This paper examines network design where OD demand is not known a priori, but is the subject of responses in household or user itinerary choices that depend on subject infrastructure improvements. Using simple examples, we show that falsely assuming that household itineraries are not elastic can result in a lack in understanding of certain phenomena; e.g., increasing traffic even without increasing economic activity due to relaxing of space-time prism constraints, or worsening of utility despite infrastructure investments in cases where household objectives may conflict. An activity-based network design problem is proposed using the location routing problem (LRP) as inspiration. The bilevel formulation includes an upper level network design and shortest path problem while the lower level includes a set of disaggregate household itinerary optimization problems, posed as household activity pattern problem (HAPP) (or in the case with location choice, as generalized HAPP) models. As a bilevel problem with an NP-hard lower level problem, there is no algorithm for solving the model exactly. Simple numerical examples show optimality gaps of as much as 5% for a decomposition heuristic algorithm derived from the LRP. A large numerical case study based on Southern California data and setting suggest that even if infrastructure investments do not result in major changes in itineraries the results provide much higher resolution information to a decision-maker. Whereas a conventional model would output the best set of links to invest given an assumed OD matrix, the proposed model can output the same best set of links, the same OD matrix, and a detailed temporal distribution of activity participation and travel, given a set of desired destinations and schedules.

A new facility location model and a solution algorithm are proposed that feature 1) itinerary-interception instead of flow-interception; 2) stochastic demand as dynamic service requests; and 3) queueing delay. These features are essential to analyze battery-powered electric shared-ride taxis operating in a connected, centralized dispatch manner. The model and solution method are based on a bi-level, simulation-optimization framework that combines an upper level multiple-server allocation model with queueing delay and a lower level dispatch simulation based on earlier work by Jung and Jayakrishnan. The solution algorithm is tested on a fleet of 600 shared-taxis in Seoul, Korea, spanning 603 km2, a budget of 100 charging stations, and up to 22 candidate charging locations, against a benchmark “naïve” genetic algorithm that does not consider cyclic interactions between the taxi charging demand and the charger allocations with queue delay. Results show not only that the proposed model is capable of locating charging stations with stochastic dynamic itinerary-interception and queue delay, butt that the bi-level solution method improves upon the benchmark algorithm in terms of realized queue delay, total time of operation of taxi service, and service request rejections. Furthermore, we show how much additional benefit in level of service is possible in the upper-bound scenario when the number of charging stations approaches infinity.

Freight forecasting models are data intensive and require many explanatory variables to be accurate. One problem, particularly in the United States, is that public data sources are mostly at highly aggregate geographic levels, while models with more disaggregate geographic levels are required for regional freight transportation planning. Second, supply chain effects are often ignored or modeled with economic input-output models which lack explanatory power. This study addresses these challenges by considering a structural equation modeling approach, which is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between commodities. A FAF-based structural commodity generation model is specified and estimated and shown to provide a better fit to the data than independent regression models for each commodity. Three features of the model are discussed: indirect effects, supply chain elasticity, and intrazonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using data imputed with a goal programming method.