<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>Assessing induced road traffic demand in New Zealand</title>
      <link>https://trid.trb.org/View/2378049</link>
      <description><![CDATA[In an age of concern about climate change it is important to understand the induced traffic to be expected following the building or extension of a road. A review of international literature was undertaken on induced demand, along with an examination of the very limited evidence available in New Zealand. A search was also made for an induced demand tool suitable for use within the early stages of business case development – that is, before more extensive projects are defined and transport modelling sought. The few tools available did not incorporate the contextual information that the international research revealed as important, so a tool was developed by the project team. Important findings included that key determinants of induced travel are the travel-cost reduction achieved by new lanes and the base road network used to infer VKT growth. The tool combines a lane-km elasticity with a cost elasticity approach to guide the user through the logic and calculations to indicate a potential range of induced VKT to expect from the proposed additional road lanes. Rules were applied to indicate the scale of land-use effects on VKT.]]></description>
      <pubDate>Thu, 09 May 2024 08:48:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2378049</guid>
    </item>
    <item>
      <title>An Air Passenger Airport Access Model</title>
      <link>https://trid.trb.org/View/2137643</link>
      <description><![CDATA[A very important component of common carrier long-distance travel is the access/egress trips to/from airports and train/bus stations. Access/egress travel times and costs can influence choice of intercity travel mode, as well as the choice of departure/arrival station in cities that have more than one airport or train/bus station. They can also be a significant component of the overall intercity travel time (for example, getting to/from the airport for a flight between Toronto and Montreal or Ottawa). Travel to/from airports, train stations, etc. also generate considerable traffic and congestion in peak periods, especially in the vicinity of the stations, and so are of importance in urban travel demand models as well. These trip components, however, often are poorly modelled, due to a variety of reasons, such as lack of appropriate data, overly aggregate model zone systems that preclude precise travel time calculations, and, sometimes perhaps, simple inattention to the problem. This paper presents one step towards addressing this gap by developing a “groundside access travel demand model” for Toronto Pearson International Airport (TPIA). It uses 2018 survey data provided by the Greater Toronto Airports Authority (GTAA) to develop a model that predicts trips to TPIA by air passengers for a typical weekday.]]></description>
      <pubDate>Mon, 27 Mar 2023 09:33:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2137643</guid>
    </item>
    <item>
      <title>A Commercial Vehicle Model for Greater Toronto And Hamilton Area</title>
      <link>https://trid.trb.org/View/2134541</link>
      <description><![CDATA[The Greater Toronto and Hamilton Area (GTHA) is a major generator of freight traffic in Ontario due to its large consumer base, location of major transportation hubs (airports, intermodal terminals, etc.) and manufacturing activities (HDR, 2011). The GTHA is Canada’s largest urban region, consisting of City of Toronto, Durham Region, York Region, Peel Region, Halton Region and City of Hamilton (Figure 1). The province of Ontario generates about 75% more shipments and receives 50% more shipments than the next ranking province, Alberta, (Ferguson et al., 2014). Approximately two-thirds of these shipments are generated by the GTHA and trucking is responsible for over 77 percent of total tonnage of freight movements in the region (Ferguson et al., 2014). Efficient movement of freight in the region is one of the key policy objectives in the regional growth plan – the Big Move (HDR, 2011). Freight demand models are used as tools for the analysis of infrastructure, regulatory and technology policies. The GTHA commercial vehicle (CV) model presented in this paper has been developed for such purposes. The current CV model is a 12.5-hour model (6:30 am – 7:00 pm) based on trip generation and count data from 2011. The model outputs include the number of trucks by truck type (light, medium and heavy) over a 12.5-hour period on road links across the GTHA. This paper presents the details of the model, with particular focus on trip generation. The next section discusses the freight trip generation literature. Next, various data sources used in the model development and calibration are discussed. Then, the 3-stage modeling steps are presented, followed by calibration results. Finally, some concluding remarks are made.]]></description>
      <pubDate>Mon, 27 Mar 2023 09:33:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2134541</guid>
    </item>
    <item>
      <title>Improving Trip Generation Estimates for Canadian Sites Using Aggregation and Extraction Techniques (Poster)</title>
      <link>https://trid.trb.org/View/1682717</link>
      <description><![CDATA[Trip generation analysis plays a vital role in determining the impact of new land use developments. Trips are generally estimated using regionally established trip rates from trip generation data collected over the period of time at specific land uses. However, some regions which do not have their own data usually rely on trip generation data published by other similar regions or on data collected by the Institute of Transportation Engineers (ITE), USA. Over the last 50 years, ITE has already collected data at over 26,000 sites in various parts of the USA and Canada for 173 different types of land uses. As only a small subset of data (~0.5%) is from Canada, there is a question as to whether that data could better represent Canadian sites. As ITE’s Trip Generation Manuals in the hardcopy format do not facilitate viewing or extracting data for specific regions (i.e. country or state) or the ability to aggregate data with local data, practitioners face a number of challenges in using ITE’s data for Canadian sites. To evaluate if data extraction and aggregation have any impact on improving trip generation estimates, data from Canadian and American sites were studied separately in order to analyze the statistical parameters by using the OTISS Pro analysis tools. The analysis revealed that 38% of studies with good data showed significant improvements in terms of fine-tuning standard deviation and regression coefficient (R2) after extracting Canadian sites data. Similarly, about 15% of studies with good data showed improved statistics by aggregating with the American site data. This further helped us to improve trip generation estimates by establishing appropriate trip rates and equations for Canadian sites. Based on this finding, this poster illustrates that by having a way to extract ITE’s data by regions or aggregate with the relevant local data could benefit Canadian practitioners performing qualitative trip generation analyses. It also emphasizes the importance of collecting more regional data.]]></description>
      <pubDate>Tue, 04 Feb 2020 15:00:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1682717</guid>
    </item>
    <item>
      <title>Inferring Activity Selection and Scheduling Behavior of Population Cohorts for Travel Demand Modeling</title>
      <link>https://trid.trb.org/View/1667762</link>
      <description><![CDATA[Transportation can be considered as one of the main and essential human activities, that involves nearly everyone on a daily basis. To date, numerous travel demand models have been developed, using both aggregated and disaggregated approaches, for modeling short-term and long-term choices of travelers, such as activity participation, timing, transport mode, activity location, route choice, work/residential location, and vehicle ownership (Oppenheim, 1995; Ortuzar & Willumsen, 2011). Complexities in individual travel behavior have increased with continued urban development and rapid technological progress. Trip chaining and multimode transport, flexible working hours, self-employment, and online shopping have become far more common in recent years (Goran, 2001). As travel behavior becomes more complex, travel demand forecasting requires more detailed information. From a disaggregated modeling point of view, there are significant associations between trips and the activity participation of travelers (Kitamura, Chen, & Pendyala, 1997). Furthermore, travelers with varying socio-demographic and socio-economic characteristics in the region have divergent time-use activity patterns. This paper presents a new disaggregated travel demand microsimulation model framework that is sensitive to the mix of variables connected to travelers’ decisions. New pattern recognition and inference models are developed to identify population clusters with homogeneous time-use daily activity patterns, and to predict activity selection and scheduling behavior of these population cohorts. The representative behavior within each cluster is then used as an information guide for modeling the 24-hour activity schedule and the travel linked to it. The proposed model is applied to data from the large Halifax STAR household travel diary survey. The proposed modeling framework has much higher reproducibility and shorter computational time compared to alternative modeling frameworks.]]></description>
      <pubDate>Thu, 21 Nov 2019 14:03:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1667762</guid>
    </item>
    <item>
      <title>Trip Generation Modeling for London, Ontario, Canada: A Micro-Analytical Approach</title>
      <link>https://trid.trb.org/View/1537249</link>
      <description><![CDATA[The transportation planning process has been used intensively by estimating urban travel demand models since the mid-1950s. Researchers and policy makers have been using these models to make informed decisions on the future development and management of urban transportation systems. To date, the majority of the efforts in the literature have been focused on developing aggregate zone based models (TMIP, 2014). In this framework, the urban area is divided into a finite number of zones known as traffic analysis zones (TAZs) that form the units of analysis in the model. However, with the need to capture the travelers’ behavior, there has been a shift towards developing micro-based models that make use of households and individuals as the unit of analysis. In general, the zone based approach has been criticized due to the lack of the realism needed to capture the actual travel behavior observed in the urban area. This paper strives to advance the micro-based paradigm by studying trip generation in the London Census Metropolitan Area (CMA). It does so by developing micro-based trip generation models using a household travel survey that was collected in the year 2009. The focus will be to compare various techniques that could be used to model trip generation (i.e., regression, cross-classification, discrete choice, and count models) at the micro-level.]]></description>
      <pubDate>Wed, 22 Aug 2018 13:41:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1537249</guid>
    </item>
    <item>
      <title>Estimating residential public transport trip generation rates</title>
      <link>https://trid.trb.org/View/1505860</link>
      <description><![CDATA[Traffic Impact Assessment (TIA) uses vehicle trip generation rates to quantify the transport impacts of developments. However, very little guidance is available on how to estimate the impact of developments on public transport trip generation. This may lead to underestimating the impacts that new developments have on the public transport system, especially in inner-city areas. This paper attempts to fill this gap by estimating residential trip generation rates for public transport in Melbourne. These were derived using the VISTA09 travel survey (Victorian Integrated Survey of Travel and Activity). We found that public transport trip generation rates depended on the distance to public transport stop/station and distance to Melbourne CBD. Several case studies were used to illustrate the difference between public transport and private vehicle trip generation rates. Developments in inner and middle Melbourne suburbs that have good access to public transport generate a significant number of public transport trips, comparable to the trips generated by private vehicles. The inclusion of public transport trip generation data within TIA guidelines will assist in a much more detailed measurement of the impacts developments may have on the public transport system.]]></description>
      <pubDate>Thu, 22 Mar 2018 12:28:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1505860</guid>
    </item>
    <item>
      <title>Method of Data Integration for GPS and Roadside Intercept Survey Data</title>
      <link>https://trid.trb.org/View/1483989</link>
      <description><![CDATA[The Commercial Vehicle Survey (CVS) has been one of Ministry of Transportation Ontario’s (MTO) main instruments for obtaining information about freight vehicle flows on the provincial transportation system. The CVS is a roadside survey that intercepts truck trips at data collection sites to gather information about inter-city truck flows as well as some urban truck flows. The survey is conducted every 5 years, and the most recent available data were collected in the period from 2010 to 2014, and is referred to as the 2012 CVS. During the survey, the intercepted truck drivers were interviewed to collect information about vehicle type, trip movement, and cargo contents. The 2012 CVS encompasses over 200 data collection sites and a total of 45,000 interviews (Ministry of Transportation Ontario, 2015). While the CVS is an extensive data collection effort, the sample collected through CVS is still a small portion of the entire truck population that flows through Ontario.  A new source of data is also becoming accessible to the MTO. Probe GPS tracking data from fleet management providers such as Shaw and ATRI have a greater geographic coverage of truck movement than intercept surveys, resulting in better representation of the truck population. It is estimated that GPS data cover as much as 50% of total vehicle kilometers travelled by trucks (Ministry of Transportation Ontario, 2015). However, the GPS tracking data are collected automatically by a device on the truck without any interaction from the driver, and the device lacks intelligence to collect data with more complexity such as cargo content. In summary, the CVS contains highly detailed information about truck flows at the points of intercept but lacks population coverage, while GPS tracking data provide a wider coverage of truck movements but lack useful supplementary information about the tour.  The purpose of this study is to apply data fusion methods (D'Orazio, Di Zio, & Scanu, 2006) to integrate these two data sets to produce a more useful combined source of information for modelling and policy analysis. The GPS data and the CVS data complement each other nicely in theory, but in practice the two sources of data have different levels of aggregation, sample methods, and statistical precision, all of which are common problems of data fusion (Polak, 2006). While challenges exist, the merits of data fusion are notable. First, it avoids the costly option of conducting an entirely new survey when the variables of interest exist in multiple previous surveys (Van Der Puttan, Kok, & Gupta, 2002). Second, it provides a means to analyze variables from different surveys within one platform (Bayart, Bonnel, & Morency, 2008). Third, it provides an approach to address the lack of comparability between data when a mix of survey methodologies are used (Bayart, Bonnel, & Morency, 2008).]]></description>
      <pubDate>Thu, 28 Sep 2017 12:31:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1483989</guid>
    </item>
    <item>
      <title>Expansion of a GPS Truck Trip Sample to Remove Bias and Obtain Representative Flows for Ontario</title>
      <link>https://trid.trb.org/View/1483988</link>
      <description><![CDATA[This paper identified two types of bias, industry and distance, found in a sample of GPS derived truck trips. A method was established to remove the industry bias using trip rates and expanding by the population of firms in a given zone. In addition, distance bias was accounted for by utilizing the IPF method to match total estimated zonal production and attraction (from the first expansion) while maintaining the origin-destination patterns obtained from the 2006 CVS survey created by MTO. A second expansion was then applied by optimizing the expanded GPS totals with the truck totals from survey station points located along major Ontario routes.  The total trip productions and attractions generated from the analysis provided a better representation of truck trips in Ontario compared to the original sample while closely matching the aggregate totals observed on the road network. However, the microscopic behaviour of individual trips is lost at an aggregate level. To retain the travel behaviour of vehicles, we plan to utilize the original sample to synthesize a full population of trips by using methods such as combinatorial optimization (Ryan et al., 2009). In such a case, the synthesis algorithm can be used to ensure that the aggregate zonal totals by industry type are maintained. Such a method has been applied before for expanded trip rates. For example, Goulias et al. (2014) used population synthesis to expand a household survey in California. After the trips are synthesized, our data can then be used in microscopic transportation models (such as truck tours) without the biases inherent in the original GPS sample.]]></description>
      <pubDate>Thu, 28 Sep 2017 12:31:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1483988</guid>
    </item>
    <item>
      <title>The assessment of the effects of small-scale development proposals on the transport network</title>
      <link>https://trid.trb.org/View/1467913</link>
      <description><![CDATA[The national integrated transport assessment guidelines used by practitioners in New Zealand only provide guidance for the assessment of significant sized developments, setting out the approach to be taken with varying assessment levels relative to size. It is becoming increasingly evident there are cases when small-scale developments, which do not trigger the lower thresholds for assessment, are having an effect either individually or cumulatively on the transportation network. In these instances, it may be necessary for the impacts of these small-scale developments to be assessed in an appropriate manner. This research investigated if and how the potential effects of small-scale developments should be identified and in doing so has provided an opportunity to fully understand if the absence of national guidelines is limiting the opportunity for effective network management and land use planning. Both Auckland and Christchurch have gone through a process of identifying appropriate thresholds that will trigger the need for an integrated transport assessment through a high trip generator rule. This has resulted in extensive discussions amongst practitioners regarding the appropriate extent of assessment based on the size, scale and location of development. This research assists the debate by resolving a number of core issues.]]></description>
      <pubDate>Wed, 24 May 2017 13:44:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1467913</guid>
    </item>
    <item>
      <title>A best practice evaluation of traffic impact assessment guidelines in Australia and New Zealand</title>
      <link>https://trid.trb.org/View/1457947</link>
      <description><![CDATA[Traffic impact assessments (TIAs) are crucial to understanding how a proposed development will impact the surrounding transport network. Various national and state TIA guidelines are available throughout Australia and New Zealand, but there is little understanding of the extent to which these guidelines constitute best practice in TIA. This research aims to understand what a standard of best practice means in the context of TIA and to what extent the Australian and New Zealand TIA guidelines represent this standard. The research included an evaluation of the national guidelines for Australia and New Zealand, as well as state/region specific guidelines from New South Wales, Queensland, Tasmania, Western Australia and Auckland. An international literature review of best practice in TIA was undertaken to inform the development of an assessment framework using on a ‘scorecard’ approach. This scorecard was then applied to assign numerical weight to reflect the extent to which each TIA guideline met best practice standards. The results highlighted a number of key areas for improvement, particularly those related to legislative frameworks, multi-modal transport considerations, and the monitoring and review of TIAs. The conclusions of this research are indicative only, and limited to the breadth of literature review that informed the paper. Further research should seek the opinions of academics, various transport stakeholders and industry participants to revise and refine the framework in order to provide a more accurate standard with which to measure best practice.]]></description>
      <pubDate>Mon, 27 Feb 2017 10:10:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1457947</guid>
    </item>
    <item>
      <title>Environmental Impact of the Induced Traffic from Highway 25 Extension Project</title>
      <link>https://trid.trb.org/View/1417742</link>
      <description><![CDATA[The objective of this study is to address the air pollution from the induced traffic during and after the construction of highway 25 extension project in Montreal Island. This study is conducted into two parts. The first portion interpolates the spatial pattern of carbon dioxide equivalent (CO2e) GHGs concentrations within a 5-km buffer zone of highway 25 extension project in Montreal Island. The second part simulates the AADT for each road link of the road network within a 5-km buffer zone of the project for different scenarios in order to understand the change of traffic volumes during and after the construction of the extension project.]]></description>
      <pubDate>Tue, 26 Jul 2016 17:03:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1417742</guid>
    </item>
    <item>
      <title>Quick Analysis Technique to Estimate GHG Emissions Based on Neighbourhood Built Form (Poster)</title>
      <link>https://trid.trb.org/View/1399696</link>
      <description><![CDATA[There is growing awareness of climate change and its potential impacts. In response, many municipalities and provinces have set greenhouse gas (GHG) emission reduction targets. The transportation sector is one of the largest contributors to Canadian GHGs. As such, it will increasingly be called upon to help achieve GHG emission reductions. In the past few years, considerable research and policy in the transport sector has focused on reducing GHG emissions through improvements in technology, fuels and vehicles. However, there has been less attention given to travel activity. There are even fewer studies looking at how to reduce automobile usage and distances travelled, and especially less work investigating the relationship between built form (suburban vs. urban, type of infrastructure, etc.) and its impact on automobile usage. This study focuses on transportation related GHG emissions by examining the association between built form and travel activity. The objective is to present a quick-analysis tool to assess the GHG impacts based on changes in vehicle kilometers travelled (VKT): built form (density, mixed-uses, etc.), infrastructure (street connectivity, distance to high order transit, sidewalks, etc.) and context (location in region, demographics, TDM, etc.). This tool was developed to identify the most efficient improvement methods in a suburban context, without the need to develop an extensive travel activity model. This relationship is shown through the development of multiple scenarios to compare and contrast the GHG emissions of neighbourhoods with different characteristics (built form, infrastructure, regional context, etc.). Existing research on travel behaviour and the built form is used to analyse the relationship between the vehicle-kilometres driven by residents and the built form and regional context of a neighbourhood. This method is used to examine the effects of improving street grid connectivity, sidewalk coverage, cycling infrastructure and transit service areas on GHG emissions, for each neighbourhood scenario. In turn, this information provides a useful decision-support tool that enables to identify the most effective strategies for reducing GHG emissions in existing and new neighbourhoods, while taking into account the local and regional context.]]></description>
      <pubDate>Fri, 26 Feb 2016 10:51:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1399696</guid>
    </item>
    <item>
      <title>Get me to the track on time! - Traffic management for the 2015 PanAm/ParaPanAm Games in the Greater Toronto Area</title>
      <link>https://trid.trb.org/View/1399629</link>
      <description><![CDATA[The 2015 PanAm/ParaPanAm Games are scheduled for July and August 2015 in the Greater Toronto Area. Due to the overall level of congestion on area highways and the fact that the Games venues are distributed across a wide area, with the Athleteâ€™s Village being located on the Toronto Waterfront, travel time reliability for athletes and officials is a key issue. The Ministry of Transportation of Ontario was assigned the responsibility of planning and implementing a traffic management strategy to ensure that athletes and officials could be at their venues â€œon timeâ€ while minimizing the impact on the travelling public. To facilitate the development and evaluation of traffic management strategies, a large multi-level traffic simulation model was developed using AIMSUN. While the â€œmacroâ€ and â€œmesoâ€ levels of the model were used at various stages in the process, the principal tool for operational analysis was the â€œhybridâ€ level, featuring â€œmicroâ€ operation on key expressway corridors and â€œmesoâ€ operation on the remaining expressways and arterial roads. The model includes 345 kilometres of expressways, 135 interchanges, approximately 2,000 km of surface streets, and 920 signalized intersections. In addition to the evaluation of Transportation Systems Management (TSM) strategies, the simulation model was used to evaluate the potential role of Travel Demand Management (TDM) in mitigating the traffic impacts of the Games.]]></description>
      <pubDate>Fri, 26 Feb 2016 10:46:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1399629</guid>
    </item>
    <item>
      <title>Comparing two processing routines for GPS traces: lessons learnt</title>
      <link>https://trid.trb.org/View/1285284</link>
      <description><![CDATA[This paper describes what may be one of the first side-by-side tests of two alternative software products for processing GPS traces into trips, and discusses some lessons learnt from the comparisons.  For GPS to be useful as an alternative to self-report survey mechanisms, it is imperative that good processing software becomes available to reduce the data streams from the GPS devices into specific trips, with the various attributes of trips that are needed for modelling purposes.  Currently, a number of agencies and researchers around the world have developed alternative software products, but none of these are generally open source, and comparisons between them are almost non-existent, although most make claims to certain levels of accuracy.  In this paper, we describe an exercise in which two software products were used on the same GPS data set, following which a detailed comparison was made of the results.  While it is interesting to see, overall, the accuracy differences between the two software products, what is of even more interest is the lessons that can be learnt about processing software in general.  The paper draws some conclusions about the directions forward for processing software and processing routines in general.]]></description>
      <pubDate>Mon, 06 Jan 2014 10:52:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1285284</guid>
    </item>
  </channel>
</rss>