Factors affecting bus bunching at the stop level: A geographically weighted regression approach

Andres, 2017, A predictive-control framework to address bus bunching, Transp. Res. B Methodol., 104, 123, 10.1016/j.trb.2017.06.013 Arriagada, 2019, Modeling bus bunching using massive location and fare collection data, J. Intell. Transp. Syst., 23, 332, 10.1080/15472450.2018.1494596 Blainey, S., Mulley, C., 2013. Using Geographically Weighted Regression to forecast rail demand in the Sydney Region. In: Australasian Transport Research Forum (ATRF), Brisbane, Queensland, Australia. Cardozo, 2012, Application of geographically weighted regression to the direct forecasting of transit ridership at station-level, Appl. Geogr., 34, 548, 10.1016/j.apgeog.2012.01.005 Chiou, 2015, Factors affecting public transportation usage rate geographically weighted regression, Transp. Res. A Policy Pract., 78, 161, 10.1016/j.tra.2015.05.016 Chow, 2006, Transit ridership model based on geographically weighted regression, Transp. Res. Record, 1972, 105, 10.1177/0361198106197200113 De Jong, 1984, On extreme values of Moran's I and Geary's c, Geogr. Anal., 16, 17, 10.1111/j.1538-4632.1984.tb00797.x Diab, E., Bertini, R., El-Geneidy, A., 2016. Bus transit service reliability Understanding the impacts of overlapping bus service on headway delays and determinants of bus bunching. In: The 95th Annual Meeting of the Transportation Research Board, Washington, DC, USA. Du, 2006, Relationship between transport accessibility and land value local model approach with geographically weighted regression, Transp. Res. Record, 1977, 197, 10.1177/0361198106197700123 Enayatollahi, 2019, Modelling bus bunching under variable transit demand using cellular automata, Publ. Transp., 11, 269, 10.1007/s12469-019-00203-2 Feng, 2011, Empirical findings of bus bunching distributions and attributes using archived AVL/APC bus data, 4330 Fonzone, 2015, A model of bus bunching under reliability-based passenger arrival patterns, Trans. Res. Proc., 7, 276 2008 Fotheringham, 2003 Furth, P.G., Hemily, B., Muller, T.H., Strathman J.G., 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Transit Cooperative Research program (TCRP) Report 113, Transportation Research Board, Washington, D.C., USA. He, 2020, A holding strategy to resist bus bunching with dynamic target headway, Comput. Ind. Eng., 140, 10.1016/j.cie.2019.106237 Iliopoulou, 2019, Combining ITS and optimization in public transportation planning state of the art and future research paths, Eur. Transp. Res. Rev., 11, 10.1186/s12544-019-0365-5 Iliopoulou, 2018, The bus bunching problem empirical findings from spatial analytics, 871 Iliopoulou, 2020, Identifying spatio-temporal patterns of bus bunching in urban networks, J. Intell. Transp. Syst., 10.1080/15472450.2020.1722949 Lee, 2001, Developing a bivariate spatial association measure an integration of Pearson's r and Moran's I, J. Geogr. Syste., 3, 369, 10.1007/s101090100064 Liang, 2019, Coordinated control method to self-equalize bus headways an analytical method, Transportmetr. B Transp. Dyn., 7, 1175, 10.1080/21680566.2019.1589597 Mesbah, 2012, Spatial and temporal visualization of transit operations performance data at a network level, J. Transp. Geogr., 25, 15, 10.1016/j.jtrangeo.2012.07.005 Moreira-Matias, 2012, Bus bunching detection by mining sequences of headway deviations, Vol. 7377, 77 Moreira-Matias, 2014, An incremental probabilistic model to predict bus bunching in real-time, 227 Nakaya, T., Fotheringham, S., Charlton, M., Brunsdon, C., 2009. Semiparametric geographically weighted generalised linear modelling in GWR 4.0. Nguyen, 2019, Understanding the factors that influence the probability and time to streetcar bunching incidents, Publ. Transp., 11, 299, 10.1007/s12469-019-00201-4 Pan, 2019, Exploring spatial variation of the bus stop influence zone with multi-source data a case study in Zhenjiang, China, J. Transp. Geogr., 76, 166, 10.1016/j.jtrangeo.2019.03.012 Pulugurtha, 2011, Pedestrian crash estimation models for signalized intersections, Accid. Anal. Prev., 43, 439, 10.1016/j.aap.2010.09.014 Pulugurtha, 2008, Hazardous bus stops identification an illustration using GIS, J. Publ. Transp., 11, 4, 10.5038/2375-0901.11.2.4 Rashidi, 2017, Using automatic vehicle location data to model and identify determinants of bus bunching, Transp. Res. Proc., 25, 1444 Rogerson, 2014 Schmöcker, 2016, Bus bunching along a corridor served by two lines, Transp. Res. B Methodol., 93, 300, 10.1016/j.trb.2016.07.005 Tu, 2018, Spatial variations in urban public ridership derived from GPS trajectories and smart card data, J. Transp. Geogr., 69, 45, 10.1016/j.jtrangeo.2018.04.013 Verbich, 2016, Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times, Publ. Transp., 8, 225, 10.1007/s12469-016-0126-y Wu, 2016, Designing robust schedule coordination scheme for transit networks with safety control margins, Transp. Res. B Methodol., 93, 495, 10.1016/j.trb.2016.07.009 Wu, 2017, Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour, Transp. Res. B Methodol., 104, 175, 10.1016/j.trb.2017.06.019 Wu, 2019, Stochastic bus schedule coordination considering demand assignment and rerouting of passengers, Transp. Res. B Methodol., 121, 275, 10.1016/j.trb.2019.01.010 Wu, 2019, Simulation-based robust optimization of limited-stop bus service with vehicle overtaking and dynamics a response surface methodology, Transp. Res. E Logist. Transp. Rev., 130, 61, 10.1016/j.tre.2019.08.012