applied Mechanics and Materials, (2013) 321-324, pp.1903 – 1906, doi: 10.4028/www.scientific.net/AMM.321-324.1903Search in Google Scholar
Public transport network from Bucharest-Ilfov, https://stbsa.ro/maps1/Generala.pdf, 26.02.2024.Search in Google Scholar
