You are here:
Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions (proceedings)
Citation:
Kolling, J., N. Flanders, A. Bassi, L. Cox, A. Procter, R. Araujo, N. Tanner, AND D. Hines. Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions (proceedings). In Proceedings, 35th International Conference of the System Dynamics Society 2017, Cambridge, MA, July 16 - 20, 2017. Curran Associates, Inc., Red Hook, NY, 1-19, (2018).
Impact/Purpose:
This paper reports on the development of a system dynamics model for the interactions of a transportation project with land use, the economy and a number of sustainability issues of community concern. It is a proof-of-concept demonstration of integrated approaches to project evaluation and decision making in SHC.
Description:
In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic and non-linear interactions among transportation, land use, and socioeconomic systems. System dynamics (SD) provides a common framework for modeling the complex interactions among transportation and other related systems. This study uses a SD model to simulate the cascading impacts of a proposed light rail transit (LRT) system in central North Carolina, USA. The Durham-Orange Light Rail Project (D-O LRP) SD model incorporates relationships among the land use, transportation, and economy sectors to simulate the complex feedbacks that give rise to the travel behavior changes forecasted by the region’s transportation model. This paper demonstrates the sensitivity of changes in travel behavior to the proposed LRT system and the assumptions that went into the transportation modeling, and compares those results to the impacts of an alternative fare-free transit system. SD models such as the D-O LRP SD model can complement transportation studies by providing valuable insight into the interdependent community systems that collectively contribute to travel behavior changes.
