A REGIONAL ECONOMY, LAND USE, AND TRANSPORTATION MODEL (RELU‐TRAN_©): FORMULATION, ALGORITHM DESIGN, AND TESTING*

Abstract

RELU is a dynamic general equilibrium model of a metropolitan economy and its land use, derived by unifying in a theoretically valid way, models developed by one of the authors []. RELU equilibrates floor space, land and labor markets, and the market for the products of industries, treating development (construction and demolition), spatial interindustry linkages, commuting, and discretionary travel. Mode choices and equilibrium congestion on the highway network are treated by unifying RELU with the TRAN algorithm of stochastic user equilibrium [Anas- Kim (1990)]. The RELU-TRAN algorithm's performance for a stationary state is demonstrated for a prototype consisting of 4-building, 4-industry, 4-labor-type, 15-land-use-zone, 68-link-highway-network version of the Chicago MSA. The algorithm solves 656 equations in a special block-recursive convergent procedure by iterations nested within loops and loops within cycles. Runs show excellent and smooth convergence from different starting points, so that the number of loops within successive cycles continually decreases. The tests also imply a numerically ascertained unique stationary equilibrium solution of the unified model for the calibrated parameters. * Alex Anas dedicates this paper to the memory of Britton Harris , a visionary and devoted advocate for the development of better quantitative models for metropolitan planning.Economic theory is rich in behavioral propositions which are useful in land use and transport modeling, and these fields have often unwisely neglected them. However, the converse is also true, and many of the pragmatic discoveries of modeling contained implications for economic behavior which were long neglected, or even worse denied. . . . planning and simulation have usually recognized that similar people in similar situations exhibit diverse behaviors. Gravity models capture that diversity, while standard economic models like linear programming do not. Only recently have the economists provided an alternative to their Procrustean bed in the form of discrete choice theory.-Britton Harris (1985, p. 547).

City size, network structure and traffic congestion

“…The latter assumption does not account for the temporal and spatial dimensions of congestion and is not consistent with the laws of physics, because travel speed is entirely determined by traffic density at a specific location and time (Ross and Yinger, 2000;Arnott, 2007). Even recent, conceptually sophisticated models of this type, such as the Regional Economy, Land Use and Transportation (RELU-TRAN) model (Anas and Liu, 2007;Anas, 2011), do not consider the intra-day traffic dynamics. Moreover, such models involve increased calibration costs for network modeling (including partitioning into a considerable number of zones) and require the availability of origin-destination trip matrices and traffic assignment procedures to produce a user equilibrium pattern of link travel times.…”

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

“…This project is still ongoing and our next step is to enable sophisticated system modeling through the RELU-TRAN model (Regional Economy Land Use-Transformation) [48] to establish a virtual collaborative laboratory and assess its performance in practice. This involves model validation and calibration in the back-end and a statistical analysis feature in the front-end of our GUI.…”

Additional detail in aggregate integrated land-use models via simulating developer pro forma thinking

“…For example, macro-economic conditions influence the number of travelers moving about a region on any given day while the ease of moving people and goods effects regions' economic viability. The field has many examples of models simulating these diverse activities that are methodologically integrated regardless of their formal "coupling" in software code (Anas & Liu, 2007;Pendyala et al, 2014;Wegener, 2012;Zhang & Kockelman, 2016). Figure 1 shows such a "model chain" with feedback loops implemented in current practice in purple lines (see for example Pendyala et.…”