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    <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" />
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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>
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    <item>
      <title>Research on Optimization of Urban Traffic Microcirculation System</title>
      <link>https://trid.trb.org/View/1990411</link>
      <description><![CDATA[An urban traffic microcirculation system plays an important role in an urban transportation system. The research on optimization of urban traffic microcirculation systems saves resources and creates favorable conditions for daily traveling. First, this paper summarizes the current research situation of the branch roads and the gain and loss of the construction of urban traffic microcirculation systems. Second, ten evaluation indexes of traffic conditions are presented from three aspects: traffic flow running state, branch roads technical characteristics, and public transportation coverage level using an analytic hierarchy process. Finally, the traffic microcirculation system of Nanjing City center is taken as a research subject. Through the establishment of the evaluation index system, suggestions of optimizing the urban traffic microcirculation system are put forward in this paper.]]></description>
      <pubDate>Thu, 23 Feb 2023 17:08:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990411</guid>
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    <item>
      <title>Impacts of Technological Progress, Structural Adjustment on Energy-Related Carbon Emissions Intensity in Logistics Industry: Empirical Research on Beijing-Tianjin-Hebei Region</title>
      <link>https://trid.trb.org/View/1990410</link>
      <description><![CDATA[To achieve integrated green development of logistics industry in the Beijing-Tianjin-Hebei area, the traditional logarithmic mean Divisia index (LMDI) model is refined. Focusing on technological progress and structural adjustment effect, influencing factors of carbon emissions intensity of logistics industry in the Beijing-Tianjin-Hebei region and the contribution of each province and cities to total carbon emissions intensity in logistics industry were analyzed. Results show that the contribution rate of technological progress, structural upgrading, and regional economic development to the decrease of carbon emission intensity were 89.2%, 5.8%, and 5.0%, respectively. Integrated transportation structure was the main factor to increase carbon emission intensity. Beijing, Tianjin, and Hebei played a positive role in reducing the total logistics carbon emission intensity, and Tianjin has the greatest contribution to the intensity decline. It is important for the Beijing-Tianjin-Hebei region to develop differentiated emissions abatement policies and implement energy-saving emission abatement responsibilities based on the characteristics of logistics industry.]]></description>
      <pubDate>Thu, 23 Feb 2023 13:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990410</guid>
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    <item>
      <title>Investigating the Factors Influencing Driving Risk Using Driving Experimental Data</title>
      <link>https://trid.trb.org/View/1990409</link>
      <description><![CDATA[Many factors impact driving risk; managing these factors can improve traffic safety. This study investigates significant factors, including driving behavior, critical incident event (CIE) factors, environment and time conditions, driver demographic information, and driver risk level. Thirty drivers participated in a field driving experiment for data collection. Each participant completed one 550 km driving test along the expressway from Wuhan to Xiangyang. During the naturalistic driving test, all abovementioned measurements were recorded to build the CIEs database. A K-means cluster analysis method was used to classify CIEs according to their risk. Besides, logistic models were developed to examine the contributing factors influencing driving risk of CIEs. Results indicated that six main factors were identified and considered as significant, including average deceleration, CIE type, CIE reason, weather, age, and driving experience. The findings of this study can help transportation planners and engineers understand factors influencing driving risk under the naturalistic driving.]]></description>
      <pubDate>Thu, 23 Feb 2023 13:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990409</guid>
    </item>
    <item>
      <title>Traveler Activity Analysis of Typical Buildings: Based on Cell Phone Signaling Data</title>
      <link>https://trid.trb.org/View/1990408</link>
      <description><![CDATA[Traveler activity analysis is essential to traffic organization and urban planning. Different types of buildings such as residential units, shopping malls, and core business district possess different features. This paper applies continuous, complete, and high-quality raw cell phone signaling data to extract both temporal and spatial features of different buildings divided by different trip modes. Based on the cell phone signaling row data of 92 consecutive days (August, September, and October 2017) in Suzhou, China, visualizations of hotspots of high-frequency traveler activity were plotted. Then, features of trips provide a temporal and spatial early warning recommendation. Both the attraction and production volume of certain building influence regions are calculated to predict the short-term flow development trend based on a time series method to better avoid crashes and trampling accidents.]]></description>
      <pubDate>Thu, 23 Feb 2023 13:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990408</guid>
    </item>
    <item>
      <title>Analysis on the Logistics Policy Impact of Energy Saving and Emission Reduction: A System Dynamics Approach</title>
      <link>https://trid.trb.org/View/1990407</link>
      <description><![CDATA[To advocate green logistics and circular economy, the logistics policy simulation model of energy saving and emission reduction based on system dynamics (SD) was constructed. Within this context, the nitrogen oxide (NOₓ) emissions were regarded as the object of this study and the policy simulation was implemented in four ways. The results of the SD model have demonstrated that increasing the added value of the tertiary industry can significantly reduce NOₓ emissions. The effect of energy saving and emission reduction via reduction of the highway freight turnover is obvious. However, the emission decline effect of improving the proportion of gas energy consumption is not manifest compared to the optimization of transportation structure. In addition, increasing investment in environmental protection can only serve as an auxiliary approach. Therefore, the structural policies are crucial.]]></description>
      <pubDate>Thu, 23 Feb 2023 13:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990407</guid>
    </item>
    <item>
      <title>Study on Driver Attention Allocation under Common Driving Behaviors</title>
      <link>https://trid.trb.org/View/1990406</link>
      <description><![CDATA[To investigate the features of driver attention allocation, eye movement data was collected in a driving simulator environment, the distribution of fixation time, and searching scope of visual information were used to express the mode of a drivers’ attention allocation. Based on these factors, drivers’ attention allocation was investigated under different driving behaviors, and the influence of distractive vehicle coming from different directions was examined. Three conclusions were drawn from this study: (1) Drivers always give enough attention to information far ahead. (2) Under free driving, a driver will decrease his concern on information far ahead, but will increase his concern on the turning side. At the same time, a driver will transfer his concern from a distant region to a near region. (3) Traffic environment will impact little to a drivers’ attention allocation mode while driving along a road with a small curvature, and it will greatly impact a drivers’ attention mode while turning or changing lanes.]]></description>
      <pubDate>Thu, 23 Feb 2023 13:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990406</guid>
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    <item>
      <title>Vehicle Trajectory Reconstruction and Home Places Estimation from Monitoring Data</title>
      <link>https://trid.trb.org/View/1990405</link>
      <description><![CDATA[Analyzing vehicle travel information and roadway network traffic state of perception data has been a popular research topic. In order to excavate the vehicle travel characteristics in the city of Rui’an from monitoring data, the authors proposed a trajectory reconstruction model integrated into the ordered weighted averaging (OWA) and setting four attributes to solve trajectory records missing phenomenon, the authors verified the model’s reliability through actual experiments. Then they devised a method combining two algorithms to estimate the important places of each individual vehicle based on the reconstructed vehicles trajectory. The authors' results showed that the method using the K-means algorithm makes good predictions: 97% of the distances between the actual places and estimated places are within 2 km. Both the trajectory reconstruction and home places estimation will provide data support for studying vehicle commuting and road network characteristics.]]></description>
      <pubDate>Thu, 23 Feb 2023 13:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990405</guid>
    </item>
    <item>
      <title>Utilizing Ensemble Learning Methods in Real-Time Traffic Crash Prediction</title>
      <link>https://trid.trb.org/View/1990404</link>
      <description><![CDATA[Real-time traffic crash prediction is very important to traffic areas because it can help take active intervention actions before traffic accidents happen, and hence reduce accident rates. This study aims to use ensemble learning methods to improve the real-time traffic crash prediction accuracy by applying them to build models based on traffic flow data with crash occurrence labels collected from the Shanghai urban expressway in April and May of 2014. The authors applied four ensemble methods to build models, and used AUC, F-measure, and other evaluating indicators to measure the classification result of these models. It was concluded that the best ensemble learning model based on decision tree and logistic regression are random forest and LogitBoost, respectively. Their AUC measure can reach to 0.703 and 0.733, which are 7.99% and 4.12% higher than individual learners.]]></description>
      <pubDate>Wed, 22 Feb 2023 17:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990404</guid>
    </item>
    <item>
      <title>Research on the Adaptive Learning Support System of Air Traffic Control</title>
      <link>https://trid.trb.org/View/1990403</link>
      <description><![CDATA[The authors establish an adaptive learning support system of air traffic control, sending different teaching content to learners, in order to design a support system with adaptive learning based on aerodrome four-word codes. The system can determine learning strategies according to learners’ own learning style. Two groups of learners use a traditional learning method and an adaptive learning method, and the authors then determined their effects. The results show that average scores of the first group increased from 25 to 82, or about 228%; the average scores of the second group increase from 49 to 77, a growth rate of about 57%. For the first and second groups, the adaptive learning system reduced the time learners took to answer questions. The authors can conclude that the adaptive learning system can effectively improve the study efficiency for air traffic control learners.]]></description>
      <pubDate>Wed, 22 Feb 2023 17:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990403</guid>
    </item>
    <item>
      <title>A Trip Building and Chaining Methodology Using Traffic Surveillance Data</title>
      <link>https://trid.trb.org/View/1990402</link>
      <description><![CDATA[In many cities, traffic video surveillance systems have been installed at major intersections. These cameras can capture not only the traffic flow or violations but also the time, location, driving direction, color, and license plate of vehicles. This paper proposes an approach to build trips based on video surveillance data, combine the trips to form daily travel chains, and efficiently classify all travel chains into different modes. A K-means method finds clusters of different types of vehicles, and exclude profitable vehicles, which are always on the road. Four trip chaining patterns are derived from the data and used as the training set. A support vector machine (SVM) method classifies the daily trip chaining patterns. The results show that video surveillance data contains rich information on the traffic patterns and can be used in building the trips. The SVM method can classify trip chaining patterns efficiently with excellent results when processing a large amount of data.]]></description>
      <pubDate>Wed, 22 Feb 2023 17:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990402</guid>
    </item>
    <item>
      <title>Study on Residents’ Travel Behavior Based on “Internet +” Information Feedback</title>
      <link>https://trid.trb.org/View/1990400</link>
      <description><![CDATA[Today, the Internet affects residents’ travel behavior because of the rapid development of the urban transport systems in China. As information technology is increasingly involved in the transport field, residents’ travel behavior will become more closely interconnected with it. Travel concept is shifting quietly, travel mode and purpose are becoming diversified, and travel needs and characteristics are varying significantly in time and space. In this paper, interactions between “Internet +” and residents’ travel behavior are analyzed, and characteristics of residents’ travel behavior based on “Internet +” information feedback are studied. First, challenges and opportunities associated by "Internet+" are analyzed. Then, the residents’ travel behavior based on “Internet +” information feedback is surveyed using Nanchang as an example. The structural equation model is constructed, and the relationship between information feedback and residents’ travel behavior is verified through Statistical Package for the Social Sciences (SPSS). Finally, suggestions and measures are shared to improve residents’ travel environment.]]></description>
      <pubDate>Wed, 22 Feb 2023 17:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990400</guid>
    </item>
    <item>
      <title>Road Marking Crossing Behaviors of Electric Bicycles on Urban Road Sections</title>
      <link>https://trid.trb.org/View/1990401</link>
      <description><![CDATA[The behaviors of electric bicycles at crossroad markings that separate motorized and nonmotorized vehicles was modeled for the purpose of obtaining better traffic management measures. The probability model of nonmotorized vehicles’ road marking crossing was established to estimate the crossing rate using the number of nonmotorized vehicles, the width of bicycle lanes, and the number of motorized vehicles in the adjacent lane. The results of using observed data from three typical sections indicates that the proposed model can predict the crossing rate with an error of less than 6%. The proposed model can provide information that improves urban traffic operations and safety by adding separate road marking for motorized and nonmotorized vehicles.]]></description>
      <pubDate>Wed, 22 Feb 2023 17:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990401</guid>
    </item>
    <item>
      <title>Analysis of Urban Metro Accidents Based on Social Media Data</title>
      <link>https://trid.trb.org/View/1990399</link>
      <description><![CDATA[Metro plays an important role in middle and long-distance travel in large cities. However, aging equipment and maintenance problems may lead to metro accidents. In this paper, social media data are crawled from both the official and personal microblog to study the severity of accidents and subjective attitude of passengers. The relationship between metro ridership and accident is investigated. In addition, natural language processing (NLP) technology is applied to analyze the microblog text, so that public attitudes and responses to metro accidents can be identified. Through this method, the direct impact of metro accidents can be measured by easily-obtained and time-sensitive social media data. Moreover, this method is also helpful to evaluate the effect of emergency measures for the metro accident.]]></description>
      <pubDate>Wed, 22 Feb 2023 17:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990399</guid>
    </item>
    <item>
      <title>Research on the Effect of Subway Stations Guide Rails on Passenger Emergency Evacuation</title>
      <link>https://trid.trb.org/View/1990398</link>
      <description><![CDATA[The guide rail is an important and commonly used tool to organize passenger flow in subway stations, but still lacks theoretical supports about how to set it for better passenger evacuation in case of emergency. This paper fits the density-velocity relationship of the passengers according to the results of horizontal channel evacuation experiments and observational data. Then, using AnyLogic (7.1.2) as a simulation analysis tool, it compares passenger evacuation time and efficiency in settings with no guide rail, a continuous but fixed guide rail, and an optimized layout in the entrance-exit area, platform, and hall. The results show that the density-velocity relationship of passengers in a horizontal channel is close to the cubic polynomial function. The passenger emergency evacuation efficiency is higher when the entrance-exit is a set guide rail with channel type, while the platform and the subway hall channel use the discontinuous rail of the mobile gate.]]></description>
      <pubDate>Wed, 22 Feb 2023 09:39:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990398</guid>
    </item>
    <item>
      <title>Deterrent Effects of Disposition of Traffic Fines on Speeding Violations</title>
      <link>https://trid.trb.org/View/1990397</link>
      <description><![CDATA[Speeding is the most frequent behavior among traffic violation behaviors in South Korea. However, about 98% of speeding violators pay their traffic fines without demerit points and the effect of punishments is limited. The purpose of this study is to analyze the deterrent effect of the disposition of traffic fines on speeding violations and to establish countermeasures for traffic safety improvement. The descriptive statistics on data used in this study consists of driver license acquisition, traffic fine, and accident data provided by the Korean National Police Agency. Based on the data, the standard of the violator was suggested and the deterrent effects of disposition of traffic fines on speeding violations were analyzed. The Cox proportional hazards analysis was carried out to investigate behavior of habitual violators. An analysis of the relationship between speeding and accidents revealed that the drivers' speeding violation had an effect on the accident.]]></description>
      <pubDate>Wed, 22 Feb 2023 09:39:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990397</guid>
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