Relaxed Singly Constrained Static Traffic Assignment Model with Elastic Demand: Application to Telework and Urban Development Scenarios in Austin, Texas

Increasing opportunities for telework and other forms of online activity participation are changing the landscape of transportation demand. This paper presents a relaxed singly constrained static traffic assignment model that extends existing approaches to accommodate both destination choice and the decision not to travel. We demonstrate that this formulation maintains desirable properties of existence and uniqueness of solutions, while being more flexible in capturing travel behaviors. A case study on the Austin, Texas, network examines scenarios related to telework adoption, targeted development in low-income areas, and changes to central business district attractiveness. Results show the model captures important behavioral shifts not reflected in simpler approaches such as models allowing destination choice or elastic demand alone. Using the relaxed singly constrained model, we demonstrate non-uniform traffic impacts across the network and find tradeoffs between congestion reduction and economic activity. The case study highlights potential equity impacts of both overall increases in telework and targeted changes in destination attractiveness that would otherwise be overlooked, suggesting that it is critical to develop more sophisticated models integrating traffic assignment with demand-side predictions.