<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes</title>
      <link>https://trid.trb.org/View/1602192</link>
      <description><![CDATA[The paper presents an investigation of the temporal transferability of activity scheduling process models and a Meta model of activity scheduling processed by using repeated cross-sectional datasets. Three repeated cross-sectional household travel survey datasets collected in the greater Toronto and Hamilton Area in the years 2001, 2006, and 2011 are used for the investigation. A random utility maximization based dynamic activity scheduling model is utilized to develop activity-travel scheduling models for non-workers and workers separately. Individual year-specific models are compared to identify the temporal stability of the effects of different variables in the model. Results are used to define temporal evolution components in the Meta models. Estimated models are tested for temporal transferability. Different transferability measures are used to test the temporal transferability of cross-sectional year-specific and the Meta models. Results demonstrate an approach of effectively using multiple repeated cross-sectional datasets as pseudo panel data to develop Meta models to improve the temporal transferability of activity scheduling models.]]></description>
      <pubDate>Thu, 30 May 2019 17:22:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1602192</guid>
    </item>
    <item>
      <title>Data-driven activity scheduler for agent-based mobility models</title>
      <link>https://trid.trb.org/View/1576025</link>
      <description><![CDATA[Activity-based modelling is a modern agent-based approach to travel demand modelling, in which the transport demand is derived from the agent’s needs to perform certain activities at specific places and times. The agent’s mobility is considered in a broader context, which allows the activity-based models to produce more realistic trip chains, compared to traditional trip-based models. The core of any activity-based model is an activity scheduler – a software component producing sequences of agent’s daily activities interconnected by trips, called activity schedules. Traditionally, activity schedulers used to rely heavily on hard-coded knowledge of transport behaviour experts. The authors introduce the concept of a Data-Driven Activity Scheduler (DDAS), which replaces numerous expert-designed components and their intricately engineered interactions with a collection of machine learning models. Its architecture is significantly simpler, making it easier to deploy and maintain. This shift towards data-driven, machine learning based approach is possible due to increased availability of mobility-related data. The authors demonstrate DDAS concept using their own proof-of-concept implementation, perform a rigorous analysis and compare the validity of the resulting model to one of the rule-based alternatives using the Validation Framework for Activity-Based Models (VALFRAM).]]></description>
      <pubDate>Fri, 25 Jan 2019 10:34:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1576025</guid>
    </item>
    <item>
      <title>Within- and Between-Days Activity Scheduling by Infinite Trip Chaining</title>
      <link>https://trid.trb.org/View/1287460</link>
      <description><![CDATA[Modeling the activity scheduling problem as a chain of trips in a dynamic discrete choice framework gives a dynamically consistent and microeconomically sound model. However, the possibility to estimate such a model has previously been limited by the large number of states that are needed to represent all possible locations, times and activity combinations for an individual during a day.  To overcome this problem the authors utilize that the state-space shrinks in the end of every day, and introduce some assumptions under which a sequential estimation approach can be used to get consistent estimates. The estimation approach involves: firstly, using sampling of alternative sequences of actions within a day to estimate part of the structural parameters; and secondly, conditioned on these parameters solve the dynamic discrete choice model for the between days choices using a nested fixed point algorithm.  To verify the approach, the authors construct a simple prototype problem from which synthetic data can be simulated. Statistical tests verify that the sequential estimation approach gives consistent and unbiased estimates to all structural parameters, including the between day discount factor.]]></description>
      <pubDate>Wed, 12 Feb 2014 12:32:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1287460</guid>
    </item>
    <item>
      <title>The value of travel time variability with trip chains, flexible scheduling and correlated travel times</title>
      <link>https://trid.trb.org/View/1141249</link>
      <description><![CDATA[This paper extends the analysis of the value of mean travel time (VMTT) and day-to-day travel time variability (VTTV) from single, isolated trips to daily trip chains, considering the effects of flexibility in activity scheduling and within-day correlation of travel times. Using a multi-stage stochastic programming approach, the authors show that the VMTT and VTTV on a trip is conditional on the realized travel times on preceding trips, first through the arrival time to the preceding activity and second through the information provided about subsequent travel times. Analytical formulas for the VMTT and VTTV are obtained for two special cases with piecewise constant and linear marginal cost functions, respectively. With flexible scheduling, there is typically a cost associated with a positive correlation of travel times, arising from persistent deviations from typical travel demand or supply on a given day. However, there is also a strict benefit in the dependence since it allows for a more efficient scheduling of later trips.]]></description>
      <pubDate>Fri, 29 Jun 2012 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1141249</guid>
    </item>
    <item>
      <title>Network User Equilibrium Model for Scheduling Daily Activity Travel Patterns in Congested Networks</title>
      <link>https://trid.trb.org/View/1132710</link>
      <description><![CDATA[Travel demands are derived from the needs and the desires for participating in various activities, such as work, eating, and shopping. An understanding of the interaction between people’s activity and travel choice behavior can serve as a base for long-term transport service planning and policy evaluation purposes. This paper proposes an activity-based network user equilibrium model for dealing with daily activity travel pattern (DATP) scheduling problems in congested networks, in which interdependency between people’s activity and travel choices is comprehensively investigated. A diagonalization solution algorithm is developed to solve the scheduling problems. A remarkable merit of the developed solution algorithm is that it enables an automatic generation of individuals’ DATPs, implying obviation of an explicit enumeration of those patterns that often were required in many previous related studies. A numerical example is provided to illustrate the application of the proposed model and solution algorithm.]]></description>
      <pubDate>Tue, 28 Feb 2012 07:37:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1132710</guid>
    </item>
    <item>
      <title>A latent class accelerated hazard model of social activity duration</title>
      <link>https://trid.trb.org/View/1124171</link>
      <description><![CDATA[Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.]]></description>
      <pubDate>Fri, 16 Dec 2011 14:48:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1124171</guid>
    </item>
    <item>
      <title>Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process</title>
      <link>https://trid.trb.org/View/1124179</link>
      <description><![CDATA[Traditionally, the parking choice/option is considered to be an important factor in only the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.]]></description>
      <pubDate>Fri, 16 Dec 2011 14:48:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1124179</guid>
    </item>
    <item>
      <title>Travel Behavior 2011, Volume 1</title>
      <link>https://trid.trb.org/View/1122810</link>
      <description><![CDATA[This issue contains 15 papers concerned with the following aspects of travel behavior:  the role of social networks in start time and duration of activities; day-to-day choice of bicycle commuting; predicting route choice behavior; continuous activity planning for continuous traffic simulation; large-scale single-day survey samples; parametric hazard functions; travel trends for young Germans and Britons; psychological factors in mode choice models; repetitiveness of daily travel; mode choice for grocery shopping; behavioral analysis of route choice; joint activity-travel scheduling; modeling influences on the use of nonmotorized transport mode; changes in variations of travel time expenditure; and spatial analysis of propensity to escort children to school.]]></description>
      <pubDate>Tue, 22 Nov 2011 16:22:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1122810</guid>
    </item>
    <item>
      <title>Senior Travelers' Trip Chaining Behavior: Survey Results and Data Analysis</title>
      <link>https://trid.trb.org/View/1118541</link>
      <description><![CDATA[The research team conducted a survey of travel and activity scheduling behavior to better understand senior citizens’ trip chaining behavior in the Chicago metropolitan area’s most populous counties. The team used an internet-based, prompted recall activity-travel survey using Global Positioning System (GPS) devices to collect activity-travel diaries and other necessary information. This survey was conducted with 112 people living in 101 households in Northeastern Illinois’ Cook, DuPage, Lake, and Will Counties. Because aging is a growing concern among transportation planners, this survey focused especially on the elderly population, with approximately half of the survey sample consisting of elderly households and the remainder of non-elderly households. Each respondent within these households was asked to carry a portable GPS device ideally for 14 consecutive days and upload the collected data to a website at the end of each day to fill in their activity-travel survey questionnaires. The results suggest that GPS surveys have an improved ability to capture trips that are frequently under-reported; the use of prompted recall provides valuable data about the activity planning and scheduling process itself, which is not found in traditional surveys. Analysis of the decision-making process from the collected data reveals that some aspects of elderly travel behavior are intrinsically distinct from those of the younger population. Results indicate that while age does not affect some aspects of activity-travel behavior, it does affect such aspects as planning horizons, trip flexibility, and trip chaining practices. This study’s results can therefore be used to plan more efficient transit services targeting senior travelers and may help change their attitudes toward public transportation.]]></description>
      <pubDate>Fri, 14 Oct 2011 16:17:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1118541</guid>
    </item>
    <item>
      <title>Activity-based Demand Modelling on a Large Scale: Experience from the New Danish National Model</title>
      <link>https://trid.trb.org/View/1117034</link>
      <description><![CDATA[This paper introduces an activity-based model framework that is being designed for the Danish national transport model. The paper puts emphasis on specific challenges for large-scale implementation in terms of population generation, activity-scheduling and management of trip chain matrices. Conceptually, the model represents separately long-term and short-term decisions and constructs different demand according to duration, geography and day of the trip. Emphasis is given to (1) the population synthesizer and how data consistency is guaranteed; (2) the adaptation of the activity-scheduling approach; and (3) the storage and handling of matrices prior to the assignment model.]]></description>
      <pubDate>Mon, 26 Sep 2011 07:58:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1117034</guid>
    </item>
    <item>
      <title>Travel as a Function of (Life) Projects</title>
      <link>https://trid.trb.org/View/1107767</link>
      <description><![CDATA[Activity-travel behavior is motivated by a variety of human needs, commitments and life styles. Over the last few decades, several approaches were developed with a focus on people’s deeper motivations to travel which exceed usual explanatory determinants such as the belonging to a certain socio-economic group, the spatial dispersion of activity opportunities, generalized costs or income budgets. The list of such approaches involves the identification of ‘mobility styles’, mobility biographies, or social network geographies and their travel implications. This paper has a conceptual character and invites colleagues in the field of travel behavior research to discuss the "travel as a function of projects" approach. First empirical evidence is presented; however, the authors will not provide any econometric modeling of observed data at this stage. The paper provides working hypotheses and a behavioral model. The authors will identify the relationship with other strands of analysis. The authors hope that this work will initiate a broad discussion about the viability of the concept and its operationalization.The authors start the paper with some general notes and hypotheses about the idea of projects and travel. The paper presents an existing general model of motivation, planning and action which the authors amended by aspects of personal mobility. The paper provides results of an initial survey to capture people’s involvement in projects, their planning process and mode choice. The paper  discusses the interactions with a selection of related approaches, directions for future methodological work and implications for transport planning and policy.]]></description>
      <pubDate>Fri, 29 Jul 2011 07:45:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1107767</guid>
    </item>
    <item>
      <title>Generating Comprehensive All-day Schedules: Expanding Activity-based Travel Demand Modelling</title>
      <link>https://trid.trb.org/View/1106253</link>
      <description><![CDATA[Activity-based travel demand micro-simulation generates activity schedules for a certain period of time (e.g. a day) for every member of a population. Travel demand is derived from these activity schedules by the fact that most activities take place at different locations and people need to travel between these. Therefore, understanding people's daily activity schedules is fundamental to understanding and predicting the dynamics of transport. The agent-based micro-simulation toolkit MATSim implements an activity-based approach to travel demand generation for large samples. This paper presents the authors enhancements of the MATSim utility function.]]></description>
      <pubDate>Wed, 20 Jul 2011 07:24:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1106253</guid>
    </item>
    <item>
      <title>Traveler delay costs and value of time with trip chains, flexible activity scheduling and information</title>
      <link>https://trid.trb.org/View/1102626</link>
      <description><![CDATA[The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time on itself as well as other trips, which is important to consider for long-lasting disturbances and when assessing the value of travel information. In this paper the authors extend the single trip approach into a two trips chain and activity scheduling model. Preferences are represented as marginal activity utility functions that take scheduling flexibility into account. The authors analytically derive trip timing optimality conditions, the value of travel time and schedule adjustments in response to travel time increases. The authors show how the single trip models are special cases of the present model and can be generalized to a setting with trip chains and flexible scheduling. The authors investigate numerically how the delay cost depends on the delay duration and its distribution on different trips during the day, the accuracy of delay prediction and travel information, and the scheduling flexibility of work hours. The extension of the model framework to more complex schedules is discussed.]]></description>
      <pubDate>Tue, 21 Jun 2011 12:00:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1102626</guid>
    </item>
    <item>
      <title>Assessment of the Effect of Micro-simulation Error on Key Travel Indices: Evidence from the Activity-Based Model FEATHERS</title>
      <link>https://trid.trb.org/View/1092007</link>
      <description><![CDATA[Current transportation models often do not explicitly address the degree of uncertainty in travel forecasts. Of particular interest in activity-based travel demand models is the model uncertainty that is caused by the statistical distributions of random components, i.e. micro-simulation error. Therefore, the main objective of this paper is to assess the impact of micro-simulation error on two key travel indices, namely the average daily number of trips per person and the average daily distance traveled per person. The effect of micro-simulation error will be investigated by running the activity-based modeling framework FEATHERS 200 times using the same 10% fraction of the population. Results show that micro-simulation errors are limited especially when disaggregation is limited to two levels. Notwithstanding, results indicate that for more elaborate analyses a 10% fraction might not be sufficient. The size of micro-simulation error increases along with complexity. Moreover, more commonly used transport modes such as using the car as driver have a lower error rate. Further research should investigate the impact of the population fraction on the micro-simulation error rates. Besides, one could also investigate other aspects (e.g. the number of activities) involved in the activity-scheduling process.]]></description>
      <pubDate>Wed, 18 May 2011 11:21:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/1092007</guid>
    </item>
    <item>
      <title>A Random Utility Maximization (RUM) Based Dynamic Activity Scheduling Model: Application in Weekend Activity Scheduling</title>
      <link>https://trid.trb.org/View/1091354</link>
      <description><![CDATA[The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers Random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002-2003. The data set classifies activities into nine general categories. For the empirical model of a 24-hour weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.]]></description>
      <pubDate>Wed, 18 May 2011 11:21:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1091354</guid>
    </item>
  </channel>
</rss>