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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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    <item>
      <title>Population Synthesis Accommodating Heterogeneity: A Bayesian Network and Generalized Raking Technique</title>
      <link>https://trid.trb.org/View/2095373</link>
      <description><![CDATA[Agent-based microsimulation modeling techniques are adopted for urban system modeling mainly because of their capacity to address the complex interactions among individuals, households, and other urban elements. The performance of urban simulation models is largely dependent on the quality of the input data, which is generated through a population synthesis procedure. This study proposes a Bayesian network and generalized raking techniques for population synthesis. The Bayesian network is used to generate the synthetic population pool from the microsample, and generalized raking is used to fit the synthetic population with the control total. Some of the key features of the proposed population synthesis are as follows: accommodating heterogeneity based on both household and individual attributes; tackling missing/incomplete observations in the microsample; and generating a true synthesis of the population from the microsamples. A data-driven structure learning technique is adopted to generate effective and optimal structures among the heterogenous households and individuals. This Bayesian network?+?generalized raking procedure is implemented to generate a 100% synthetic population at the smallest zonal level of dissemination area for the Central Okanagan region of British Columbia. The results suggest that capturing heterogeneity within the Bayesian network has tremendously benefitted the reconstruction process to efficiently and accurately generate a synthetic population from the available microsample. Finally, this population synthesis is developed as a component of the agent-based integrated urban model, currently under development at The University of British Columbia’s Okanagan campus.]]></description>
      <pubDate>Thu, 12 Jan 2023 09:18:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2095373</guid>
    </item>
    <item>
      <title>Framework for designing sample travel surveys for transport demand modelling in cities</title>
      <link>https://trid.trb.org/View/1918915</link>
      <description><![CDATA[Travel surveys in cities remain the main source of information for obtaining people’s trip characteristics and developing transport models that serve to predict the performance indicators of transport systems. Despite the large number of travel surveys that have been conducted, there is still no universal methodology for survey design and no strict instructions for choosing a certain methodology for a particular case. Moreover, existing guidelines for sample size definition do not provide any estimates of the accuracy of obtained results. A new approach is proposed for determining the number of sample observations necessary for obtaining travel characteristics—such as travel time or distance—with a given precision. It takes into account the probabilistic nature of sample estimates and does not depend on the trip characteristic being studied. An example of the application of this approach demonstrated that existing guidelines for sample size definition allow trip characteristics to be defined with an error of 20–40%. Additionally, it was determined that while maintaining an average error below 30%, there is a possibility to decrease the required number of observations by up to 56% compared to the number recommended in the Handbook of Transport Modelling (HTM). The study also shows that it is possible to significantly decrease the sample size when the distribution law of the trip characteristic being investigated is known. Another outcome of the study was a substantiated reference size for a pilot survey sample.]]></description>
      <pubDate>Thu, 28 Apr 2022 15:41:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1918915</guid>
    </item>
    <item>
      <title>Propensity score methods for road safety evaluation: Practical suggestions from a simulation study</title>
      <link>https://trid.trb.org/View/1855477</link>
      <description><![CDATA[The propensity score (PS) based method has been increasingly used in road safety evaluation studies. However, several major considerations regarding its implementation arise when using the PS method. First, as is well known, the PS method is ‘data hungry’ in terms of the number of treated and control units, however, it is sometimes difficult and time-consuming to construct a large sample in road safety studies. It would be helpful to better understand how to choose a proper sample size, as well as the ratio of the number of treated units to the control ones. Second, the criteria used for covariates selection of the PS model were not fully consistent across the existing road safety evaluation studies. Due to the complicated mechanisms behind the implementation of road safety measures and policies, including all relevant covariates that affect both the selection into treatment (i.e., implementation of road safety measures) and the outcomes (i.e., road accidents) is impossible. In this paper, the authors conduct a simulation study to investigate such issues and provide some practical suggestions for using PS methods in road safety evaluations. The estimator considered in this study is the inverse probability weighting estimator based on the PS. The results suggest that the bias and variance of the estimated treatment effect will remain stable when the sample size reaches a certain level. A proper sample size is the one that ensures relevant covariates achieve acceptable balance. Regarding the issue of covariates selection, including the covariates that significantly affect the road accidents is recommended, regardless of whether they affect the implementation of road safety measures. This study also proposes practical procedures for using the weighting approach to evaluate the effects of road safety treatments.]]></description>
      <pubDate>Tue, 22 Jun 2021 14:24:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1855477</guid>
    </item>
    <item>
      <title>Passenger perceptions on sustainable propulsion systems: which factors mediate or moderate the relationship?</title>
      <link>https://trid.trb.org/View/1765002</link>
      <description><![CDATA[The purpose of these studies was to examine how the type of propulsion system impacted the willingness to fly in a hypothetical scenario. Using a sample of 624 participants from the USA across two studies, it was found, in general, that participants were most willing to fly using traditional jet fuel or a biofuel followed by battery/electric and then solar-powered aircraft. There were no significant differences based on participant gender, any significant interactions nor any significant mediators between the type of propulsion and the willingness to fly. Still, familiarity with sustainability, willingness to pay for sustainability, and environmental commitment were found to be significant moderators. In cases where participants had high levels of the three moderating variables, they were significantly more willing to fly using sustainable propulsion systems than compared to low levels of the moderating variables. The study concludes with a discussion of these findings and the practical applications of this research.]]></description>
      <pubDate>Tue, 25 May 2021 16:20:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1765002</guid>
    </item>
    <item>
      <title>GPS-data in bicycle planning: “Which cyclist leaves what kind of traces?” Results of a representative user study in Germany</title>
      <link>https://trid.trb.org/View/1738205</link>
      <description><![CDATA[In the recent decade, numerous scientific studies investigated the utility of GPS data to bridge the existing data gap in cycling. Almost all studies faced the lack of representativeness of data because the participation in data collection was influenced by self-selection of participants. Thus, the global positioning system (GPS) data were biased by dominant user groups. However, the difference between biased samples and representative data has not been quantified, yet, as there was no data from representative selected samples that could be used for comparison. The present work investigates whether and how cycling behaviour of different groups differs. It furthermore examines the parameters cycling behaviour depends on. For this purpose, nearly 200 cyclists were selected according to representativeness regarding age, gender and type of cyclist. The study participants recorded their ways using a GPS smartphone app during a two-week field study. Data analysis revealed little influence of the user group on cycling behaviour, only. In contrast, the variables age, gender and trip purpose show a strong influence on cycling speed, distance, acceleration and frequency. The study results point out the need for representative data collection in GPS-based studies on cycling.]]></description>
      <pubDate>Mon, 05 Oct 2020 14:36:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1738205</guid>
    </item>
    <item>
      <title>Workshop Synthesis: Representativeness in surveys: challenges and solutions</title>
      <link>https://trid.trb.org/View/1567737</link>
      <description><![CDATA[During this workshop on “representativeness in surveys: challenges and solutions” for mobility surveys, the group discussed various issues of representativeness in travel surveys. Issues such as lack of coverage of sampling frames, nonresponse mechanism and measurement error bias were investigated along with models that can help to reduce the impacts of these issues and preserve data quality for transport analysis. According to the participants of the workshop, the best way to avoid problems with representativeness in surveys is to tackle the problem upstream (good sampling frame, follow-up with respondents, incentives, response facilitators, etc.). But still at the end of the day the authors will need to reweigh the respondent sample to account for differential probabilities of selection among subgroups; effects arising from nonresponse; inadequacies in sample frame, etc. and bring the respondent sample data up to the dimension of the study population.]]></description>
      <pubDate>Thu, 20 Dec 2018 15:33:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1567737</guid>
    </item>
    <item>
      <title>Crash Report Sampling System: Design Overview, Analytic Guidance, and FAQs</title>
      <link>https://trid.trb.org/View/1507601</link>
      <description><![CDATA[This document describes the Crash Report Sampling System sample design and weighting procedures and explains some basic concepts about estimation based on complex survey data. In addition, it provides examples and discusses issues of CRSS data analysis.]]></description>
      <pubDate>Mon, 23 Apr 2018 16:44:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1507601</guid>
    </item>
    <item>
      <title>Analysis of Required Minimum Sample Size of Floating Cars for Estimating Urban Road Link Travel Time Considering Bimodal Distribution and Estimation Error</title>
      <link>https://trid.trb.org/View/1438315</link>
      <description><![CDATA[Despite the wide application of floating car data (FCD) in urban road link travel time and congestion estimation, limited efforts have been made on determining the minimum sample size of floating cars and its impact on travel time estimation error. This study contributes to determining the required minimum sample size of floating cars and the corresponding travel time estimation errors considering the bimodal nature of interrupted flow on urban road links. Travel times are drawn from two populations: one representing non-stop movement through signalized intersection and one representing vehicles which stop at traffic lights. Hellinger distance comparison is made between the distribution of travel times from FCD and Radio Frequency Identification Data (RFID) for the studied road link, where RFID is treated as the real travel time distribution pattern because its capability as a full-sample sensor. This study obtains the Hellinger distances with respect to different sample sizes of FCD from two populations using the genetic algorithm, from which the minimum FCD sample sizes corresponding to different levels of travel time estimation errors can be identified. This study also quantifies two critical factors that affect the minimum required FCD sample size and provide recommendations for FCD sampling of two populations.]]></description>
      <pubDate>Mon, 27 Feb 2017 17:12:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1438315</guid>
    </item>
    <item>
      <title>Sample Size and Precision for Pavement Inspection in a Maintenance Quality Assurance Program</title>
      <link>https://trid.trb.org/View/1372031</link>
      <description><![CDATA[Considering the large amount of highway asset items, a statistically valid data sampling method is required for the level of service inspection of highways. The presented paper discussed the determination of sample size for the pass/fail highway inspection approach. The historical highway inspection data from Tennessee were collected for demonstration. It was found that passing percent is the most significant factor for sample size while population size has little influence. An effective approach to improve the precision at lower management levels without increasing sample size is to use an average sample size for all different subgroups. To investigate the necessity of stratified sampling, statistical paired t-tests were conducted to identify if the passing percent of subgroups are significantly different with the whole state. The results showed no significant difference between interstates and state routes. Although sampling at district levels could potentially improve the significance level of half of the districts, the sample size would be multiplied and is not cost-effective.]]></description>
      <pubDate>Mon, 02 Nov 2015 09:18:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1372031</guid>
    </item>
    <item>
      <title>Comparing Participants' Attributes of Interview-based and Smartphone-based Visitors' Behavior Survey</title>
      <link>https://trid.trb.org/View/1372737</link>
      <description><![CDATA[Many researchers and practitioners now consider global positioning system (GPS)-based or smartphone-based travel surveys as promising alternatives to traditional paper-based or interview-based travel surveys. They will be useful to capture behaviors not only in metropolitan area but also in downtown area. However, the authors have to care about representativeness of samples in smartphone-based survey, because it will naturally limit the participant to smartphone owners and sample distribution may have bias. This study investigates this issue by comparing participants’ attributes of interview-based and smartphone-based travel survey. The authors conducted these two surveys in same target area - downtown Kumamoto - and reveal some sampling difference. Finally, the authors estimated duration model of staying time using these two data.紙面やインタビュー型の交通調査への代替手段として，GPSやスマートフォン (スマホ)を利用した交通行動調査の研究や実務への応用が試みられている．これらの新たな交通調査は，都市圏レベルの人の動きだけでなく，回遊調査への応用も期待されている．しかし，新たな調査法の参加者の母集団代表性には注意が必要である．例えば，スマホ型調査の対象者はスマホ所有者に当然限定され，サンプルのランダム性は確保されるとは限らない．本研究は，この問題意識を背景として，調査参加者の属性に着目してインタビュー調査とスマホ型調査の比較を行うことを目的としている．熊本都心部回遊調査を，この2つの調査形式で実施しており，それらのサンプルの属性分布などを比較する．最後に，これら2つの調査統合データで滞在時間モデルを推定した例も提示する．]]></description>
      <pubDate>Wed, 28 Oct 2015 10:20:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1372737</guid>
    </item>
    <item>
      <title>Minimum Sample Size for Measuring Travel Time Reliability</title>
      <link>https://trid.trb.org/View/1339444</link>
      <description><![CDATA[Travel time reliability is increasingly being used as a major indicator of service quality for travelers, but although a great deal of research has focused on estimating travel time reliability, the minimum data size required for measuring reliability has received little attention. The sample size not only determines the collection cost but also the computational cost of processing the data. Here the authors propose a methodological framework to determine the minimum sample size that supports stable distributions for freeway travel times. The new framework consists of two methods: the first applies a parametric model (Log-normal distribution) and Maximum A Posterior (MAP) estimation to examine the convergence of variances; and the second uses a non-parametric model (Kernel Density Estimation, KDE) and cosine similarity to measure distribution similarity. The results for the parametric model revealed that the majority of the distribution variances failed to converge, although this approach could be used for roadway links without non-recurrent congestion. A major advantage of KDE, the nonparametric method, is that the distribution’s shape was not easily affected by a small number of travel time outliers caused by weather or special events. For freeway travel time data collected in the Greater St. Louis Area, it is recommended that the minimum travel time sample size should be 65 weeks. Overall, the proposed framework has demonstrated its potential utility for transportation practitioners and researchers seeking to determine the minimum data size for measurements of travel time reliability that also minimizes the cost of data collection.]]></description>
      <pubDate>Mon, 30 Mar 2015 09:33:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1339444</guid>
    </item>
    <item>
      <title>Naturalistic Driving Study: Descriptive Comparison of the Study Sample with National Data</title>
      <link>https://trid.trb.org/View/1344942</link>
      <description><![CDATA[This report provides a descriptive comparison of data from the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS) sample and national data. The primary objective of the SHRP 2 NDS is to support analyses relating crash risk to driver, vehicle, roadway, and environmental characteristics. Since age is one of the most important driver characteristics, this objective is best supported by adequate sample sizes across all age groups. The national population of drivers has the greatest number of drivers in the middle age groups and progressively fewer in the younger and older ages. In contrast, the NDS oversampled younger and older drivers. In addition, the NDS oversampled newer-model-year vehicles because these vehicles provided useful data through their vehicle networks. It is important for users of the NDS data to have information on the relationship of the NDS sample to the national population. In general, many statistics taken directly from the NDS sample will not be nationally representative unless they are adjusted to account for relevant characteristics of the NDS sample.]]></description>
      <pubDate>Tue, 03 Mar 2015 12:55:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1344942</guid>
    </item>
    <item>
      <title>A spatio-temporal approach for identifying the sample size for transport mode detection from GPS-based travel surveys: A case study of London’s road network</title>
      <link>https://trid.trb.org/View/1316236</link>
      <description><![CDATA[Compared with conventional household one/two days travel survey, GPS-based travel surveys hold many attractive features for travel behaviour studies. Different machine learning-based techniques have been developed to infer the transportation mode based upon GPS data from such surveys. However, nearly none of these studies calculate the sample size required for validating these techniques. Since different surveys target different study areas for different temporal periods and different travel modes, identifying sample sizes for all transport modes at different spatio-temporal granularities is of imperative urgency given the high time and financial costs of GPS-based travel surveys. Here the authors use road network journey time data of London to calculate appropriate sample sizes for travel surveys designed either for a specific period-of-the-day, day-of-the-week or month-of-the-year. They also use different transportation analysis zones (central, inner and outer London) to demonstrate the spatial variability of the data over these different survey durations. Then they finally calculate and analyse the range of required sample sizes for different travel modes within these spatio-temporal granularities. This case study provides a good reference of sample size design for GPS-based travel survey in big cities.]]></description>
      <pubDate>Thu, 24 Jul 2014 15:18:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1316236</guid>
    </item>
    <item>
      <title>Comparing three commonly used crash severity models on sample size requirements: Multinomial logit, ordered probit and mixed logit models</title>
      <link>https://trid.trb.org/View/1290246</link>
      <description><![CDATA[There have been many studies that have documented the application of crash severity models to explore the relationship between accident severity and its contributing factors. Although a large amount of work has been done on different types of models, no research has been conducted about quantifying the sample size requirements for crash severity modeling. Similar to count data models, small data sets could significantly influence model performance. The objective of this study is therefore to examine the effects of sample size on the three most commonly used crash severity models: multinomial logit, ordered probit and mixed logit models. The study objective is accomplished via a Monte-Carlo approach using simulated and observed crash data. The results of this study are consistent with prior expectations in that small sample sizes significantly affect the development of crash severity models, no matter which type is used. Furthermore, among the three models, the mixed logit model requires the largest sample size, while the ordered probit model requires the lowest sample size. The sample size requirement for the multinomial logit model is located between these two models.]]></description>
      <pubDate>Tue, 04 Feb 2014 10:23:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1290246</guid>
    </item>
    <item>
      <title>Allocation planning for probe taxi devices based on information reliability</title>
      <link>https://trid.trb.org/View/1258090</link>
      <description><![CDATA[In recent years, the gathering of information about road traffic conditions using probe vehicles has begun in many cities. The travel time obtained using probe systems varies among vehicles because of driver characteristics and/or GPS errors even when traffic conditions are constant. To generate traffic information with high reliability, a relatively large number of samples is needed. Also, for efficient information collection, it is necessary to give careful consideration to the allocation of probe vehicles. In this study, the authors analyse the efficient allocation of probe vehicle devices to taxi dispatch centers (TDCs) by using taxi-probe data collected around Nagoya City in Japan. The results show that the efficiency of data collection changes according to the total number of probe devices and the data collection period. In general, efficient data collection requires the allocation of many devices to suburban areas surrounding the central urban area.]]></description>
      <pubDate>Tue, 03 Sep 2013 12:30:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1258090</guid>
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