Assessment of Models to Estimate Bus-Stop Level Transit Ridership using Spatial Modeling Methods

Pulugurtha, Srinivas S.; Agurla, Mahesh

doi:10.5038/2375-0901.15.1.3

Cited by 49 publications

(26 citation statements)

References 10 publications

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“…Forecasting method for URT ridership at station level with multivariate regression models, also known as directforecast method, can forecast ridership based on the changes in factors affecting ridership throughout service area of stations [4][5][6][7][8]. This method considers factors affecting ridership such as built environment, social and economic attributes within service area, and ownership of stations as independent variables and average daily or peak hour ridership as dependent variable for regression analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Cervero and Kockelman analyzed relationship between travel demand and 3Ds (density, diversity, and design) [9]. "Density" refers to population and employment density within the service area of the stations, which is considered as the most important factor [4][5][6][7][8]. "Diversity" means land-use type and land-use mix within the service area of the stations [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

Yao

2016

Discrete Dynamics in Nature and Society

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Direct forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN) model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs), maximum relative error (MRE), smallest relative error (SRE), average relative error (ARE), and mean square root of relative error (MSRRE). Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

Yao

2016

Discrete Dynamics in Nature and Society

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“…); stop neighborhood socioeconomic and demographic factors (e.g. based on work by Pucher & Renne, 2003;Buehler & Pucher, 2012;Pulugurtha & Agurla, 2012;Taylor et al, 2009, etc. ); bus line service quality factors (e.g.…”

Section: Conceptual Modelmentioning

confidence: 99%

The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system

Chakrabarti

2015

Journal of Transport Geography

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“…model Matlab e parameters should be calibrated by the method of composite marginal likelihood (CML) in this paper Spatial econometric models Cardozo et al (2012) [10] GWR GIS e model could explain the diversity of results for spatial factors; however, it needs a large data sample Pulugurtha and Agurla (2012) [21] Spatial proximity method (SPM)/ spatial weighted method (SWM) SPSS/GIS e buffer of a stop has been divided into four bandwidths, and the best catchment can be identified based on SPM, but the weight function of SWM model (1/D2) is not continuous because of the defined set of bandwidths e problem of autocorrelation has been addressed; however, the application of GWR needs a larger data sample for support Ma et al (2018) [24] Geographically and temporally weighted regression (GTWR) -Explanatory variables are eliminated with the index of Pearson correlation lager than 0.6; however, the multicollinearity may exist among the variables and should be tested with variance inflation factor (VIF) index. presented a GIS-based weighted accessibility approach for estimating light rail transit peak-hour boarding, considering both the potential travel demand around a station and the attractiveness of target stations.…”

Section: Direct Ridership Forecastingmentioning

confidence: 99%

Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China

Guo

Huang

2020

Journal of Advanced Transportation

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Many large cities rely on Mass Rapid Transit (MRT) to increase passenger mobility. For efficiency, MRT stations should be arranged to attract maximal number of travelers. It is therefore important to develop methods for estimating MRT ridership forecasting models, which are important for policies on land use development or new MRT lines. Direct ridership models (DRMs) at the station level are superior in estimating the benefits of transit-oriented development policies. In this paper, a principal component regression (PCR) is proposed to overcome the issue of multicollinearity that widely occurs in multivariate regression analyses for DRM modeling, especially the ordinary least squares regression. Based on the analysis of 72 MRT stations in Wuhan, China, four principal components are obtained to explain the potential linkage to MRT ridership, which include built-environment related factors, jobs-housing spatial structure related factors, station attributes, and the large compound. Nineteen significant determinants have been identified, among which the four factors of office building area, land use mix, the number of restaurants, and financial institutions are the most influential factors. Built-environment-related factors exert more significant impact on MRT ridership than others. The distance to city center and the number of bus lines around stations have negative association with MRT demand. The proposed PCR-based DRM provides insights for forecasting transit demand brought about by new metro lines and forecasting the consequences of land use development.

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Assessment of Models to Estimate Bus-Stop Level Transit Ridership using Spatial Modeling Methods

Abstract: The objective of this research is to develop and assess bus transit ridership models at a bus-stop level using two spatial modeling methods: spatial proximity method (SPM)

Cited by 49 publications

References 10 publications

Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system

Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China

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