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    <title>Transport Research International Documentation (TRID)</title>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Hierarchical Scheme for Vehicle Make and Model Recognition</title>
      <link>https://trid.trb.org/View/1866583</link>
      <description><![CDATA[A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.]]></description>
      <pubDate>Mon, 26 Jul 2021 15:48:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1866583</guid>
    </item>
    <item>
      <title>A vehicle-by-vehicle approach to assess the impact of variable message signs on driving behavior</title>
      <link>https://trid.trb.org/View/1838291</link>
      <description><![CDATA[Variable Message Signs (VMS) provide real-time information on traffic conditions, making it possible to guide drivers through electronic signs along the road. Relevant literature has proved VMS to be effective, especially for diverting traffic during incidents in the highway or inducing a speed reduction. Previous efforts, however, usually involve off-highway experiences, including the use of simulators or stated preference surveys, or the measurement of aggregate values of traffic through technologies that are prone to a higher failure rate, such as loop detectors. For bridging this gap, in this research, the authors propose a novel vehicle-by-vehicle approach (VBV), that differentiate by vehicle type, to assess the impact of VMS on drivers’ road behavior patterns along a section of a Chilean urban highway during risky situations. In addition to the messaging information, the authors use full traffic data obtained from free-flow gates equipped with automatic vehicle identification (AVI) technology. The authors conduct statistical analyses to study two potential messaging-induced behavioral changes, namely speed reduction and lane changes. For the speed reduction behavior, in 87.50% of the studied messages, the results indicate that the messages failed to induce the desired change in behavior. This value decreases to 71.85% for lane changes. The results indicate that heavy vehicle drivers and low-mileage drivers are more likely to follow lane change messages.]]></description>
      <pubDate>Mon, 26 Apr 2021 09:31:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1838291</guid>
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    <item>
      <title>Analysis of overtaking patterns of Indian drivers with data collected using a LiDAR</title>
      <link>https://trid.trb.org/View/1737876</link>
      <description><![CDATA[Vehicle overtaking is a complex maneuver on highways and high-speed arterials. When overtaking occurs in heterogeneous traffic conditions, it becomes even more challenging due to the variety of different vehicle sizes and operating characteristics. This study analyzes overtaking behavior on Indian highways under high vehicle heterogeneity. Data were collected using LiDAR and video cameras from an instrumented vehicle. Overtaking times for both sides of overtaking and for different types of vehicles were analyzed. This study introduces a new variable, “excess distance,” to understand overtaking. The results show that on divided four-lane roads, the side of overtaking plays an important role in determining overtaking behavior. On undivided two-lane roads, the type of overtaken vehicle is found to be significant. It is observed, as expected, that while overtaking at higher speeds, overtaking vehicles maintained greater excess distances with the overtaken vehicle. However, data shows that the rate of change of excess distance depends upon the type of road and the type of overtaking vehicle. The analysis also shows that overtaking behavior on undivided roads is affected by the presence of oncoming vehicles and the type of overtaken vehicle.]]></description>
      <pubDate>Fri, 02 Oct 2020 10:00:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1737876</guid>
    </item>
    <item>
      <title>Empirical validation of vehicle type-dependent car-following heterogeneity from micro- and macro-viewpoints</title>
      <link>https://trid.trb.org/View/1679292</link>
      <description><![CDATA[This paper validates the vehicle type-dependent car-following heterogeneity from micro- and macro-aspects by using Next Generation Simulation (NGSIM) trajectory data. Regarding the micro-viewpoint, traffic conflict analysis techniques and three surrogate safety indexes (i.e., time headway (TH), time to collision (TTC) and safety margin (SM)) are introduced to evaluate the safety performance during ‘steady’ car following. When driving or following a larger vehicle, Z-test results show that TH, TTC and SM all become higher significantly, that is, the driver’s accepted risk tends to be lower. From the macro-viewpoint, with the aggregated traffic data, a flexible Lagrangian fundamental diagram is calibrated by a bi-level optimization procedure. These calibrated curves illustrate that during close car following process with the same speed, the driver generally keeps a higher gap distance with the leader when following or driving a larger vehicle. Therefore, the consistent findings contribute to the conclusive results about the vehicle type-dependent car-following heterogeneity.]]></description>
      <pubDate>Wed, 19 Feb 2020 17:13:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1679292</guid>
    </item>
    <item>
      <title>Vehicle modeling for the analysis of the response of detectors based on inductive loops</title>
      <link>https://trid.trb.org/View/1652392</link>
      <description><![CDATA[Magnetic loops are one of the most popular and used traffic sensors because of their widely extended technology and simple mode of operation. Nevertheless, very simple models have been traditionally used to simulate the effect of the passage of vehicles on these loops. In general, vehicles have been considered simple rectangular metal plates located parallel to the ground plane at a certain height close to the vehicle chassis. However, with such a simple model, it is not possible to carry out a rigorous study to assess the performance of different models of vehicles with the aim of obtaining basic parameters such as the vehicle type, its speed or its direction in traffic. For this reason and because computer simulation and analysis have emerged as a priority in intelligent transportation systems (ITS), this paper aims to present a more complex vehicle model capable of characterizing vehicles as multiple metal plates of different sizes and heights, which will provide better results in virtual simulation environments. This type of modeling will be useful when reproducing the actual behavior of systems installed on roads based on inductive loops and will also facilitate vehicle classification and the extraction of basic traffic parameters.]]></description>
      <pubDate>Fri, 27 Sep 2019 09:58:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1652392</guid>
    </item>
    <item>
      <title>An empirical study on aggregation of alternatives and its influence on prediction in car type choice models</title>
      <link>https://trid.trb.org/View/1627876</link>
      <description><![CDATA[Assessing and predicting car type choices are important for policy analysis. Car type choice models are often based on aggregate alternatives. This is due to the fact that analysts typically do not observe choices at the detailed level that they are made. In this paper, we use registry data of all new car purchases in Sweden for two years where cars are observed by their brand, model and fuel type. However, the choices are made at a more detailed level. Hence, an aggregate (observed) alternative can correspond to several disaggregate (detailed) alternatives. We present an extensive empirical study analyzing estimation results, in-sample and out-of-sample fit as well as prediction performance of five model specifications. These models use different aggregation methods from the literature. We propose a specification of a two-level nested logit model that captures correlation between aggregate and disaggregate alternatives. The nest specific scale parameters are defined as parameterized exponential functions to keep the number of parameters reasonable. The results show that the in-sample and out-of-sample fit as well as the prediction performance differ. The best model accounts for the heterogeneity over disaggregate alternatives as well as the correlation between both disaggregate and aggregate alternatives. It outperforms the commonly used aggregation method of simply including a size measure.]]></description>
      <pubDate>Mon, 22 Jul 2019 20:00:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1627876</guid>
    </item>
    <item>
      <title>Joined Car Ownership and Car Type Preference Model Considering Social Involvement, Regional Effect and Environmental Concern</title>
      <link>https://trid.trb.org/View/1572624</link>
      <description><![CDATA[In this study, the authors investigate young people’s attitude to own a car by, their car purchase desire and attitude. Their dependent variable is the desire to purchase any type of car as well as specific car types, such as small cars, sports cars or hybrid cars. The authors focus on Japanese people aged 18 to 25 and obtain a sample from Tokyo residents, Kyoto as well as people living in rural areas of Japan. As expected they find significant differences according to the city or rural living context. They control further for a number of attitudinal aspects that have been found significant in previous studies. The authors find that instrumental values of the car are most important though (more expensive) foreign cars appear to be still desired. Their main focus and contribution is on the inclusion of car’s “usefulness to avoid pollution” and social involvement in their analysis. They find some weak evidence  for  an “environmental dilemma” where pollution in fact encourages more car usage in order to avoid this pollution. Regarding social involvement they find that those spending more time alone with online activities,  have less desire  to  purchase  cars. The authors discuss that  there  might be cyclic  relationships which call for careful discussion on the implication of car ownership reduction in particular in rural areas.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1572624</guid>
    </item>
    <item>
      <title>Consumer willingness to pay for vehicle attributes: What do we know?</title>
      <link>https://trid.trb.org/View/1549457</link>
      <description><![CDATA[As standards for vehicle greenhouse gas emissions and fuel economy have become more stringent, concerns have arisen that the incorporation of fuel-saving technologies may entail tradeoffs with other vehicle attributes important to consumers such as acceleration performance. Assessing the effects of these tradeoffs on consumer welfare requires estimates of both the degree of the tradeoffs, and consumer willingness to pay (WTP) for the foregone benefits. This paper has two objectives. The first is to review recent literature that presents, or can be used to calculate, marginal WTP (MWTP) for vehicle attributes to describe the attributes that have been studied and the estimated MWTP values. The authors found 52 U.S.-focused papers with sufficient data to calculate WTP values for 142 different vehicle attributes, which they organized into 15 general groups of comfort, fuel availability, fuel costs, fuel type, incentives, model availability, non-fuel operating costs, performance, pollution, prestige, range, reliability, safety, size, and vehicle type. Measures of dispersion around central MWTP values typically show large variation in MWTP values for attributes. The authors explore factors that may contribute to this large variation via analysis of variance (ANOVA) and find that, although most have statistically significant effects, they account for only about one third of the observed variation. Case studies of papers that provide estimates from a variety of model formulations and estimation methods suggest that decisions made by researchers can strongly influence MWTP estimates. The paper’s second objective is to seek consensus estimates for WTP for fuel cost reduction and increased acceleration performance. Meta-analysis of MWTP for reduced fuel cost indicates that estimates based on revealed vs. stated preference data differ, as do estimates from models that account for endogeneity and those that do not. The authors find greater consistency in estimates of MWTP for acceleration despite substantial uncertainty about the overall mean. The authors conclude with recommendations for improving the understanding of consumers’ MWTP for vehicle attributes.]]></description>
      <pubDate>Fri, 05 Oct 2018 09:27:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1549457</guid>
    </item>
    <item>
      <title>Comparison of Speed Control Bumps and Humps according to Whole-Body Vibration Exposure</title>
      <link>https://trid.trb.org/View/1522732</link>
      <description><![CDATA[One of the easiest and most efficient ways to control vehicle speeds is to create undulations perpendicular to the axis of the road. The types of undulations especially used for speed management on urban road networks are called speed control bumps (SCBs) and speed control humps (SCHs) according to their width. In general, the undulation geometry is a very important factor in changing the shock levels to which passing vehicles are exposed, and accordingly, in reducing the vehicle speeds. This study compares SCBs and SCHs with regard to human health risks using the whole-body vibration (WBV) components (VDV, Sₑ, and R) to which vehicle drivers are exposed while passing over the undulations. Because SCBs and SCHs are usually preferred for use in urban road networks, experimental vibration measurements are conducted at 20, 30, 40, and 50  km/h vehicle speeds. In order to demonstrate the effects of different vehicle types, vibration measurements are repeated in the same driver and undulation geometries with sedan, hatchback, and station wagon vehicles for each measurement speed. The evaluations use standard evaluation methods which are frequently preferred in the world in WBV analysis. Using these methods, vehicle type and vehicle speed effects are reciprocally evaluated considering SCB and SCH geometries with equal heights. Use of the SCHs appears to be more suitable for human health in traffic speed management.]]></description>
      <pubDate>Mon, 27 Aug 2018 14:05:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/1522732</guid>
    </item>
    <item>
      <title>A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data</title>
      <link>https://trid.trb.org/View/1505686</link>
      <description><![CDATA[There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, the authors propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, the authors extract some important mathematical and experimental features from these data. Afterwards, the authors propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, the authors develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.]]></description>
      <pubDate>Tue, 17 Apr 2018 17:16:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1505686</guid>
    </item>
    <item>
      <title>Multifactor Analysis of Online Reputation of Selected Car Brands</title>
      <link>https://trid.trb.org/View/1472187</link>
      <description><![CDATA[The paper discusses the issue of online reputation, more specifically the ways and methods of its measurements in selected entities operating in the automotive sector. A thorough multifactor analysis of reputation in the virtual world of the Internet was conducted on a specific sample of entities/ subjects – selected car brands operating on a central European market. Using a careful statistical testing relationships between factors were examined in order to identify and describe basic facts affecting online reputation of those entities in the hyper competitive market environment of the Internet. The findings identified by the analysis conducted on the selected part of the global market, can be effectively used in any market for the purpose of increasing competitiveness of selected entities from (not only) automotive industry.]]></description>
      <pubDate>Tue, 29 Aug 2017 10:13:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1472187</guid>
    </item>
    <item>
      <title>Comparative Investigation on the Aerodynamic Effects of Combined Use of Underbody Drag Reduction Devices Applied to Real Sedan</title>
      <link>https://trid.trb.org/View/1479742</link>
      <description><![CDATA[To reduce the aerodynamic drag, the performance of the underbody aerodynamic drag reduction devices was evaluated based on the actual shape of a sedan-type vehicle. An undercover, under-fin, and side air dam were used as the underbody aerodynamic drag reduction devices. In addition, the effects of the interactions based on the combination of the aerodynamic drag reduction devices were investigated. A commercial sedan-type vehicle was selected as a reference model and its shape was modeled in detail. Aerodynamic drag was analyzed by computational fluid dynamics at a general driving speed on highway of 120 km/h. The undercover reduced the slipstream area through the attenuation of the longitudinal vortex pair by enhancing the up-wash of underflow, thereby reducing the aerodynamic drag by 8.4 %. The under-fin and side air dam showed no reduction in aerodynamic drag when they were solely attached to the actual complex shape of the underbody. Simple aggregation of the effects of aerodynamic drag reduction by the individual device did not provide the accurate performance of the combined aerodynamic drag reduction devices. An additional aerodynamic drag reduction of 2.1 % on average was obtained compared to the expected drag reduction, which was due to the synergy effect of the combination.]]></description>
      <pubDate>Tue, 29 Aug 2017 10:13:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1479742</guid>
    </item>
    <item>
      <title>A time-dependent stated preference approach to measuring vehicle type preferences and market elasticity of conventional and green vehicles</title>
      <link>https://trid.trb.org/View/1467742</link>
      <description><![CDATA[The diversity of new vehicle technology and fuel markets, the governments’ sustainable call to reduce energy consumption and air pollution lead to a change in the personal vehicle market. Considering the impact of these factors, a stated preference survey approach is adopted to analyze household future preferences for gasoline, hybrid electric, and battery electric vehicles in a dynamic marketplace. The stated choice experiment places respondents in a nine-year hypothetical time window with dynamically changing attributes including vehicle purchasing price, fuel economy, recharging range, and fuel price. A web-based survey was performed during 2014 in the state of Maryland. The collected data include household social-demographics, primary vehicle characteristics, and vehicle purchasing preferences of 456 respondents during the year of 2014–2022. Mixed Multinomial logit (MMNL) models are employed to predict vehicle preferences based on households’ socio-demographics and vehicle characteristics. The estimation results show that young people are more likely to buy vehicles with new technology, especially battery electric vehicles (BEV). Women with a high education level (bachelor degree or higher) prefer to choose hybrid electric vehicle (HEV) while men with a high education level are more likely to buy BEV. The estimated vehicle market elasticities with respect to vehicle price are from -1.1 to -1.8 for HEV and BEV, higher than those for gasoline vehicles from -0.6 to -1.0. The vehicle market cross-elasticities estimated by MMNL models range from 0.2 to 0.6. In addition, willingness to pay (WTP) of vehicle characteristics estimated by MMNL models provide a good understanding of household future vehicle preferences.]]></description>
      <pubDate>Tue, 27 Jun 2017 16:11:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1467742</guid>
    </item>
    <item>
      <title>Size matters: How vehicle body type affects consumer preferences for electric vehicles</title>
      <link>https://trid.trb.org/View/1467735</link>
      <description><![CDATA[Electric vehicles (EVs) hold great promise for reducing greenhouse gas emissions, yet achieving their environmental benefits depends on greater market uptake. While a growing body of literature has sought to offer information on consumer stated preferences for EVs, to date no research has examined how preferences for hybrid, plug-in hybrid, and battery electric vehicles are shaped by vehicle body size or type. The automobile market is differentiated with vehicle attributes that respond to heterogeneous consumer demands. The authors hypothesize that each bundle of attributes as it relates to vehicle body size also shapes demand for EVs. Using a large primary dataset, the authors segment respondents according to their preferred next vehicle body type (economy, intermediate, full-size sedan, luxury, minivan, sport utility, and pickup). Multivariate analysis of variance (MANOVA) results show significant differences in the socioeconomic, demographic, and psychological profile of consumers across the seven vehicle segments. From this, discrete choice models detail how vehicle type plays a significant role in the choicemaking behaviour of potential EV consumers. While factors like age, education, and the importance of fuel economy and reduced or eliminated emissions generally play a consistent role in improving the utility of EVs, the authors' results also reveal significant heterogeneity in choice of powertrain across vehicle segments, with luxury and pickup buyers among the most distinct. The results offer useful information for marketing, policy, and research.]]></description>
      <pubDate>Tue, 27 Jun 2017 16:10:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1467735</guid>
    </item>
    <item>
      <title>Learning Discriminative Pattern for Real-Time Car Brand Recognition</title>
      <link>https://trid.trb.org/View/1376385</link>
      <description><![CDATA[In this paper, the authors study the problem of recognizing car brands in surveillance videos, cast it as an image classification problem, and propose a novel multiple instance learning method, named Spatially Coherent Discriminative Pattern Learning, to discover the most discriminative patterns in car images. The learned discriminative patterns can effectively distinguish cars of different brands with high accuracy and efficiency. The experimental results demonstrate that the authors' method is significantly superior to recent image classification methods on this problem. The proposed method is able to deliver an end-to-end real-time car recognition system for video surveillance. Moreover, the authors construct a large and challenging car image data set, consisting of 37 195 real-world car images from 30 brands, which could serve as a standard benchmark in this field and be used in various related research communities.]]></description>
      <pubDate>Mon, 29 Feb 2016 16:54:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1376385</guid>
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