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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>E-Bus Energy Planning Framework Through Open-Source Low-Resolution Data</title>
      <link>https://trid.trb.org/View/2226047</link>
      <description><![CDATA[This paper develops and assesses an open-source low-resolution data-based framework to estimate battery electric buses energy consumption (BEB's EC). The framework integrates a data-driven prediction model developed using the Multiple Linear Regression Analysis (MLR) technique based on various operational, topological, external, and vehicular parameters, and it relies on low-resolution open-source data collected by transit providers. The model's performance analysis indicates high fitting of the results derived from the data-driven prediction model (predicted EC) and the validated simulation model (observed EC). A one-minute single trip validation resulted in a promising goodness-of-fit. The coefficient of determination (R2) (up to 0.8857) indicates that the prediction model explains the variation in the EC on the selected routes. Further, the lowest R2 value for the one-minute segments is 0.8035. The results from Mean Absolute Percentage Error (MAPE) reveal that the error percentage for all route types included in our assessment was between 10% and 18%, which proves that the prediction model was well fitted. Another validation is performed for the full bus trips. This considers the impact of the parameters derived from the low-resolution data, including weather condition, passenger loading, the initial state of charge, and road condition. The collected passenger loading data (0 passengers to 70 passengers) and the weather condition data (-20 deg C to 30 deg C), road condition (level I to level III) yielded a very accurate prediction with an R2 of 0.9699, 0.9672, and 0.9582, respectively. In addition, the initial state of charge resulted in a superior goodness-of-fit with an R2 of 0.9719.]]></description>
      <pubDate>Fri, 25 Aug 2023 09:18:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2226047</guid>
    </item>
    <item>
      <title>What impressions do users have after a ride in an automated shuttle? An interview study</title>
      <link>https://trid.trb.org/View/1605651</link>
      <description><![CDATA[In the future, automated shuttles may provide on-demand transport and serve as feeders to public transport systems. However, automated shuttles will only become widely used if they are accepted by the public. This paper presents results of an interview study with 30 users of an automated shuttle on the EUREF (Europäisches Energieforum) campus in Berlin-Schöneberg to obtain in-depth understanding of the acceptance of automated shuttles as feeders to public transport systems. From the interviews, the authors identified 340 quotes, which were classified into six categories: (1) expectations about the capabilities of the automated shuttle (10% of quotes), (2) evaluation of the shuttle performance (10%), (3) service quality (34%), (4) risk and benefit perception (15%), (5) travel purpose (25%), and (6) trust (6%). The quotes indicated that respondents had idealized expectations about the technological capabilities of the automated shuttle, which may have been fostered by the media. Respondents were positive about the idea of using automated shuttles as feeders to public transport systems but did not believe that the shuttle will allow them to engage in cognitively demanding activities such as working. Furthermore, 20% of respondents indicated to prefer supervision of shuttles via an external control room or steward on board over unsupervised automation. In conclusion, even though the current automated shuttle did not live up to the respondents’ expectations, respondents still perceived automated shuttles as a viable option for feeders to public transport systems.]]></description>
      <pubDate>Tue, 21 May 2019 11:06:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1605651</guid>
    </item>
    <item>
      <title>Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression</title>
      <link>https://trid.trb.org/View/1595132</link>
      <description><![CDATA[The Expectation-Maximization algorithm is perhaps the most broadly used algorithm for inference of latent variable problems. A theoretical understanding of its performance, however, largely remains lacking. Recent results established that EM enjoys global convergence for Gaussian Mixture Models. For Mixed Regression, however, only local convergence results have been established, and those only for the high SNR regime. We show here that EM converges for mixed linear regression with two components (it is known not to converge for three or more), and moreover that this convergence holds for random initialization.]]></description>
      <pubDate>Mon, 01 Apr 2019 10:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1595132</guid>
    </item>
    <item>
      <title>Trip stage satisfaction of public transport users: A reference-based model incorporating trip attributes, perceived service quality, psychological disposition and difference tolerance</title>
      <link>https://trid.trb.org/View/1568454</link>
      <description><![CDATA[The concept of satisfaction reflects the extent by which a consumed product or service meets the expectations of an individual consumer. Generally, a smaller discrepancy between expectation and service delivery is associated with a higher satisfaction. Satisfaction ratings do, however, not purely reflect the mapping of experienced attributes on some (reference-based) satisfaction scale. The expressed satisfaction rating may also vary as a function of attitudes, personality traits and moods of the respondent at the time of measurement. Thus, in order to estimate unbiased satisfaction with the attributes of a choice alternative, one needs to control for attitudes, moods and personality traits. In contrast with the state of the art in travel behaviour research, which is almost invariantly based on linear regression and structural equations analysis, this study explores the performance of non-linear models of trip satisfaction for public transportation. Focusing on public transportation, the authors assume that trip stage satisfaction systematically varies in a non-linear fashion with the discrepancy between expectations and actual experienced attributes of the trip stage, controlling for perceived service quality, attitudes, moods and personality traits. In contributing to the rapidly growing literature on travel satisfaction in general and in the context of public transportation in particular, the aim of this study is to develop a reference-based model of trip stage satisfaction that takes perceived service quality, attitudes, moods, personality traits and difference tolerance into account. Difference tolerance refers to the notion that within some tolerance level, differences between service delivery and expectations do not have a major effect on satisfaction ratings. The data used to estimate the model were collected in January 2015 in Xian, China among a random sample of respondents. Results support the contentions underlying the study: several relationships between attributes and satisfaction appear non-linear. There is also evidence of difference tolerance, but results differ between attributes.]]></description>
      <pubDate>Wed, 21 Nov 2018 11:19:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1568454</guid>
    </item>
    <item>
      <title>Density-based mixed platoon dispersion modelling with truncated mixed Gaussian distribution of speed</title>
      <link>https://trid.trb.org/View/1509272</link>
      <description><![CDATA[In China, urban arterial traffic presents a mixed-flow feature because the percentage of bus flow is relatively high. This affects the applicability of traditional platoon dispersion models, which are generally only suitable for homogeneous traffic flow. Based on field observations, this paper proposes a mixed platoon dispersion model (MPDM) to macroscopically simulate the mixed platoon dispersion process along the road segment between two successive signalised intersections from the density view. In order to capture the heterogeneity in mixed platoon speeds, the truncated mixed Gaussian distribution is adopted here to fit the speed data collected in the field, and expectation–maximisation (EM) algorithm is employed to estimate the distribution parameters. Later, the piecewise platoon density function is developed to examine the platoon dispersion characteristics. By applying this density function, the formulations of the expected number of vehicles in the front of the platoon that have passed and the expected number of vehicles at the rear of the platoon that have not passed a downstream intersection, as well as the downstream arriving flow function, are derived. Furthermore, numerical calculation for signal coordination verifies the effectiveness of the proposed MPDM.]]></description>
      <pubDate>Thu, 17 May 2018 14:46:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1509272</guid>
    </item>
    <item>
      <title>A New Approach of Estimating Metro Passenger’s Route Choice Model Based on Smart Card Data</title>
      <link>https://trid.trb.org/View/1496120</link>
      <description><![CDATA[There are usually multiple alternative routes for most OD pairs in a large-scaled metro network where route choice model should be established and estimated to analyze passenger’s route choice behavior and carry out passengers flow assignment. Different from the estimation approach based on self-reported RP (Revealed Preference) or SP (Stated Preference) surveyed data which cost too much in collection, this paper proposes a new estimation approach based on smart card data which is collected automatically by automatic fare collection system. Due to the complex form of the log-likelihood function derived from smart card data, EM (expectation maximization) approach is regarded as a reference to develop the new estimation approach. Further, considering travel time reliability, referring to the variation in travel time, mean-standard deviation Path Sized Logit route choice model is developed and estimated based on the new estimation approach. According to the data in Guangzhou Metro, estimation results based on smart card data are very close to those based on self-reported RP data and their applications in travel demand prediction are also very close. These results indicate that estimating route choice model based on smart card data is feasible and acceptable.]]></description>
      <pubDate>Tue, 13 Feb 2018 09:53:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1496120</guid>
    </item>
    <item>
      <title>An Expectation-Maximization Algorithm to Estimate the Integrated Choice and Latent Variable Model</title>
      <link>https://trid.trb.org/View/1480082</link>
      <description><![CDATA[As computing capability has grown dramatically, the transport choice model has rigorously included latent variables. However, integrated latent and choice variable (ICLV) models are hampered by a serious problem that is caused by the maximum simulated likelihood method. The method cannot properly reproduce the true coefficients, which is a problem that is often referred to as a lack of empirical identification. In particular, the problem is exacerbated particularly when an ICLV model is calibrated based on cross-sectional data. An expectation-maximization (EM) algorithm has been successfully employed to calibrate a random coefficient choice model, but it has never been applied to the calibration of an ICLV model. In this study, an EM algorithm was adapted to calibrate an ICLV model, and it successfully reproduced the true coefficients in the model. The main contribution of adopting an EM algorithm was to simplify the calibration procedure by decomposing the procedure into three well known econometric problems: a weighted linear regression, a weighted discrete choice problem, and a weighted ordinal choice problem. Simulation experiments also confirmed that an EM algorithm is a stable method for averting the problem of lack of empirical identification.]]></description>
      <pubDate>Tue, 29 Aug 2017 10:07:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1480082</guid>
    </item>
    <item>
      <title>Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization</title>
      <link>https://trid.trb.org/View/1480011</link>
      <description><![CDATA[The authors consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample corresponds to which model) are not observed. The authors give a tractable algorithm for the mixed linear equation problem, and show that under some technical conditions, their algorithm is guaranteed to solve the problem exactly with sample complexity linear in the dimension, and polynomial in k, the number of components. Previous approaches have required either exponential dependence on k, or super-linear dependence on the dimension. The proposed algorithm is a combination of tensor decomposition and alternating minimization. The authors' analysis involves proving that the initialization provided by the tensor method allows alternating minimization, which is equivalent to expectation-maximization (EM) in their setting, to converge to the global optimum at a linear rate.]]></description>
      <pubDate>Wed, 16 Aug 2017 10:30:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1480011</guid>
    </item>
    <item>
      <title>Home-based telework in France: Characteristics, barriers and perspectives</title>
      <link>https://trid.trb.org/View/1422554</link>
      <description><![CDATA[The aim of this article is to explain the gap between high social expectations, particularly in terms of reducing commuting frequency, increasing productivity and improving work-life balance, and the reality of home-based telework. The authors use three French databases which give information about employers but also employees. The authors highlight that telework is not only a fairly restricted phenomenon but also one that lacks impetus; it is mainly an informal working arrangement. The main reasons raised by both employees and employers are the uncertain advantages coupled with immediate disadvantages. The conclusion examines different contextual factors that could alter this cost-benefits dilemma and foster the development of home-based telework.]]></description>
      <pubDate>Fri, 23 Sep 2016 11:17:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1422554</guid>
    </item>
    <item>
      <title>A Modified Hidden Semi-Markov Model for Traffic Related PM10 Pollution Levels Estimation</title>
      <link>https://trid.trb.org/View/1417761</link>
      <description><![CDATA[Traffic related PM10 (particulate matter with less than 10 microns) pollution exposure leads to different types of diseases e.g., lung function changes, heart rate variability, immune cell responses and asthma attacks. New investigations confirmed a massive increase in particulate matter of central congested urban areas in Tehran metropolitan which exceeds the standards of Environmental Protection Agency (EPA, 150 µg/m3) and World Health Organization (WHO, 20 µg/m3 annual mean and 50 µg/m3 24-hour mean). Long term continuous real-time monitoring of air quality at these areas is essential but is not possible due to financial and operational constraints. Hence, using an alternative tool is important to ensure compliance with the standards and also provides a choice for commuters to reduce their unnecessary trips in contaminated areas across the city. In this study, a stochastic framework is developed based on a Hidden Semi-Markov model (HSMM) to predict the state of PM10 particulates. Our proposed HSMM model predicts PM10 concentration for the next day, based on PM10 levels in previous days. The result of simulation shows the proposed technique achieves good accuracy in estimation of PM10. It also indicates that the model can be used for one-day ahead forecast to alert individuals in the study area which is particularly useful in situations where the information on external variables such as traffic volume is not available.]]></description>
      <pubDate>Tue, 26 Jul 2016 17:04:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1417761</guid>
    </item>
    <item>
      <title>Estimating the Process Noise Variance for Vehicle Motion Models</title>
      <link>https://trid.trb.org/View/1406298</link>
      <description><![CDATA[Vehicle motion models are employed in driver assistance systems for tracking and prediction tasks. For probabilistic decision making and uncertainty propagation, the prediction's inaccuracy is taken into account in the form of process noise. This work estimates Gaussian process noise models from measured vehicle trajectories using the expectation maximisation (EM) algorithm. The method is exemplified and the results evaluated for three commonly used motion models based on a large-scale dataset. A novel closed-form adaptation of the algorithm to a covariance matrix with Kronecker product structure, as in models for translational motion, is presented. The findings suggest that the longitudinal prediction errors feature a non-Gaussian distribution but a reasonable approximation is given by the estimated model.]]></description>
      <pubDate>Wed, 22 Jun 2016 11:59:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406298</guid>
    </item>
    <item>
      <title>Modeling Temporal Flow Assignment in Metro Networks Using Smart Card Data</title>
      <link>https://trid.trb.org/View/1406304</link>
      <description><![CDATA[Understanding passenger flow assignment patterns in a complex metro network is crucial to maintaining service reliability and developing efficient response during disruption. In reality passengers' perception of different cost attributes may vary with time. This paper focuses on quantifying the temporal variation of passenger route choice behavior and its impact on overall passenger flow assignment. In order to efficiently estimate model parameters, the authors modify a previous model to a missing data problem by introducing latent variable on route choice outcomes for each travel time observation. The revised model can be estimated using the Expectation-Maximization (EM) algorithm. The authors apply the proposed framework on Singapore's metro system and temporal grouped smart card transactions. They find that route choice coefficients vary substantially with time. The relative value of transfer time in terms of in-vehicle time ranges from 2 to 3, being higher at off-peak hours than during morning/evening peaks. The result suggests that passengers care more about total travel time during peak hours, whereas comfort (e.g., less transfer time) is of more concern to users during off-peaks. The proposed framework is general and can be applied on other networks.]]></description>
      <pubDate>Wed, 22 Jun 2016 11:59:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406304</guid>
    </item>
    <item>
      <title>Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets</title>
      <link>https://trid.trb.org/View/1367406</link>
      <description><![CDATA[As transit agencies increasingly adopt the use of Automatic Passenger Count (APC) technologies, a large amount of boarding and alighting data are being amassed on an ongoing basis. These datasets offer opportunities to infer good estimates of passenger origin–destination (OD) flows. In this study, a method is proposed to estimate transit route passenger OD flow matrices for time-of-day periods based on OD flow information derived from labor-intensive onboard surveys and the large quantities of APC data that are becoming available. The computational feasibility of the proposed method is established and its accuracy is empirically evaluated using differences between the estimated OD flows and ground-truth observations on an operational bus route. To interpret the empirical differences from the ground-truth estimates, differences are also computed when using the state-of-the-practice Iterative Proportional Fitting (IPF) method to estimate the OD flows. The empirical results show that when using sufficient quantities of boarding and alighting data that can be readily obtained from APC-equipped buses, the estimates determined by the proposed method are better than those determined by the IPF method when no or a small sample sized onboard OD flow survey dataset is available and of similar quality to those determined by the IPF method when a large sample sized onboard OD flow survey dataset is available. Therefore, the proposed method offers the opportunity to forgo conducting costly onboard surveys for the purpose of OD flow estimation.]]></description>
      <pubDate>Fri, 25 Sep 2015 16:28:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1367406</guid>
    </item>
    <item>
      <title>Moving object trajectory processing based on multi-laser sensing</title>
      <link>https://trid.trb.org/View/1354777</link>
      <description><![CDATA[There have been many researches on moving object detection and tracking. There are also great needs in trajectory analysis and scene modeling so that to provide higher knowledge to surveillance and intelligent transportation systems (ITS) application for decision making. However in crowded environment, trajectory data sets obtained through online processing contain many broken, group and fragment ones, which degrades trajectory quality, affect the performance in trajectory analysis and scene modeling. A trajectory processing algorithm is developed in this research on a multi-laser sensing system that was developed in the authors' previous work. It contains a trajectory association algorithm, where an interaction graph is built to represent the relationships of trajectories; a graph-based trajectory labeling algorithm; and an expectation maximization (EM)-based trajectory parameter optimization algorithm. Experiments are conducted using the data collected at an intersection in Beijing with promising results demonstrated.]]></description>
      <pubDate>Fri, 26 Jun 2015 13:43:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1354777</guid>
    </item>
    <item>
      <title>Identification and estimation of latent group-level-effects in infrastructure performance modeling</title>
      <link>https://trid.trb.org/View/1347365</link>
      <description><![CDATA[As in other panel data analyses, the presence of unobserved heterogeneity is a critical issue in the estimation of infrastructure performance models. In the literature, this issue has been addressed by formulating variable intercept, fixed or random effects models under the assumptions that (1) heterogeneity stems from facility/individual-level effects, and that (2) the coefficients are constant and homogeneous across the population. In contrast, we present mixture regression as a performance modeling framework. The approach relies on the assumption that the underlying population is comprised of a finite set of classes/segments in unknown proportions. The segmentation basis is latent meaning that the criteria to establish the number and type of segments are related to unobserved heterogeneity manifested in facility performance/deterioration. The segments are characterized by a set of commonly specified regression equations, which allows for the identification and estimation of coefficients, i.e., group-level effects, that differ in terms of their level-of-significance, magnitude or sign. We also derive an instance of the Expectation-Maximization Algorithm to estimate the associated parameters, and to assign facilities to the population segments. To illustrate the framework, we analyze the performance of a panel of 131 pavements from the AASHO Road Test. The results suggest both observed and unobserved sources of heterogeneity in the panel. The heterogeneity is captured by differential group-level effects, which we estimate and interpret. We also discuss how these effects can be exploited in the development of resource allocation strategies. We also compare the mixture regression model to established benchmarks.]]></description>
      <pubDate>Tue, 07 Apr 2015 11:40:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1347365</guid>
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