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Activity Pattern

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Hjp Harry Timmermans – One of the best experts on this subject based on the ideXlab platform.

  • Activity Pattern similarity : a multidimensional sequence alignment method
    Transportation Research Part B: Methodological, 2002
    Co-Authors: C H Chang-hyeon Joh, Ta Theo Arentze, F Frank Hofman, Hjp Harry Timmermans

    Abstract:

    The classification of Activity Patterns is an important research topic in Activity analysis. First, it constitutes the basis for analyzing Activity Patterns, for instance by correlating the derived classification with spatial and/or socio-economic variables. Secondly, the underlying mechanisms can be used to assess the degree of correspondence between observed Activity Patterns and Activity Patterns predicted by some Activity-based model of transport demand. Traditionally, conventional Euclidean distance measures have been used for the comparison of Activity Patterns. Consequently, the sequence information embedded in Activity Patterns has not been explicitly considered when comparing Activity Patterns. More recently, sequence alignment methods have been proposed. Although these methods have some advantages, they are uni-dimensional and hence cannot incorporate the interdependencies between attributes. This paper therefore proposes a multidimensional sequence alignment method to measure differences in both sequential and interdependency information embedded in Activity Patterns.

  • albatross multiagent rule based model of Activity Pattern decisions
    Transportation Research Record, 2000
    Co-Authors: T Theo A Arentze, Frank F Hofman, Henk Van Mourik, Hjp Harry Timmermans

    Abstract:

    The development of ALBATROSS: A Learning-Based, Transportation-Oriented Simulation System is summarized. This Activity-based model of Activity-travel behavior is derived from theories of choice heuristics that consumers apply when making decisions in complex environments. The model, one of the most comprehensive of its kind, predicts which activities are conducted when, where, for how long, and with whom, and the transport mode involved. In addition, various logical, temporal, spatial, spatial-temporal, and institutional constraints are incorporated in the model. The conceptual underpinnings of the model, its architecture, the functionality of its key agents, data collection, and model performance are discussed.

Will Recker – One of the best experts on this subject based on the ideXlab platform.

  • Mining Activity Pattern trajectories and allocating activities in the network
    Transportation, 2015
    Co-Authors: Mahdieh Allahviranloo, Will Recker

    Abstract:

    GPS enabled devices, generating high-resolution spatial–temporal data, are opening new lines of possibilities for transportation applications in both planning and research. Mining these rich and large datasets to infer people’s travel behavior, the Activity Patterns resulting from their behavior, and allocating activities in the network is the focus of this paper. Here we introduce a methodology that relies only on geocoded location data and socioeconomic characteristics to infer types of activities in which individuals engage at different locations in the network. Depending on the duration of the stop, arrival time and geographic distance to home location and previous activities, the type of Activity is inferred at the census tract level using adaptive boosting algorithm. Then, using a model based on Markov chains with conditional random field to capture dependency between Activity sequencing and individuals’ socioeconomic attributes, the spatial–temporal trajectory of Activity/travel engagement is generated. The model is trained on data obtained from the California Household Travel Survey data 2000–2001 and subsequently applied to an out-of sample test set to validate the accuracy and performance.

  • the location selection problem for the household Activity Pattern problem
    Transportation Research Part B-methodological, 2013
    Co-Authors: Jee Eun Kang, Will Recker

    Abstract:

    Abstract 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.

  • inverse optimization with endogenous arrival time constraints to calibrate the household Activity Pattern problem
    Transportation Research Part B-methodological, 2012
    Co-Authors: Joseph Y J Chow, Will Recker

    Abstract:

    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.

Moshe Benakiva – One of the best experts on this subject based on the ideXlab platform.

  • capturing well being in Activity Pattern models within Activity based travel demand models
    , 2013
    Co-Authors: Moshe Benakiva, Maya Abouzeid

    Abstract:

    The Activity-based approach which is based on the premise that the demand for travel is derived from the demand for activities, currently constitutes the state of the art in metropolitan travel demand forecasting and particularly in a form known as the day schedule approach. This approach first models the day Activity Pattern of an individual (number of activities and tours by type), and then models the travel dimensions including destination, mode, and time-of-travel given an Activity Pattern. Several modeling developments have been incorporated into these models over the last decade or so. Yet, the specification of the Activity Pattern model in operational Activity-based model systems is not founded in a behavioral theory, but rather combines in ad-hoc ways a number of socio-economic, demographic, lifestyle, and accessibility variables based on empirical considerations. The authors postulate that activities are planned and undertaken to satisfy needs so as to maintain or enhance subjective well-being, and extend Activity Pattern models in this direction. The authors develop two extensions to enhance the specification of the Activity Pattern model. The first extension maintains the standard Activity Pattern utility specification but adds information about the utility using well-being measures in addition to the usual choice indicators. It is expected that the Activity Pattern models that incorporate well-being would be behaviorally more realistic and would enhance the efficiency of the Activity Pattern models thereby yielding better prediction of travel Patterns. The second extension explicitly models the drivers of Activity participation, based on the notion that individuals pursue different activities to satisfy their needs (sustenance, social, recreation, etc.). Each Activity that an individual conducts may satisfy one or several of his/her needs. Conversely, each need may be satisfied by one or several activities. The authors model an individual’s choice of Activity dimensions including frequency, sequence, location, mode, time-of-travel, etc. as one that maximizes his/her need-satisfaction.

  • Activity based disaggregate travel demand model system with Activity schedules
    Transportation Research Part A-policy and Practice, 2001
    Co-Authors: John L. Bowman, Moshe Benakiva

    Abstract:

    We present an integrated Activity-based discrete choice model system of an individual’s Activity and travel schedule, for forecasting urban passenger travel demand. A prototype demonstrates the system concept using a 1991 Boston travel survey and transportation system level of service data. The model system represents a person’s choice of activities and associated travel as an Activity Pattern overarching a set of tours. A tour is defined as the travel from home to one or more Activity locations and back home again. The Activity Pattern consists of important decisions that provide overall structure for the day’s activities and travel. In the prototype the Activity Pattern includes (a) the primary – most important – Activity of the day, with one alternative being to remain at home for all the day’s activities; (b) the type of tour for the primary Activity, including the number, purpose and sequence of Activity stops; and (c) the number and purpose of secondary – additional – tours. Tour models include the choice of time of day, destination and mode of travel, and are conditioned by the choice of Activity Pattern. The choice of Activity Pattern is influenced by the expected maximum utility derived from the available tour alternatives.