01697549

Component

The authors present a direct demand modelling approach for origin-destination (OD) public transportation commuting flows between municipalities in Switzerland. The purpose is to improve the gravity modelling approach for OD flows by applying a spatial autoregressive regression model and testing different spatial weighting schemes. Besides the usual characteristics to explain commuting, the authors include a variable based on mean income differences to examine interregional demand patterns. In addition, they treat for the endogenous nature of the newly constructed variable and test its ability to serve as the basis for the construction of a spatial weight matrix, thus replacing the commonly used travel time / distance metric. The authors apply Ordinary Least Squares (OLS), Generalized Method of Moments (GMM) and Intrumental Variable (IV) estimators to obtain unbiased and consistent parameter estimates. They compare in-sample predictions of the models among each other and to the flows of the National transport model. They use data from the 2000 Federal Census and found significant spatial dependence in the residuals of the gravity model and thus the need for spatial regression models. The authors use a valid set of instruments to account for endogeneity and show that income differences are underestimated in the gravity and spatial models if assumed exogenous. Neighbouring municipalities affect flows under consideration positively at origins and negatively at destinations. Last, the spatial autoregressive models relying on a combination of origin- and destination-centric weight matrix outperform the gravity models in terms of the predictive accuracy when network and economic distance weights are used.

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

19-06011

English

20p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Commuting; Flow; Gravity models; Origin and destination; Public transit; Regression analysis; Spatial analysis

Endogeneity; Spatial modeling

Switzerland

Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2019 Paper #19-06011

TRIS, TRB, ATRI

Dec 7 2018 9:31AM