Household Activity Pattern Problem Research Articles

Household Activity Pattern Problem with Autonomous Vehicles

Household Activity Pattern Problem with Autonomous Vehicles

Household use of autonomous vehicles with ride sourcing

The prospect of autonomous vehicles (AVs) offers the possibility that a household could reduce household owned vehicles to a single vehicle. At the same time, with AVs, a Shared Autonomous Vehicle (SAV) system will rise as a primary mode of serving travel demands. This study aims to model households’ activity/travel decisions using both household owned AVs as well as readily available SAVs to perform daily activities. We formulate the Household Activity Pattern Problem with Autonomous Vehicles and Ride Sourcing (HAPPAV-RS), as a mixed integer linear program. The model generates an optimal activity/travel patterns for household members under spatial and temporal constraints. The model can capture driverless operations such as AV pick-up and drop-off, parking availability, empty trips and ridesharing among household members, as well as the use of SAVs with the request waiting time. A decomposition method is used to solve the NP-hard problem HAPPAV-RS. scenarios on using AVs and SAVs with different travel mode availability, AV/SAV cost and SAV waiting time are designed to enable sensitivity analysis. Various travel metrics such as activity pattern feasibility, household’s total travel disutility, travel mode VMT and AV-SAV trip coverage are reported.

Comprehensive scenario analysis of household use of battery electric vehicles

This study assesses the potential use of Battery Electric Vehicles (BEVs) in place of conventional Internal Combustion Engine Vehicles (ICEVs) at the household level. The assumption is that travelers will change their travel pattern and behaviors to overcome the limitations of BEVs, such as limited range and long charging times. We generate four scenarios representing different levels of traveler adaptation and apply them to sample households from the California Statewide Travel Survey. These scenarios are defined to assess the effect of changes when one of household vehicles is replaced with a BEV, in particular scheduling flexibilities and intra-household interactions on activity and vehicle allocation. These changes in their travel behavior are simulated by the Household Activity Pattern Problem with Electric vehicle (HAPPEV) model which is a variant of the well-known Household Activity Pattern Problem (HAPP). A sequential activity allocation and insertion heuristic is developed to solve the HAPPEV. In addition to testing different levels of scheduling flexibility and travel behavior, we conduct a scenario analysis on activity-travel pattern flexibility, fuel/electricity prices, BEV range and charging location availability. The results show that (1) BEVs when used as household secondary vehicles provide better environmental/economic benefits, (2) work place charging improves activity-travel pattern feasibility and travel disutility (3) electricity/fuel price impact is not significant, (4) current BEV incentives should be continued, and (5) BEV range is still a significant factor.

A random utility based estimation framework for the household activity pattern problem

This paper develops a random utility based estimation framework for the Household Activity Pattern Problem (HAPP). Based on the realization that outputs 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) model estimation. The estimation results show that utilities for work, shopping and disutilities for travel time, time outside home, and average tour delay are found to be significant in activity-travel decision making.

Network-oriented household activity pattern problem for system optimization

The recently emerging trend of self-driving vehicles and information sharing technologies, made available by private technology vendors, starts creating a revolutionary paradigm shift in the coming years for traveler mobility applications. By considering a deterministic traveler decision making framework at the household level in congested transportation networks, this paper aims to address the challenges of how to optimally schedule individuals’ daily travel patterns under the complex activity constraints and interactions. We reformulate two special cases of household activity pattern problem (HAPP) through a high-dimensional network construct, and offer a systematic comparison with the classical mathematical programming models proposed by Recker (1995). Furthermore, we consider the tight road capacity constraint asanother special case of HAPP to model complex interactions between multiple household activity scheduling decisions, and this attempt offers another household-based framework for linking activity-based model (ABM) and dynamic traffic assignment (DTA) tools. Through embedding temporal and spatial relations among household members, vehicles and mandatory/optional activities in an integrated space-time-state network, we develop two 0–1 integer linear programming models that can seamlessly incorporate constraints for a number of key decisions related to vehicle selection, activity performing and ridesharing patterns under congested networks. The well-structured network models can be directly solved by standard optimization solvers, and further converted to a set of time-dependent state-dependent least cost path-finding problems through Lagrangian relaxation, which permit the use of computationally efficient algorithms on large-scale high-fidelity transportation networks.

A Random Utility Based Estimation Framework for the Household Activity Pattern Problem

Abstract: This paper develops a random utility based estimation framework for the Household Activity Pattern Problem (HAPP). Based on the realization that outputs 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) model 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.

Network-oriented Household Activity Pattern Problem for System Optimization

Abstract: The recently emerging trend of self-driving vehicles and information sharing technologies, made available by private technology vendors, starts creating a revolutionary paradigm shift in the coming years for traveler mobility applications. By considering a deterministic traveler decision making framework at the household level in congested transportation networks, this paper aims to address the challenges of how to optimally schedule individuals’ daily travel patterns under the complex activity constraints and interactions. We reformulate two special cases of household activity pattern problem (HAPP) through a high-dimensional network construct, and offer a systematic comparison with the classical mathematical programming models proposed by Recker (1995). Furthermore, we consider the tight road capacity constraint as another special case of HAPP to model complex interactions between multiple household activity scheduling decisions, and this attempt offers another household-based framework for linking activity-based model (ABM) and dynamic traffic assignment (DTA) tools. Through embedding temporal and spatial relations among household members, vehicles and mandatory/optional activities in an integrated space-time-state network, we develop two 0-1 integer linear programming models that can seamlessly incorporate constraints for a number of key decisions related to vehicle selection, activity performing and ridesharing patterns under congested networks. The well-structured network models can be directly solved by standard optimization solvers, and further converted to a set of time-dependent state-dependent least cost path-finding problems through Lagrangian relaxation, which permit the use of computationally efficient algorithms on large-scale high-fidelity transportation networks.

Constructing the feasible space–time region of the Household Activity Pattern Problem

ABSTRACTIn this paper, a method of constructing the feasible region in space–time dimension of the well-known Household Activity Pattern Problem (HAPP) is developed. HAPP is an optimization model that generates an optimal set of full-day activity-travel decisions. Based on the definition of an activity and its spatial and temporal constraints, feasible space–time region for the completion of one activity is derived. Then, a full-day feasible space–time region is determined as an intersection of a set of feasible regions for activities to be performed on a given travel day. This feasible space–time region is shown to be consistent with the concept of space–time prism. An algorithm is developed for the construction of an activity-specific feasible space–time region for a network-based travel environment. The algorithm generates a set of time windows for each activity and for each node that later converts to link-based time windows of accessibility.

Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics

ABSTRACTGoods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles

A hydrogen refueling station siting model that considers scheduling and routing decisions of individual vehicles is presented. By coupling a location strategy of the set covering problem (SCP) and a routing and scheduling strategy of the household activity pattern problem, this problem falls into the category of location routing problems. It introduces a tour-based approach to refueling station siting, with tour-construction capability within the model. There are multiple decision makers in this problem: the public 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 a refueling location. A solution method that does not require the full information of the coverage matrix is developed to reduce the computational burden. Compared to the point-based SCP the results indicate that the minimum infrastructure requirement may be overestimated when vehicle (refueling demand)-infrastructure (refueling supply) interactions with daily out-of-home activities are excluded.

Activity-based market equilibrium for capacitated multimodal transport systems

Empirical studies have shown that demand for multimodal transport systems is highly correlated with activity schedules of individuals. Nonetheless, existing analytical equilibrium models of multimodal systems have only considered trip-based demand. We propose a new market equilibrium model that is sensitive to traveler activity schedules and system capacities. The model is based on a constrained mixed logit model of activity schedule choice, where each schedule in the choice set is generated with a multimodal extension of the household activity pattern problem. The extension explicitly accounts for both passenger choices of activity participation and multimodal choices like public transit, walking, and vehicle parking. The market equilibrium is achieved with Lagrangian relaxation to determine the optimal dual price of the capacity constraint, and a method of successive averages with column generation finds an efficient choice set of activity schedules to assign flows over the dynamic network load capacities. An example illustrates the model and algorithm, effects similar to Vickrey’s morning commute model can be observed as a special case. A case study of the Oakville Go Transit station access “last mile” problem in the Greater Toronto Area is conducted with 166 survey samples reflecting 3680 individuals. Results suggest that a $10 fixed parking fee at Oakville station would lead to a reduction of access auto share from 54.8% to 49.5%, an increase in access transit share from 20.7% to 25.9%, and a disutility increase of 11% for the of single-activity residents of Oakville.

Activity-based Market Equilibrium for Capacitated Multimodal Transport Systems

Empirical studies have shown that demand for multimodal transport systems is highly correlated with activity schedules of individuals. Nonetheless, existing analytical equilibrium models of multimodal systems have only considered trip-based demand. We propose a new market equilibrium model that is sensitive to traveler activity schedules and system capacities. The model is based on a constrained mixed logit model of activity schedule choice, where each schedule in the choice set is generated with a multimodal extension of the Household Activity Pattern Problem. The extension explicitly accounts for both passenger choices of activity participation and multimodal choices like public transit, walking, and vehicle parking. The market equilibrium is achieved with Lagrangian relaxation to determine the optimal dual price of the capacity constraint, and a method of successive averages with column generation finds an efficient choice set of activity schedules to assign flows over the dynamic network load capacities. An example illustrates the model and algorithm, effects similar to Vickrey's morning commute model can be observed as a special case. A case study of the Oakville Go Transit station access “last mile” problem in the Greater Toronto Area is conducted with 166 survey samples reflecting 3,680 individuals. Results suggest that a $10 fixed parking fee at Oakville station would lead to a reduction of access auto share from 54.8% to 49.5%, an increase in access transit share from 20.7% to 25.9%, and a disutility increase of 11% for the of single-activity residents of Oakville.

Stochastic Preplanned Household Activity Pattern Problem with Uncertain Activity Participation (SHAPP)

The so-called activity-based approach to analysis of human interaction with the social and physical environments dates back to the original time-space geography works of Hägerstrand and his colleagues at the Lund School in 1970. Despite their obvious theoretical attractiveness, activity-based approaches to understanding and predicting travel behavior have suffered from the absence of an analytical framework that unifies the complex interactions among the resource allocation decisions made by households in conducting their daily affairs outside the home while preserving the utility-maximizing principles presumed to guide such decisions. In this paper, we develop a computationally tractable system, based on an extension and modification of some rather well-known network-based formulations in operations research, to model human dynamics in uncertain environments. The research builds on the mathematical programming formulation of the household activity pattern problem by embedding stochastic elements in the planned household activity schedule decision process that capture the uncertainty of the need for rescheduling.

On activity-based network design problems

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 to 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 link investment decisions compared to a conventional model, the results provide much higher resolution temporal OD 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 daily OD matrix, and a detailed temporal distribution of activity participation and travel from which changes in peak period OD patterns can be observed.

The location selection problem for the household activity pattern problem

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.

On Activity-based Network Design Problems

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 to 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 link investment decisions compared to a conventional model, the results provide much higher resolution temporal OD 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 daily OD matrix, and a detailed temporal distribution of activity participation and travel from which changes in peak period OD patterns can be observed.

Activity‐Based Travel Scenario Analysis with Routing Problem Reoptimization

AbstractActivity‐based travel scenario analysis and network design using a household activity pattern problem (HAPP) can face significant computational cost and inefficiency. One solution approach, called reoptimization, makes use of an optimal solution of a prior problem instance to find a new solution faster and more accurately. Although the method is generally NP‐hard as well, the approximation bound has been shown in the literature to be tighter than a full optimization for several traveling salesman problem variations. To date, however, there have not been any computational studies conducted with the method for scenario analysis with generalized vehicle routing problems, nor has there been any metaheuristics designed with reoptimization in mind. A generalized, selective household activity routing problem (G‐SHARP) is presented as an extension of the HAPP model to include both destination and schedule choice for the purpose of testing reoptimization. Two reoptimization algorithms are proposed: a simple swap heuristic and a new class of evolutionary algorithms designed for reoptimization, dubbed a Genetic Algorithm with Mitochondrial Eve (GAME). The two algorithms are tested against a standard genetic algorithm in a computational experiment involving 100 zones that include 400 potential activities (resulting in a total of 802 nodes per single‐traveler household). Five hundred households are synthesized and computationally tested with a base scenario, a scenario where an office land use in one zone is dezoned, and a scenario where a freeway is added onto the physical network. The results demonstrate the effectiveness of reoptimization heuristics, particularly GAME, and the capability of G‐SHARP to capture reallocations of activities and schedules with respect to spatiotemporal changes.

Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem

A parameter estimation method is proposed for calibrating the household activity pattern problem so that it can be used as a disaggregate, activity-based analog of the traffic assignment problem for activity-based travel forecasting. Inverse optimization is proposed for estimating parameters of the household activity pattern problem such that the observed behavior is optimal, the patterns can be replicated, and the distribution of the parameters is consistent. In order to fit the model to both the sequencing of activities and the arrival times to those activities, an inverse problem is formulated as a mixed integer linear programming problem such that coefficients of the objectives are jointly estimated along with the goal arrival times to the activities. The formulation is designed to be structurally similar to the equivalent problems defined by Ahuja and Orlin and can be solved exactly with a cutting plane algorithm. The concept of a unique invariant common prior is used to regularize the estimation method, and proven to converge using the Method of Successive Averages. The inverse model is tested on sample households from the 2001 California Household Travel Survey and results indicate a significant improvement over the standard inverse problem in the literature as well as baseline prescriptive models that do not make use of sample data for calibration. Although, not unexpectedly, the estimated optimization model by itself is a relatively poor forecasting model, it may be used in determining responses of a population to spatio-temporal scenarios where revealed preference data is absent.

A mathematical programming formulation of the household activity rescheduling problem

The so-called activity-based approach to analysis of human interaction with the social and physical environments dates back to the original time–space geography works of Hägerstrand and his colleagues at the Lund School. Despite their obvious theoretical attractiveness, activity-based approaches to understanding and predicting travel behavior have suffered from the absence of an analytical framework that unifies the complex interactions among the resource allocation decisions made by households in conducting their daily affairs outside the home, while preserving the utility-maximizing principles presumed to guide such decisions. In this paper, we develop a computationally tractable system, based on an extension and modification of some rather well-known network-based formulations in operations research, to model human dynamics in uncertain environments. The research builds on the mixed integer linear program formulation of the Household Activity Pattern Problem (HAPP) by embedding in the household activity schedule decision process a means of capturing uncertainty by introducing the dynamics of rescheduling.

The household activity pattern problem: General formulation and solution

The household activity pattern problem of analyzing/predicting the optimal path of household members through time and space as they complete a prescribed agenda of out-of-home activities is posed as a variant of the pickup and delivery problem with time windows. The most general case of the model includes provision for vehicle transfer, selective activity participation, and ridesharing options. A series of examples are solved using generic algorithms. The model is purported to remove existing barriers to the operationalization of activity-based approaches in travel behavior analysis.