<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>Characteristics of the Auto Users and Non-Users of Central Texas Toll Roads</title>
      <link>https://trid.trb.org/View/1428890</link>
      <description><![CDATA[As toll road usage increases to finance new road infrastructure or add capacity to existing road infrastructure, the question of who does and does not use toll roads becomes increasingly important to toll road developers, financiers, Traffic and Revenue consultants, and investors, among others. Although a number of previous studies have attempted to characterize toll road users and sub-sets of toll road users, this study presents a first attempt to differentiate the auto users and non-users of the Central Texas toll roads. Respondents (1,507) to a telephone survey that was conducted in the Spring of 2008 were categorized as users and non-users of toll roads and statistical analysis was conducted to provide insight into the demographic and trip characteristics of the auto users and non-users of the Central Texas toll roads. The report also includes a detailed analysis of actual transaction data from the Central Texas Turnpike System. These actual data coupled with the preferences expressed in the surveys provides a detailed look into the characteristics of the auto users and non-users of the Central Texas area toll roads.]]></description>
      <pubDate>Wed, 09 Nov 2016 13:24:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1428890</guid>
    </item>
    <item>
      <title>Develop Multi-Scale Energy and Emission Models</title>
      <link>https://trid.trb.org/View/1363413</link>
      <description><![CDATA[The proposed project develops a tool for the assessment of short-term and medium-term effects of network-level traffic-flow improvement projects on energy consumption and environment. Current state-of-the-art models estimate vehicle fuel consumption and emissions based on simple vehicle trip characteristics. While this approach has been widely utilized by transportation planners/engineers for the evaluation of network-wide impacts on energy consumption and environment, it is not efficient for the evaluation of energy and environmental impacts of short-term and medium-term effects of network-level traffic-flow improvement projects, including Intelligent Transportation System (ITS) applications. This tool can be utilized to evaluate the energy and environmental impacts of alternative transportation-related projects prior to their implementation in the field and possibly reduce the adverse impacts of transportation projects on energy consumption and environment.]]></description>
      <pubDate>Thu, 30 Jul 2015 01:01:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1363413</guid>
    </item>
    <item>
      <title>Route Choice Modeling Using GPS-Based Travel Surveys</title>
      <link>https://trid.trb.org/View/1289886</link>
      <description><![CDATA[The extensive use of GPS-based travel surveys in the past few years now allows vehicle movements to be traced and, thus, data on the actual routes chosen for various trips to be collected. However, efforts on the empirical modeling of route choices through the use of GPS traces are still limited. In this context, the broad focus of this research was to combine data from a large-scale GPS-based travel survey and geographic information system-based roadway network databases to develop models for route choice. Data from GPS streams for 1,913 trips were used in this analysis. Three models that considered choice set sizes of five, 10, and 15 alternatives were built. The estimation results indicated statistically significant and intuitively reasonable effects of free-flow travel time, left turns, right turns, intersections, and circuity on the attractiveness of different route alternatives. Furthermore, the sensitivity to these factors was found to vary on the basis of trip (purpose, time of day, and day of the week) and traveler (gender, age, and length of stay at the current home) characteristics.]]></description>
      <pubDate>Mon, 24 Mar 2014 14:58:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1289886</guid>
    </item>
    <item>
      <title>Reaction to Value Pricing by Different Suburban Populations</title>
      <link>https://trid.trb.org/View/1091261</link>
      <description><![CDATA[Value pricing strategies are beginning to be considered for future improvements in suburban areas that currently do not experience significant congestion but are expected to become congested in the future. This is a significant departure from implementing these strategies in congested urban areas, as is commonly done. Therefore, traveler reaction in these suburban areas is unknown. To plan and design value pricing projects most effectively, requires an understanding of the potential reaction of suburban travelers to value pricing. Responses to a survey of travelers using the eastern and western segments of Interstate 10 (I-10) outside San Antonio, Texas, were used to study differences in response to value pricing by suburban population groups. These surveys collected information on travelers’ socioeconomic and trip characteristics, as well as their attitudes toward implementation of value pricing. Overall, the majority of travelers on I-10E and I-10W did not favor the implementation of value pricing for the expansion of these corridors. However, I-10W travelers seemed to be more willing to pay for travel time savings, probably because travelers on I-10W had higher average household incomes, were more likely to use I-10W on a regular basis for commuting, and were more often exposed to some traffic congestion.]]></description>
      <pubDate>Wed, 18 May 2011 11:21:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1091261</guid>
    </item>
    <item>
      <title>Integrating Trip and Roadway Characteristics to Manage Safety in Traffic Analysis Zones</title>
      <link>https://trid.trb.org/View/1091240</link>
      <description><![CDATA[A transportation network is a conglomeration of road–traffic–environment modules and features multicategories of interdependent factors. This mix makes the management of safety in traffic analysis zones (TAZs) explicitly challenging. This study investigated the association between crash frequencies and various types of trip productions and attractions in combination with the road characteristics of 1,349 TAZs of four counties in the state of Florida. Crash safety management of these TAZs is emphasized through prioritizing them by examining the effects of trip and roadway factors on the aggregated crash frequencies. Models were developed separately for total crashes, severe crashes (fatal and severe injury crashes), total crashes during peak hours, and pedestrian- and bicycle-related crashes on the basis of various groups of estimators. It was found that the total crash model and the peak-hour crash model were best estimated by total trip productions and total trip attractions. The severe crash model was best fit by trip-related variables only, and the pedestrian- and bicycle-related crash model was best fit by road-related variables only. The results from this study pave the way for better safety management and the incorporation of safety measures in travel and network planning.]]></description>
      <pubDate>Thu, 28 Apr 2011 07:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1091240</guid>
    </item>
    <item>
      <title>Proposed Methodology for Estimating Rideshare Viability Within an Organization: Application to the MIT Community</title>
      <link>https://trid.trb.org/View/1092547</link>
      <description><![CDATA[Ridesharing as a mode of travel is a potential solution to a variety of the transportation sectors toughest challenges including congestion relief, increased energy security, reduced GHG emissions and improved travel options. However, the transportation literature provides little quantified assessment of ridesharing’s overall potential. This paper proposes a data driven methodology for estimating the viability of ridesharing at an organizational scale, and seeks to demonstrate its applicability using the Massachusetts Institute of Technology commuting population as a case. The methodology seeks to improve upon previous research by differentiating between modeled rideshare potential based on known trip characteristics, and observed rideshare behavior, within the same commuting population. By comparing rideshare potential to observed behavior,inferences can be made about the relative importance of trip characteristics vs. the importance of human attitudes in rideshare arrangements. MIT-specific results suggest that between 50% and 77% of the commuting population could rideshare on a maximum-effort day. These are values significantly higher than the 8% of the MIT community that currently choose to rideshare. Maximum achievable VMT reductions from daily ridesharing are between 9% and 27%. The disparity between the modeled potential and observed behavior suggests that human attitudes are a much larger barrier to increased rideshare participation than incompatible trip characteristics. The results suggest that policy makers seeking to increase rideshare participation may want to target large organizations and focus their efforts on personalized travel planning in an effort to improve attitudes towards ridesharing.]]></description>
      <pubDate>Thu, 21 Apr 2011 13:09:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1092547</guid>
    </item>
    <item>
      <title>Study of Urban Resident Travel Mode Choice Behavior</title>
      <link>https://trid.trb.org/View/1091147</link>
      <description><![CDATA[Based on the theory of Multinomial Logit Model (ML) and its modeling method, this paper analyzes the factors that influence the travel mode choice of residents, referring to the urban resident trip investigation data of Jinan in 2009, and develops an urban resident travel mode choice model. The results show that there is a stable relationship between travel mode choice and personal characteristics, family characteristics, and trip characteristics. Through the guidance and adjustment of controllable factors, which can affect resident travel mode choice, the purpose of optimizing and adjusting the urban travel mode structure can be achieved.]]></description>
      <pubDate>Fri, 18 Feb 2011 08:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1091147</guid>
    </item>
    <item>
      <title>Information Systems, Geographic Information Systems, and Advanced Computing 2010</title>
      <link>https://trid.trb.org/View/1086339</link>
      <description><![CDATA[This issue contains 14 papers on the subject of information systems, geographic information systems, and advanced computing.  Specific topics discussed include the following:  origin-destination matrix generation; a household travel data simulation tool; an urban travel route and activity choice survey; augmenting transit trip characterization and travel behavior comprehension; improving data quality, accuracy, and response in on-board surveys; assessing the quality of origin-destination matrices derived from activity travel surveys; semiautomatic imputation of activity travel diaries; a Global Positioning System Web-based prompted recall solution for longitudinal travel surveys; modeling network impact in the area surrounding an activity center caused by special events; information infrastructure for research collaboration; incorporating scenic view, slope, and crime rate into route choices; multimodal accessibility of modern roundabouts; fuzzy rule-based system approach to combining traffic count forecasts; and planning the implementation of three-dimensional technologies for design and construction.]]></description>
      <pubDate>Thu, 13 Jan 2011 16:20:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1086339</guid>
    </item>
    <item>
      <title>Could you also have made this trip by another mode? An investigation of perceived travel possibilities of car and train travellers on the main travel corridors to the city of Amsterdam, The Netherlands</title>
      <link>https://trid.trb.org/View/887467</link>
      <description><![CDATA[The authors investigated perceived travel possibilities (or subjective choice-sets, consideration-sets) of car and train travellers on the main corridors to the city of Amsterdam, The Netherlands, and associations with traveller and trip characteristics. The authors conducted secondary analysis on a survey sample consisting of 7,950 train and 19,232 car travellers. Forty-five percent of train travellers had a car in their objective choice-set, 27% of them would however never use it for this trip. Trip destination city centre, trip purpose, paying for the trip, public transport commitment, traffic congestion and parking problems were associated with consideration of car as alternative. Forty-two percent of car travellers had public transport in their subjective choice-set. The ratio between perceived public transport and objective car travel time stood out as determinant of consideration-sets, next to destination city centre, trip purpose, travel time and private versus company car ownership. On average, car travellers' perceptions of public transport travel time exceeded objective values by 46%. The authors  estimated that if perceptions would be more accurate, two out of three car travellers that currently do not see public transport as an alternative would include it in their choice-set, and use it from time to time. This effect has strong theoretical and policy implications.]]></description>
      <pubDate>Wed, 22 Apr 2009 07:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/887467</guid>
    </item>
    <item>
      <title>Air Passenger Preferences for Choice of Airport and Ground Access Mode in the New York City Metropolitan Region</title>
      <link>https://trid.trb.org/View/847966</link>
      <description><![CDATA[In current practice, regional models are limited in their capability to analyze policies involving changes and improvements to airports (and their services) and ground access transportation. Typically, airports are treated only as employment centers or as special generators. Important and distinct features of air passenger travel affecting trip distribution and mode choice are rarely modeled explicitly. This paper presents the development of a joint airport and ground access mode choice model for the New York City metropolitan region based on an extensive survey of airport users. Unlike travel to and from most U.S. cities, air passengers flying to and from the New York region face a nontrivial choice of airports and ground access modes (including premium transit options). A nested logit model was formulated with airport choice at the upper level and ground access mode choice at the second level; however, a multinomial logit model was found to be statistically preferable. Results indicate that air passenger travel behavior is significantly different for business and nonbusiness travelers. Overall, willingness to pay for trips to and from the airport is much higher than for regular intracity trips. Average yield, access time, and access cost are the most important determinants of air passenger’s choice; demographics and trip characteristics are also significant. The developed tool was used for a comprehensive study of airport development alternatives in the New York region and is seen as the platform for additional data development and model extensions for future studies of air passenger service planning in the New York megaregion.]]></description>
      <pubDate>Fri, 21 Mar 2008 08:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/847966</guid>
    </item>
    <item>
      <title>Application of Dynamic Value Pricing Through Enhancements to TRANSIMS</title>
      <link>https://trid.trb.org/View/841604</link>
      <description><![CDATA[In the past decade, transportation agencies have become increasingly interested in a high-occupancy toll (HOT) lane value pricing system, especially one with a dynamically varying toll system that depends on the congestion levels in the HOT lanes. HOT lane users are influenced by many factors, including toll prices, savings in time, and purpose of the trip. The current methodologies that forecast the demand for a HOT lane value pricing system are aggregate and do not consider individual traveler socioeconomic characteristics, particularly the value of time (VOT). This paper applies heterogeneous VOT for each individual into the generalized travel time function. This approach relaxes the conventional assumption of constant VOT. The generalized travel time function combined with VOT is used as a route choice for each traveler between the two choices (a toll lane versus a nontoll lane) in a HOT lane value pricing system. With this function, each driver is assigned to the network on the basis of a dynamic traffic assignment. This approach is implemented by using TRANSIMS on a large transportation network. TRANSIMS has a microscopic simulation model that enables the user to capture route choice behavior, including each traveler’s response to dynamic tolls through heterogeneous VOT. To initiate this capability, TRANSIMS was enhanced to support dynamic toll pricing. It is now capable of presenting the 15-min dynamic toll rates. In addition, the impacts of various tolls on route choice can be analyzed on the basis of socioeconomic and trip characteristics of each traveler.]]></description>
      <pubDate>Thu, 06 Dec 2007 09:40:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/841604</guid>
    </item>
    <item>
      <title>Modeling Demographic and Unobserved Heterogeneity in Air Passengers’ Sensitivity to Service Attributes in Itinerary Choice</title>
      <link>https://trid.trb.org/View/776454</link>
      <description><![CDATA[Modeling passengers’ flight choice behavior is valuable to understanding the increasingly competitive airline market and predicting air travel demand. Standard and mixed-multinomial logit models of itinerary choice for business travel are estimated on the basis of a stated preference survey conducted in 2001. The results suggest that observed demographic- and trip-related differences are incorrectly manifested as unobserved heterogeneity in a random-coefficient mixed logit model that ignores the demographic- and trip-related characteristics of travelers. Among demographics, gender and income level have the most noticeable effects on sensitivity to service attributes in itinerary choice behavior, but membership in a frequent flyer program, employment status, travel frequency, and group travel also emerge as important determinants. However, residual heterogeneity is significant because of unobserved factors, even after accommodating sensitivity variations due to demographic- and trip-related factors. Consequently, substitution rates for each service attribute show substantial variations in the willingness to pay among observationally identical business passengers.]]></description>
      <pubDate>Wed, 31 May 2006 07:54:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/776454</guid>
    </item>
    <item>
      <title>Trip Characteristics of Vehicle Crashes Involving Child Passengers</title>
      <link>https://trid.trb.org/View/759642</link>
      <description><![CDATA[Motor vehicle crashes are a leading cause of mortality and morbidity for children.  This study has three aims:  (1) to describe characteristics of motor vehicle crashes involving children with respect to various child, driver, crash and trip characteristics; (2) to examine whether the likelihood of children under age 13 years traveling in the front seat of the vehicle varied with trip characteristics or situational factors; and (3) to examine whether the likelihood of inappropriate restraint varied by trip characteristics or situational factors for children under age 9 years.  A cross sectional study was conducted on children under 16 years old in crashes of insured vehicles in 15 states, with data collected using insurance claims records and a telephone interview.  A descriptive analysis of the characteristics of vehicle crashes involving children was performed.  Multivariate Poisson regression was used to identify situational factors associated with inappropriate restraint or front row seating.  Results suggest that children were traveling in vehicles involved in crashes that occurred under usual driving circumstances--closer to home, on a local road, during normal daytime hours and within areas with relatively lower posted speed limits.  Compared with children involved in morning crashes, those in daytime crashes or in nighttime crashes were more likely to be sitting in the front seat.  Children involved in nighttime crashes were more likely to be inappropriately restrained than those in daytime crashes.  Children riding with two or more additional passengers were more likely to be inappropriately restrained than those with no other passengers.  These findings indicate that some situational characteristics of trips were associated with inappropriate restraint and front row seating behaviors for young children.  Given the potential crash risk of ordinary trips, educational efforts should emphasize the importance of following recommendations for optimal safety for children in vehicles on every trip.]]></description>
      <pubDate>Mon, 29 Aug 2005 07:47:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/759642</guid>
    </item>
    <item>
      <title>SIMULATING HOUSEHOLD TRAVEL SURVEY DATA IN METROPOLITAN AREAS</title>
      <link>https://trid.trb.org/View/680786</link>
      <description><![CDATA[Census data provide a rich range of socioeconomic characteristics from which it is shown that trip characteristics can be simulated.  This report summarizes research into the simulation of the trips and trip characteristics for a random sample of households drawn from census data.  The simulation source is the 1990 Public Use Micro-data Sample (PUMS) data from the 1990 Decennial Census of the United States.  A set of categories is defined for the simulation that allows the development of significantly different statistical distributions of trip characteristics, using the 1995 Nationwide Personal Transportation Survey (NPTS) data.  Based on the census data, samples of households are obtained and their trip characteristics in terms of number of trips by purpose, mode, time of departure, and trip length are simulated, using a Monte Carlo type of simulation procedure.  This is performed for three regions:  Baton Rouge, Louisiana; Dallas-Fort Worth, Texas; and Salt Lake City, Utah.  While there are found to be a number of statistically significant differences in the various trip characteristics between the simulation data and actual household travel surveys conducted in 1997 in Baton Rouge, 1996 in Dallas-Fort Worth, and 1993 in Salt Lake City, the numeric differences in many of the characteristics are actually quite small.  It is found that the simulation, as currently defined, does not capture trip-length variations that may be attributable to city size, nor does it do as well as might be hoped in capturing effects resulting from differences in household size between cities such as Dallas and Salt Lake City.  Further refinement of the simulation procedure appears to be warranted. In the case of Baton Rouge, comparisons are made on the trip rates by purpose with the existing trip generation models (which were borrowed in 1991 for the Baton Rouge area), with national default figures, and with new trip-generation models developed from the 1997 data.  The simulation was found to perform much better than the borrowed trip-generation models and the national default figures.  In comparison with new trip-generation models, the simulation was found to perform quite well, although the poorest results were obtained with home-based shopping trips. It is concluded that simulation is a feasible procedure for creating synthetic household travel survey data, using the procedure outlined in this report.  A number of new avenues for research are identified, which should enhance the results further.]]></description>
      <pubDate>Mon, 09 Feb 2004 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/680786</guid>
    </item>
    <item>
      <title>TRUCK COSTING MODEL FOR TRANSPORTATION MANAGERS</title>
      <link>https://trid.trb.org/View/660920</link>
      <description><![CDATA[A software model was developed to estimate truck costs under different equipment configurations, input prices, and gross vehicle weights.  The software was developed to obtain costs for many different configurations and trip characteristics. Important conclusions that can be drawn from running simulations include the sensitivity of costs and equipment use, wait time and trip distance, labor, and fuel price.  The relationships of cost variables and the cost of operations are important for trucking companies and shippers.]]></description>
      <pubDate>Tue, 23 Sep 2003 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/660920</guid>
    </item>
  </channel>
</rss>