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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Augmenting Transit Trip Characterization and Travel Behavior Comprehension: Multiday Location-Stamped Smart Card Transactions</title>
      <link>https://trid.trb.org/View/910384</link>
      <description><![CDATA[Trips need to be described and have always been characterized by various levels of abstraction. It varies from a simple label such as home-based work to complete itinerary with sociodemographic characteristics of the trip maker and household. The rationale behind such classifications is that planners and modelers recognize that the demand of transportation is highly differentiated. It is hoped that additional attributes would provide a more complete portrait of the demand and an improved understanding of the underlying travel behavior. Passive data collection technologies bring an extra dimension to travel data acquisition. Multiday data, which are difficult to collect, become accessible. In public transit, a smart card automatic fare collection system with automatic vehicle location capability provides high-resolution longitudinal data on travel pattern but also suffers from the inherent limitations of passive methods. This paper proposes a methodology to enhance transit trip characterization by adding a multiday dimension to a month of smart card transactions. On the basis of an individual, anchor points—precise to an exact address—are detected. Boarding and alighting locations are described with respect to those anchors. The enhancement allows in-depth travel behavior analysis on a subgroup sharing a common anchor or an individual. The paper demonstrates the use of spatial statistics, spatial analyses with geographic information system, visualizations, and data mining to describe activity space and locations and departure time dynamics, and to derive monthly trip table, activity schedule, and behavioral rules for cardholders. The results offer promising insights to transit planning and the understanding of travel behavior.]]></description>
      <pubDate>Wed, 21 Apr 2010 08:09:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/910384</guid>
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    <item>
      <title>The value of reliability</title>
      <link>https://trid.trb.org/View/906880</link>
      <description><![CDATA[The authors derive the value of reliability in the scheduling of an activity of random duration, such as travel under congested conditions. Using a simple formulation of scheduling utility, the authors show that the maximal expected utility is linear in the mean and standard deviation of trip duration, regardless of the form of the standardised distribution of trip durations. This insight provides a unification of the scheduling model and models that include the standard deviation of trip duration directly as an argument in the cost or utility function. The results generalise approximately to the case where the mean and standard deviation of trip duration depend on the starting time. An empirical illustration is provided.]]></description>
      <pubDate>Tue, 22 Dec 2009 08:59:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/906880</guid>
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    <item>
      <title>Adapts: Agent-Based Dynamic Activity Planning and Travel Scheduling Model--A Framework</title>
      <link>https://trid.trb.org/View/881518</link>
      <description><![CDATA[This paper describes a new framework for dynamically simulating activity planning and scheduling within an activity-based microsimulation model.  The dynamic activity planning framework is a process model which attempts to replicate time-dependent activity scheduling.  By modeling the actual underlying activity and travel planning and scheduling processes rather than revealed activity-travel patterns, the model can represent a much wider range of travel demand management policies, especially policies which are expected to impact the planning process of individuals.  In contrast with previous activity scheduling models, the proposed model considers activity scheduling steps as discrete events within the overall activity-travel simulation, and furthermore considers each attribute decision as a separate event.  The paper develops a framework for modeling each activity and its attributes, and allows for a non-fixed attribute planning order, so that there is no pre-determined planning order assumed in the model.  Various stages of the model that are implemented in an overall simulation framework are discussed.  In addition, some initial data results from a pilot test of a new GPS-based prompted recall survey used to capture the underlying activity attribute planning process are presented and discussed in the context of the overall model framework.  The initial data tend to support the hypothesis that significant variation exists in the manner in which activities are actually planned.]]></description>
      <pubDate>Tue, 19 May 2009 07:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/881518</guid>
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    <item>
      <title>Transferability: Creating Representative Activity Schedules Using 2001 NHTS</title>
      <link>https://trid.trb.org/View/882291</link>
      <description><![CDATA[To generate representative travel patterns quickly for a city’s or a region’s population for use in homeland security modeling, Los Alamos National Laboratory (LANL) has developed a methodology for generating representative activity schedules that uses a nationwide travel survey, the 2001 National Household Travel Survey (NHTS). Previously, in order to reasonably model cities, activity-based transportation models required as input an activity/travel survey representing travel in the local area of interest. The validity of this approach was evaluated using by using both the NHTS and the 2000 Twin Cities Metropolitan Area Travel Behavior Inventory (TBI) to separately model the activities of a synthetic population for the Minneapolis-St. Paul, MN (Twin Cities) metropolitan area and comparing the results. Prior to assessing the new approach of generating local activities using a national data set, a basic validation was completed to determine how successful LANL’s TRansportation ANalysis and SIMulation System (TRANSIMS-LANL) was at producing activities for a synthetic population based on a local travel survey. For this purpose, the TBI was utilized to generate activity schedules for a synthetic population. These schedules were compared directly to the TBI itself. An analysis showed that TRANSIMS-LANL was able to produce activities that closely reflect the original survey. In general, the generated total trip and activity counts varied from the survey by approximately one percent. Using the national survey to generate activities, TRANSIMS-LANL underestimated the total number of trips and activities by only eight percent and six percent, respectively.]]></description>
      <pubDate>Tue, 28 Apr 2009 08:10:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/882291</guid>
    </item>
    <item>
      <title>Vehicle Capacity and Fuel Consumption in Household Fleets: Constraint-Based Microsimulation Model</title>
      <link>https://trid.trb.org/View/882368</link>
      <description><![CDATA[Vehicle capability is one of the factors that constrain the set of transportation options available for personal transportation. The capacity for carrying passengers and cargo is of particular interest when energy consumption is considered, because the use of more efficient vehicles may be limited for trips with higher load requirements. This paper presents a method for considering trip capacity requirements when the available vehicles are assigned to the trips on a household activity schedule. A constraint-based vehicle assignment model that uses the trip data from the 2001 National Household Travel Survey as an example is introduced. Initial results from the analysis of these data show that by optimally assigning existing vehicles to trips, the average value of potential fuel savings ranges from 5% to 23%, depending on the size and vehicle type composition of the household fleet. Households with more vehicles in the fleet and a more diverse range of vehicles to choose from are able to achieve greater fuel savings than those with more homogeneous fleets. Considering the extent to which household vehicle assignment decisions were already consistent with the minimization of fuel consumption, in 2001 actual household vehicle assignment was, on average, only slightly better than random.]]></description>
      <pubDate>Tue, 28 Apr 2009 08:10:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/882368</guid>
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    <item>
      <title>Instrumental Motives for Private Car Use</title>
      <link>https://trid.trb.org/View/815356</link>
      <description><![CDATA[This chapter focuses on instrumental motives for car use. These motives are related to the perception of the car as a fast, convenient, and affordable tool for urban travel. It is in general viewed as superior to alternative modes for reaching destinations where everday activities such as work, maintenance, and leisure can be carried out. An analysis of the role of instrumental motives to other internal and external motivating factors is presented. That instrumental motives may counteract car use reduction is emphasized.]]></description>
      <pubDate>Fri, 21 Sep 2007 13:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/815356</guid>
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    <item>
      <title>Human Interaction Spaces Under Uncertainty</title>
      <link>https://trid.trb.org/View/800940</link>
      <description><![CDATA[Rigid activity schedules and pressure of time can strongly influence the determination of a meeting place and time. When several uncertain determinants need to be taken into account, joint activity planning becomes a complex issue. In this respect, a cross-pollination of Hägerstrand’s time geography and Pawlak’s rough set theory yields a fruitful foundation for analyzing multiple agents’ travel spaces under uncertainty. This paper reports on an attempt to analyze the effects of uncertain spatiotemporal settings on the determination of interaction spaces. The aim is to provide a better understanding of the uncertainty component of constraints to support agents pointing out a feasible meeting place while respecting individuals’ fixed activity programs. The concept of rough space–time prisms and their effect on current space–time accessibility measures is presented. The paper focuses on three types of uncertainty: temporal, spatial, and speed.]]></description>
      <pubDate>Wed, 02 May 2007 13:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/800940</guid>
    </item>
    <item>
      <title>Traveler Behavior and Values 2005</title>
      <link>https://trid.trb.org/View/776133</link>
      <description><![CDATA[This Transportation Research Record contains 29 papers on the subject of traveler behavior and values.  Topics considered include vehicle ownership, activity schedules, discrete choice models, the transit assignment problem, mode choice decisions, driver behavior, activity patterns, value of travel information, trip time reliability, route choice patterns, travel behavior modeling, vehicle miles traveled, walking, teleshopping, telecommunications, shopping trips, and information and communications technology.]]></description>
      <pubDate>Mon, 06 Mar 2006 14:01:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/776133</guid>
    </item>
    <item>
      <title>SPACE-TIME USER BENEFIT AND UTILITY ACCESSIBILITY MEASURES FOR INDIVIDUAL ACTIVITY SCHEDULES</title>
      <link>https://trid.trb.org/View/683905</link>
      <description><![CDATA[Accessibility is a fundamental concept in human existence, which goes to the heart of the notion of society, equity, and justice. However, despite the importance of the concept, the mathematical measures that have historically been used to quantify accessibility levels have been relatively poorly defined and have encompassed a limited range of observed forms of travel behavior.  Existing space-time locational benefit measures are extended to encapsulate more realistic temporal constraints on activity participation and the associated perceived user benefit.  The development of a family of space-time route benefit measures is outlined.  Despite their apparent theoretical attractiveness, hitherto researchers have not used such measures.  It is demonstrated how these route benefit measures can be used to develop an associated family of disaggregate activity-based space-time utility accessibility measures applicable to individual activity schedules and how income constraints can be incorporated within the space-time utility accessibility measures.  Finally, the means by which stochastic frontier models can be used in conjunction with existing travel-activity diary data sets to operationalize the proposed measure of accessibility are briefly described.]]></description>
      <pubDate>Mon, 29 Dec 2003 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/683905</guid>
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