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This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes. From the demand side, travel demand for non-primary activities is derived from the destination choices that a traveler makes that minimizes travel disutility within the context of considerations of daily scheduling and routing. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix.First, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. Instead of giving a set of pre-fixed activity locations to visit, LSPxviii HAPP chooses the location for certain activity types given a set of candidate locations. A dynamic programming algorithm is adopted and further developed for LSP-HAPP in order to deal with the choices among a sizable number of candidate locations within the HAPP modeling structure. Potential applications of synthetic pattern generation based on LSP-HAPP formulation are also presented.Second, the Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The proposed formulation isolates eac

This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes. From the demand side, travel demand for non-primary activities is derived from the destination choices that a traveler makes that minimizes travel disutility within the context of considerations of daily scheduling and routing. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix.First, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. Instead of giving a set of pre-fixed activity locations to visit, LSPxviii HAPP chooses the location for certain activity types given a set of candidate locations. A dynamic programming algorithm is adopted and further developed for LSP-HAPP in order to deal with the choices among a sizable number of candidate locations within the HAPP modeling structure. Potential applications of synthetic pattern generation based on LSP-HAPP formulation are also presented.Second, the Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The proposed formulation isolates eac

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.

In this paper, an integrated destination choice model based on routing and scheduling considerations of daily activities is proposed. Extending the Household Activity Pattern Problem (HAPP), the Location Selection Problem (LSP-HAPP) demonstrates how location choice is made as a simultaneous decision from interactions both with activities having predetermined locations and those with many candidate locations. A dynamic programming algorithm, developed for PDPTW, is adapted to handle a potentially sizable number of candidate locations. It is shown to be efficient for HAPP and LSP-HAPP applications. The algorithm is extended to keep arrival times as functions for mathematical programming formulations of activity-based travel models that often have time variables in the objective.

Set Covering problems find the optimal provision of service locations while guaranteeing an acceptable level of accessibility for every demand points in a given area. Other than reliance on static,exogenously-imposed accessibility measures, these problems either exclude substantive infrastructure-vehicle interactions or only include fragmented infrastructure-vehicle interactions related to the routing considerations of households seeking refueling service as a requirement of performing routine, daily activities. Here, we address this problem by coupling a Location-Routing Problem (LRP) that uses the set covering model as a location strategy to the Household Activity Pattern Problem (HAPP) as the mixed integer scheduling and routing model that optimizes households’ participation in out-of-home activities. The problem addressed includes multiple decision makers: the public/private sector as the service provider, and the collection of individual households that make their own routing decisions to perform a given set of “out-of-home activities” together with a visit to one of the service locations. A solution method that does not necessarily require the full information of the coverage matrix is developed to reduce the number of HAPPs that needs to be solved. The performance of the algorithm, as well as comparison of the results to the set covering model, is presented. Although the application is focused on identifying the optimal locations of Hydrogen Fuel Cell Vehicle (HFCV) refueling stations, this proposed formulation can be used as a facility location strategy for any service activity that is generally toured with other activities.

49997-16-25, This paper develops a random utility based estimation framework for the Household Activity, Pattern Problem (HAPP). Based on the realization that output of complex activity-travel decisions, form a continuous pattern in space-time dimension, the estimation framework is treated as, a pattern selection problem. In particular, we define a variant of HAPP that has capabilities of, forecasting activity selection and durations in addition to activity sequencing. The framework, is comprised of three steps, (i) choice set generation, (ii) choice set individualization and (iii), multinomial logit estimation. The estimation results show that utilities for work, shopping and, disuilities for travel time, time outside home, and average tour delay are found to be significant, in activity-travel decision making.

49198-22-27, A Battery Electric Vehicle (BEV) feasibility considering State Of Charge (SOC) level is, assessed using multiday activity-travel patterns to overcome the limitations of using one-day, activity-travel patterns. Since multi-day activity-travel patterns are not readily available, we generate multi-day activity-travel patterns through sampling from readily available, single-day household travel survey data with considerations of day-to-day intrapersonal, variability. One of the key observation we make is that the distribution of interpersonal, variability in single-day travel activity datasets is similar to the distribution of intrapersonal, variability in multi-day datasets. Thus, interpersonal variability observed in cross-sectional, single-day data of a large population can be used to generate the day-to-day intrapersonal, variability. The proposed sampling method is based on activity-travel pattern type, clustering, travel distance and variability distribution to extract such information from single-day, data. Validation and stability tests of the proposed sampling methods are presented., BEV feasibility assessment results show that our sampling method combined with trivial, method provide better estimation of population wide BEV feasibility than using cross-sectional, data only.