Transit-oriented development (TOD) has been promoted by planners and policy advocates as a solution to a variety of urban problems, including automobile traffic congestion, air pollution, and urban poverty. Since the enhanced accessibility offered by transit proximity is often capitalized into land and housing prices, many express concern that new transit investments will result in the displacement of the low-income populations likely to benefit most from transit access, a phenomenon which we term transit-induced gentrification. Whereas policy advocates have proposed a variety of interventions designed to ensure that affordable housing for low-income households is produced and preserved in areas proximate to transit stations, little is known about the effectiveness of these policy proposals. This article relies on an integrated land use/transportation model to analyze how TOD-based affordable housing policies influence the intraurban location of low-income households. We find that affordability restrictions targeted to new dwellings constructed in TODs are effective tools for promoting housing affordability and improving low-income households’ access to transit while simultaneously reducing the extent of transit-induced gentrification.

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Integrated land-use/transport models have five decades of history of both widely recognized successful implementations and implementations that remained far behind their originally stated goals. This paper summarizes the state of the art of integrated land-use/transport modeling and reports on findings from the Symposium for the Integration of Land-Use and Transport Models in Raitenhaslach, which is near Munich, in 2016. From these sources, the paper identifies major challenges in integrated land-use/transport modeling and proposes paths that support successful implementations. Particular attention is given to the coordination of short- and long-term decisions, the technical integration of models, microscopic versus macroscopic frameworks and appropriate levels of model complexity. The paper concludes with five themes that require further research to ensure that integrated land-use/transport models will keep up with modeling needs in the future.

In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented.

Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets