<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>WM Method and Its Application in Traffic Flow Modeling</title>
      <link>https://trid.trb.org/View/2282744</link>
      <description><![CDATA[Extracting fuzzy rules automatically from data is a research direction of fuzzy system and data mining. The WM method is the one of the earliest algorithms. However, it only selects the rules which have the maximal degree, so it does not consider other conflicting rules. Two other methods are presented in this paper to select fuzzy rules, and one simulation data and one field traffic flow data are used to compare all three methods. The comparative results show that the weighted mean method has the best robustness and error-tolerance, which is more suitable for extracting rules from the real data with noise.]]></description>
      <pubDate>Mon, 16 Sep 2024 08:55:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282744</guid>
    </item>
    <item>
      <title>Maximum Likelihood Method of Speed Estimation from Single Loop Outputs</title>
      <link>https://trid.trb.org/View/2283348</link>
      <description><![CDATA[The importance of real-time speed estimation is undoubtedly significant for traveler information and traffic management systems. Unfortunately, the most common form of traffic detector, the single loop detector, is incapable of providing speed measurements. This paper presents a new method of speed estimation from single loop detector data by using the Maximum Likelihood Method (MLM). On the assumption that a vehicle's velocity obeys lognormal distribution in every sample, the average speed of each sample was estimated using MLM. The algorithm of the proposed method is implemented and evaluated using the field data from urban expressways in Beijing. The results show that the proposed method has more excellent estimation accuracy than the conventional method.]]></description>
      <pubDate>Fri, 10 May 2024 16:51:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2283348</guid>
    </item>
    <item>
      <title>A barrier to the promotion of app-based ridesplitting: Travelers’ ambiguity aversion in mode choice</title>
      <link>https://trid.trb.org/View/2339061</link>
      <description><![CDATA[Ridesplitting, despite having been around for years, accounts for a low proportion of overall transportation modes. With the development of technology, app-based ridesplitting is witnessing new opportunities but its usage rate still remains poor. Intuitively, travelers’ aversion to the unreliable travel time inherent of ridesplitting may stop them from choosing it. Many studies have explored the role of risk aversion, but fewer focus on ambiguity aversion. In this study, the authors aim to understand travelers’ preferences for information ambiguity in shaping their choice behavior of using app-based ridesplitting. Therefore, the authors built up choice models of this thought to describe travelers’ behaviors in ridesplitting. Based on the models, a two-stage framework was established including field data research and experimental research testing the existence of ambiguity aversion. In the first stage, a data set containing detailed information on nearly 2.2 million trips in Chengdu, China was utilized. By the maximum likelihood method, the fitting level of unreliability model is better and the coefficient of ambiguity attitude shows the existence of ambiguity aversion. In the second stage, a stated choice experiment was designed with a variety of choice tasks to reproduce real-life scenarios. Significantly fewer ridesplitting cases happen in the ambiguous information treatment than in the certain information treatment, but risky information does not reduce the number of choosing ridesplitting significantly. The results undergo cross-validation with field research data to ensure their reliability. This study leverages travelers’ aversion to ambiguity to explain their reluctance towards ridesplitting. The findings have strong implications for relevant service platforms to prompt more travelers towards participating in ridesplitting, for example enhancing the provision of travel information to address the concerns of potential users.]]></description>
      <pubDate>Mon, 26 Feb 2024 15:42:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2339061</guid>
    </item>
    <item>
      <title>OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions</title>
      <link>https://trid.trb.org/View/2240817</link>
      <description><![CDATA[Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between subsequent choices, oversimplifying the scheduling process. In this paper the authors introduce OASIS, an integrated framework to simulate activity schedules by considering all choice dimensions simultaneously. The authors present a methodology for the estimation of the parameters of an activity-based model from historic data, allowing for the generation of realistic and consistent daily mobility schedules. The estimation process has two main elements: (i) choice set generation, using the Metropolis-Hasting algorithm, and (ii) estimation of the maximum likelihood estimators of the parameters. The authors test their approach by estimating parameters of multiple utility specifications for a sample of individuals from a Swiss nationwide travel survey, and evaluating the output of the OASIS model against realised schedules from the data. The results demonstrate the ability of the new framework to simulate realistic distributions of activity schedules, and estimate stable and significant parameters from historic data that are consistent with behavioural theory. This work opens the way for future developments of activity-based models, where a great deal of constraints can be explicitly included in the modelling framework, and all choice dimensions are handled simultaneously.]]></description>
      <pubDate>Mon, 25 Sep 2023 14:46:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2240817</guid>
    </item>
    <item>
      <title>A Novel Cascade Path Planning Algorithm for Autonomous Truck-Trailer Parking</title>
      <link>https://trid.trb.org/View/1993908</link>
      <description><![CDATA[One of the most challenging tasks for truck drivers is maneuvering the truck-trailer system in different parking scenarios. This article presents a novel path planning approach for truck-trailer parking, where a realistic and deterministic parking behavior model, Iterative Analytical Method (IAM), is proposed and combined with Closed-Loop Rapidly Exploring Random Tree (CL-RRT) approach in a cascade path planning. Cascade path planning approach combining CL-RRT with the iterative analytical method (IAM) mimicking real-world parking practice enables the generation of both kinematically feasible and deterministic parking maneuvers with obstacle avoidance. For evaluation, different parking scenarios are generated and selected through a developed case generation tool. The performance of the proposed path planning approach is evaluated through MATLAB simulations. The results achieved a noticeable success with a high rate of generated feasible maneuvers for parking.]]></description>
      <pubDate>Fri, 30 Sep 2022 14:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1993908</guid>
    </item>
    <item>
      <title>Simulation Modeling for Evaluation of Efficiency of Observed Ship Coordinates</title>
      <link>https://trid.trb.org/View/1977364</link>
      <description><![CDATA[Simulation computer modeling was used to evaluate the efficiency of the vessel’s observed coordinates using the mixed laws of distribution errors of the first and second type for lines of position (LOP). Simulation modeling showed good convergence of evaluation of efficiency calculated by analytical expressions and obtained by simulation. A graphical depiction of the observed points’ deviation relative to the mathematical expectation in the case of distribution of LOP errors of both types according to mixed laws is obtained by the method of least squares and the method of maximum likelihood estimation.]]></description>
      <pubDate>Wed, 24 Aug 2022 15:02:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977364</guid>
    </item>
    <item>
      <title>Maximum Likelihood Estimation of Probe Vehicle Penetration Rates and Queue Length Distributions From Probe Vehicle Data</title>
      <link>https://trid.trb.org/View/1993968</link>
      <description><![CDATA[Queue length estimation plays an important role in traffic signal control and performance measures of signalized intersections. Traditionally, queue lengths are estimated by applying the shockwave theory to loop detector data. In recent years, the tremendous amount of vehicle trajectory data collected from probe vehicles such as ride-hailing vehicles and connected vehicles provides an alternative approach to queue length estimation. To estimate queue lengths cycle by cycle, many existing methods require the knowledge of the probe vehicle penetration rate and queue length distribution. However, the estimation of the two parameters has not been well studied. This paper proposes a maximum likelihood estimation method that can estimate the parameters from historical probe vehicle data. The maximum likelihood estimation problem is solved by the expectation-maximization (EM) algorithm iteratively. Validation results show that the proposed method could estimate the parameters accurately and thus enable the existing methods to estimate queue lengths cycle by cycle.]]></description>
      <pubDate>Fri, 22 Jul 2022 16:07:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1993968</guid>
    </item>
    <item>
      <title>Handover Count Based MAP Estimation of Velocity With Prior Distribution Approximated via NGSIM Data-Set</title>
      <link>https://trid.trb.org/View/1948128</link>
      <description><![CDATA[In this paper, the authors propose a maximum-a-posteriori probability (MAP) based velocity estimation technique in which the prior distribution is defined by current location of the user. Motivation of this work is to improve accuracy of the existing velocity estimation techniques which are either solely based on cellular network measurements or location specific information. Their objective is to exploit both cellular measurements and location information in Bayesian sense; thus, to jointly address the critical applications of mobility management in Heterogeneous-Networks (HetNets), and intelligent transportation system. Here they assume that the Next Generation Simulation (NGSIM) data set for velocity is available at the current location and can be utilized to approximate the prior distribution. Additional information in form of prior distribution function is then exploited to improve the minimum variance unbiased (MVU) estimate of velocity which is based on handover count measurements. Since MVU estimate is a random variable, they first formulate its density function parameterized over the actual velocity. Next, they follow Bayesian approach to accommodate both prior distribution and parametric density function in deriving posterior density function of velocity. Finally, the authors derive expression of the MAP estimator considering various standard distribution functions which best fit to the density function obtained from NGSIM data set. In order to quantify the quality of estimate, they derive its variance and the corresponding Cramer-Rao-bound (CRB) on the minimum error variance. Numerical results demonstrate that the proposed estimator which incorporates NGSIM data set is asymptotically efficient and outperforms other classical handover count based estimation techniques.]]></description>
      <pubDate>Mon, 27 Jun 2022 17:19:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1948128</guid>
    </item>
    <item>
      <title>Parameter estimation of the macroscopic fundamental diagram: A maximum likelihood approach</title>
      <link>https://trid.trb.org/View/1949985</link>
      <description><![CDATA[This paper extends the Stochastic Method of Cuts (SMoC) to approximate the Macroscopic Fundamental Diagram (MFD) of urban networks and uses Maximum Likelihood Estimation (MLE) method to estimate the model parameters based on empirical data from a corridor and 30 cities around the world. For the corridor case, the estimated values are in good agreement with the measured values of the parameters. For the network datasets, the results indicate that the method yields satisfactory parameter estimates and graphical fits for roughly 50% of the studied networks, where estimations fall within the expected range of the parameter values. The satisfactory estimates are mostly for the datasets which (i) cover a relatively wider range of densities and (ii) the average flow values at different densities are approximately normally distributed similar to the probability density function of the SMoC. The estimated parameter values are compared to the real or expected values and any discrepancies and their potential causes are discussed in depth to identify the challenges in the MFD estimation both analytically and empirically. In particular, the authors find that the most important issues needing further investigation are: (i) the distribution of loop detectors within the links, (ii) the distribution of loop detectors across the network, and (iii) the treatment of unsignalized intersections and their impact on the block length.]]></description>
      <pubDate>Wed, 25 May 2022 09:32:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1949985</guid>
    </item>
    <item>
      <title>Estimation of critical gap of U-turns at uncontrolled median openings considering Iran’s driver behavior</title>
      <link>https://trid.trb.org/View/1904229</link>
      <description><![CDATA[The critical gap is typically used to determine the capacity at unsignalized intersections. A few studies have been carried out on the estimation of the U-turn critical gap at uncontrolled median openings. In this research, the critical gap was calculated using four well-known methods, Maximum Likelihood Method (MLM), Ashworth’s, Raff, and acceptance curve for different types of U-turn vehicles at four uncontrolled median openings in Iran. The data were collected using video recording. The results showed that MLM is the best method for calculating the critical gap according to the conditions of Iran. According to this method, the average critical gap was obtained 3.23 seconds, which was very low compared to other countries. Moreover, the result of MLM showed that the critical gap for Heavy Vehicles (HVs) drivers was almost less than car drivers, indicating the behavior of HVs drivers was more aggressive.]]></description>
      <pubDate>Wed, 02 Feb 2022 09:23:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1904229</guid>
    </item>
    <item>
      <title>A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression</title>
      <link>https://trid.trb.org/View/1897691</link>
      <description><![CDATA[In the last few years, the success of Machine Learning (ML) algorithms has led to the extension of their applications to areas such as transport planning. One of the main tasks within transport planning is the analysis of transport demand. To do so, it is necessary to analyse the way in which users make their decisions about the trips they make and, therefore, be able to predict the number of passengers on the transport network in relation to respect to interventions made on the transport system. Consequently, transport policies and plans can be evaluated according to the behaviour of the passengers. Discrete choice models based on random utility maximization have been developed over the last four decades, becoming the canonical tool for transport demand analysis. Nowadays, the use of ML methods could provide an alternative to discrete choice models, as they reduce the need for the analyst to specify the functional expression of these models and achieve a higher level of accuracy in their predictions. A Python software package called PyKernelLogit was developed to apply a ML method called Kernel Logistic Regression (KLR) to the problem of predicting the transport demand. This package allows the user to specify a set of models using KLR and the estimation of those using a Penalized Maximum Likelihood Estimation procedure. Moreover, this tool also provides a set of indicators for goodness of fit and the application of model validation techniques. Finally, it allows to obtain the willingness to pay or value of time indicators commonly used in transport planning.]]></description>
      <pubDate>Wed, 22 Dec 2021 14:14:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1897691</guid>
    </item>
    <item>
      <title>Modeling hurricane evacuation behavior using a dynamic discrete choice framework</title>
      <link>https://trid.trb.org/View/1860551</link>
      <description><![CDATA[Predicting evacuation-related choices of households during a hurricane is of paramount importance to any emergency management system. Central to this problem is the identification of socio-demographic factors and hurricane characteristics that influence an individual’s decision to stay or evacuate. However, decision makers in such conditions do not make a single choice but constantly evaluate current and anticipated conditions before opting to stay or evacuate. The authors model this behavior using a finite-horizon dynamic discrete choice framework in which households may choose to evacuate or wait in time periods prior to a hurricane’s landfall. In each period, an individual’s utility depends not only on his/her current choices and the present values of the influential variables, but also involves discounted expected utilities from future choices should one decide to postpone their decision to evacuate. Assuming generalized extreme value (GEV) errors, a nested algorithm involving a dynamic program and a maximum likelihood method is used to estimate model parameters. Panel data on households affected by Hurricane Gustav, which made landfall in Louisiana on 1 September 2008, was fused with the National Hurricane Center’s forecasts on the trajectory and intensity for the case study in the paper.]]></description>
      <pubDate>Wed, 22 Sep 2021 11:54:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1860551</guid>
    </item>
    <item>
      <title>Design of Digital Communications for Strong Phase Noise Channels</title>
      <link>https://trid.trb.org/View/1862948</link>
      <description><![CDATA[To meet the requirements of beyond 5G networks, the significant amount of unused spectrum in sub-TeraHertz frequencies is contemplated for high-rate wireless communications. Yet, the performance of sub-TeraHertz systems is severely degraded by strong oscillator phase noise. The authors investigate in this paper the design of digital communications robust to phase noise. This problem is addressed in three steps: the characterization of the phase noise channel, the design of the optimum receiver, and the optimization of the modulation scheme. This paper proposes a joint performance and implementation optimization. First, the authors address the design of the demodulation scheme for phase noise channels and propose the polar metric, a soft-decision rule for symbol detection. It is shown that performance gains are achieved for coded and uncoded systems with valuable complexity reductions of the receiver. Second, they investigate the optimization of the modulation scheme for phase noise. The authors demonstrate that using a constellation defined upon a lattice in the amplitude-phase domain leads to significant performance gains and a low-complexity implementation. Thereupon, they propose the Polar-QAM scheme with efficient binary labeling and demodulation. Numerical simulation results show that the proposed modulation and demodulation schemes offer valuable solutions to achieve high-rate communications on systems strongly impaired by phase noise.]]></description>
      <pubDate>Fri, 27 Aug 2021 14:58:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1862948</guid>
    </item>
    <item>
      <title>A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand</title>
      <link>https://trid.trb.org/View/1854293</link>
      <description><![CDATA[This paper proposes a method to recover an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation method based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information is available but the truncation process can be simulated. The proposed method is compared with the ubiquitous maximum likelihood estimation (MLE) method in a variety of synthetic validation experiments, where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slightly misspecified MLE. The practical application of this method is then demonstrated via a pair of case studies in which the proposed detruncation method is used alongside a car-sharing service simulator to estimate demand for round-trip car-sharing services in the Boston and New York metropolitan areas.]]></description>
      <pubDate>Thu, 19 Aug 2021 16:26:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1854293</guid>
    </item>
    <item>
      <title>A hidden Markov model for the estimation of correlated queues in probe vehicle environments</title>
      <link>https://trid.trb.org/View/1852706</link>
      <description><![CDATA[Queue length estimation is critical for traffic signal control and performance measures. With the development of connected vehicle technologies and the popularization of ride-hailing services, probe vehicle data are now being collected on a large scale. Some studies have shown that queue lengths can be estimated using only probe vehicle data. The relevant literature usually assumes the queue lengths in different traffic signal cycles are independent and identically distributed or treats the queues independently. However, in the real world, the queue lengths in different cycles might be correlated. For instance, when there exists an overflow queue, the queue length in the following cycle is correlated with the queue length in the previous cycle. In fact, the correlation of different cycles can provide additional information and thus improve the queue length estimation accuracy. In this paper, the authors model such queueing processes in probe vehicle environments using a hidden Markov model (HMM), where the queue length in each cycle is a hidden state, and the observed pattern of probe vehicles is an observation. Based on the HMM, the authors propose two novel cycle-by-cycle queue length estimation methods. In the case where the parameters of the HMM are unknown, the authors also provide an algorithm that can estimate the parameters from historical probe vehicle data. Validation results show that the proposed cycle-by-cycle queue length estimation methods outperform the existing methods, and the parameter learning algorithm can estimate the parameters adequately.]]></description>
      <pubDate>Fri, 30 Jul 2021 12:36:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1852706</guid>
    </item>
  </channel>
</rss>