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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Study on Method of Development of Driving-Decisions Model for Automated Bus Traveling Fixed Route</title>
      <link>https://trid.trb.org/View/2434033</link>
      <description><![CDATA[The authors propose an efficient method for implementing decision-making functions in automated buses at specific locations. Based on their experience operating an automated shuttle bus, they recognize that the automated bus must make judgments at various points along its route. For instance, it needs to determine whether the traffic signal is green or not, whether passengers are waiting at a bus stop or not, and whether pedestrians will cross the road at a crosswalk or not. Some of these judgments become evident to be necessary after the automated bus begins its operation. To achieve efficient decision-making at specific locations, the authors propose a method where the automated bus switches simple inference models. These models are trained using thousands of image files captured and stored during the bus's travels near the judgment points. This approach effectively enhances the driving decision capabilities of an automated bus following a fixed route.]]></description>
      <pubDate>Fri, 25 Oct 2024 13:58:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2434033</guid>
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    <item>
      <title>Development of the Questionnaire of Bus Drivers’ Road Traffic Violations Based on Extended Theory of Planned Behavior</title>
      <link>https://trid.trb.org/View/2319854</link>
      <description><![CDATA[The research on road traffic under the theory of planned behavior is increasing in China. However, in the field of public transportation, especially the research on the road traffic violations of bus drivers is relatively limited. In this study, the questionnaire about bus drivers’ red-light running behavior was developed based on the theory of planned behavior (TPB), and its reliability and validity were tested. This questionnaire was designed with questions of different dimensions based on the extended theory of planned behavior. There were two sample groups in this study. Exploratory factor analysis was conducted with sample 1, and confirmatory factor analysis and reliability analysis were conducted with sample 2. The research results show that the questionnaire developed for measuring the red-light running behavior of bus drivers based on TPB theory has good structural validity and internal consistency and reliability, which meets the requirements of psychological measurement. The results provide theoretical reference for the study of road traffic violations of bus drivers in China.]]></description>
      <pubDate>Tue, 27 Feb 2024 16:40:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2319854</guid>
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    <item>
      <title>Recognition of Low-Energy Consumption Driving Behavior of Electric Bus Based on Machine Learning</title>
      <link>https://trid.trb.org/View/2319836</link>
      <description><![CDATA[Researching the identification method of low-energy consumption driving behaviors of electric buses will help to establish energy-saving driving strategies, guide actual driving behaviors, and help bus companies reduce energy consumption costs. In this paper, the K-means algorithm is used to cluster energy consumption per unit mileage, and three types of data of low, medium, and high energy consumption are obtained. The identification model of low-energy-consumption driving behavior in four situations is established, and SHAP algorithm is used to explain the identification model. The influence mechanism of driving behavior characteristics on energy consumption is analyzed, and the low-energy-consumption driving behavior characteristics of four situations are determined. The research results show that the driving behaviors that affect low energy consumption in the two situations of road sections and departuring from a node are mainly the average accelerator pedal opening and average speed.]]></description>
      <pubDate>Fri, 23 Feb 2024 16:23:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2319836</guid>
    </item>
    <item>
      <title>An Economic Driving Energy Management Strategy for the Fuel Cell Bus</title>
      <link>https://trid.trb.org/View/2315257</link>
      <description><![CDATA[Compared with passenger cars, the fuel cell bus (FCB) driving cycles have obvious periodicity. Therefore, based on the driving cycles’ periodicity characteristics and the traditional velocity prediction energy management strategy (EMS), this article proposes an economic driving EMS (EDEMS) for the FCB. In EDEMS, two scenarios are designed for the bus line condition: when there is no front bus in the bus lane (main scenario), the FCB can follow a trapezoidal programming curve (TPC)-based speed planning from the bus station out/in, which reduce the intersection stop condition, and the speed planning as the input applied to the model predictive control (MPC)-based EMS; otherwise, traditional velocity prediction is used in the MPC-based EMS (backup scenario). Moreover, the busload change at the bus station is added for the EDEMS cost function, which can accurately calculate the energy consumption and optimal energy allocation. The results show that the main scenario can reduce the intersection stop driving condition and the energy efficiency can improve by approximately 6.70% compared with the backup EMS. Overall, the EDMES can be applied to any bus route in the network environment and to further improve the comprehensive performance of FCB.]]></description>
      <pubDate>Wed, 24 Jan 2024 16:55:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2315257</guid>
    </item>
    <item>
      <title>Effect of Floating Bridge Motion on Vehicle Ride Comfort and Road Grip</title>
      <link>https://trid.trb.org/View/1973300</link>
      <description><![CDATA[The aim of this paper is to investigate the influence of floating bridge motion on bus driver’s ride comfort and road grip for the straight concept solution across Bjørnafjorden. For this investigation 3 degrees of freedom (DOF) bus model is defined for numerical simulation. Bus model has been excited by vertical motion of the bridge for four different bus speeds. Ride comfort has been assessed according to method and criteria proposed by International ISO 2631/1997 standard. For road grip assessing Dynamic Load Coefficient (DLC) has been used. It has been concluded that on floating bridge ‘little uncomfortable’ ISO 2631 criteria is reached at lower bus speed comparing to stationary ground road. Higher values of DLC for the case of floating bridge points out higher variation in vertical tyre forces (worse road grip). For bus speed at 90 km/h, DLC for floating bridge is approximately 0.08 which is 7% higher value comparing it to the case of stationary road.]]></description>
      <pubDate>Thu, 04 Jan 2024 10:52:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1973300</guid>
    </item>
    <item>
      <title>Predictive Cruise Control Under Cloud Control System for Urban Bus Considering Queue Dissipation Time</title>
      <link>https://trid.trb.org/View/2186737</link>
      <description><![CDATA[The driving conditions of urban consecutive signalized intersections are one of the main research scenarios for vehicle speed trajectory optimization, and typical for bus driving, where frequent acceleration and deceleration before and after the intersection can intensify the energy consumption of the bus. Prior research has predictive cruise controlled under Intelligent Transportation System, which is not feasible to directly communicate with controllers’ units of Intelligent Connected Vehicles. Besides, the effect of queue dissipation is a topic that has received less attention in recent related work. Therefore, this paper proposes a vehicle-cloud hierarchical architecture based on Cloud Control System at first, under which a predictive cruise control for urban buses is deployed. Given the impact of intersection queue length and dissipation time on vehicle driving, a queue dissipation time estimation model based on shockwave theory is proposed to predict changes in intersection traffic state. The queue dissipation time equivalent to the extension of the red-light window is reflected in the constraints of the Receding Distance Horizon Dynamic Programming (RDHDP) algorithm for solving the optimal control problem. Eventually, comparison simulations, a segment of realistic trip between adjacent stops, are presented. The results show that the proposed method saves 44.94%–56.74% of energy consumption and at least 26.8s of waiting time compared to human drivers, and 22.72%–41.27% of energy consumption compared to vehicle with the Intelligent Vehicle Infrastructure Cooperative Systems.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:12:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2186737</guid>
    </item>
    <item>
      <title>The Decision Making of Bus Entering Lane-Changing Behavior Analyzed by Back-Propagation Neural Network Model</title>
      <link>https://trid.trb.org/View/2000800</link>
      <description><![CDATA[When bus enter a bus stop, it has a mandatory lane-changing behavior which may cause capacity decrease in the upstream road. Firstly, based on the traffic survey, the lane changing location has been analyzed, and the bus entering lane-changing point distribution has been provided. This paper also figures out three main factors that impact buses entering lane changing: the traffic volume, the number of buses, and the bus location. Then, the three factors are used as the input variable to model the back-propagation neural network (BPNN) of which the output is the entering lane-changing point in upstream road. Lastly, the weight product method is used to analyze the sensitivity of those three factors. The result shows that traffic volume and bus location are positively correlated with the lane-changing point. However, there is a negative relationship between the bus number and the point. Furthermore, bus location has the largest sensitivity coefficient with the value being 0.22, and when 20% perturbation is added to the three factors, the sensitivity of bus number grows higher than the other two factors.]]></description>
      <pubDate>Wed, 19 Jul 2023 09:38:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2000800</guid>
    </item>
    <item>
      <title>Support Study for an Impact Assessment for a Possible Revision of Regulation (EC) No 561/2006 on Driving Times, Breaks and Rest Periods of Road Transport Workers</title>
      <link>https://trid.trb.org/View/2166123</link>
      <description><![CDATA[This final report was prepared within the framework of the support study for an impact assessment for a possible revision of driving and rest time rules for bus and coach drivers set out in Regulation (EC) NO 561/2006 on driving times, breaks and rest periods of road transport workers (the ‘assignment’ or ‘study’), based on contract No MOVE/C1/SER/2020-556/SI2.850002 implementing framework contract No MOVE/A3/2017-257, signed on 25 May 2021. The report is submitted to the European Commission – Directorate General for Transport and Mobility (DG MOVE, or the ‘Client’) by the Consortium led by Tetra Tech in association with Oxford Research AB (lead partner for the assignment), TIS and Panteia (‘the consultants’ or ‘study team’). This report presents the results of the study in terms of analysis of the data that has been collected and input for the different steps of the impact assessment that the Commission services are expected to prepare to support the initiative.]]></description>
      <pubDate>Thu, 22 Jun 2023 09:49:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2166123</guid>
    </item>
    <item>
      <title>ADAS at work: assessing professional bus drivers’ experience and acceptance of a narrow navigation system</title>
      <link>https://trid.trb.org/View/2043485</link>
      <description><![CDATA[Due to the argued benefits of passenger comfort, cost savings, and road safety, the bus sector is showing increasing interest in advanced driver-assistance systems (ADAS). Despite this growth of interest in ADAS and the fact that work tasks are sometimes complicated (especially docking at bus-stops which may occur several hundred times per shift), there has been little research into ADAS in buses. Therefore, the aim of this study was to develop further knowledge of how professional bus drivers experience and accept an ADAS which can help them dock at bus-stops. The study was conducted on a public route in an industrial area with five different bus-stops. Ten professional bus drivers got to use a narrow navigation system (NNS) that could dock automatically at bus-stops. The participants’ experience and acceptance were investigated using objective as well as subjective data (during and after the test-drive) and data were collected using interviews, questionnaires, and video recordings. The participants indicated high levels of trust in and acceptance of the NNS and felt that it had multiple benefits in terms of cognitive and physical ergonomics, safety, and comfort. However, the relatively slow docking process (which was deemed comfortable) was also expected to negatively affect, e.g., timetabling, possibly resulting in high stress levels. Therefore, when investigating users’ acceptance of ADAS in a work context, it is important to consider acceptance in terms of the operation, use, and work system levels and how those levels interact and affect each other.]]></description>
      <pubDate>Mon, 28 Nov 2022 10:56:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2043485</guid>
    </item>
    <item>
      <title>Camera Monitoring System Human Machine Interface Support – Monitor Positioning</title>
      <link>https://trid.trb.org/View/2011961</link>
      <description><![CDATA[Transport for London (TfL)’s Bus Safety Standard, which was launched in October 2018, requires a camera monitoring system (CMS) on new buses from 2021, and TfL and bus transit operators are planning to retrofit older buses with CMS. CMS replaces driving mirrors with a combination of cameras placed outside the bus, with monitors inside the driver’s cab. The risk of a mirror hitting a pedestrian or an infrastructural component is removed, and blind spots are reduced. But because CMS is a new concept, the effect on drivers is not well understood, and if drivers find the system hard to use, there may be new risks. The authors have extended the research into CMS to close the knowledge gaps about the positioning of monitors, and to develop recommendations for monitor positioning.]]></description>
      <pubDate>Thu, 17 Nov 2022 10:15:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2011961</guid>
    </item>
    <item>
      <title>Modelling lane changing behaviors for bus exiting at bus bay stops considering driving styles: A game theoretical approach</title>
      <link>https://trid.trb.org/View/2002422</link>
      <description><![CDATA[Bus is one of the most important travel modes for citizens, but lane changing behaviors for bus exiting at bus bay stops have negative impacts on traffic flow operations. It is necessary to investigate the characteristics of drivers’ behavioral decision-makings in lane changing for bus exiting for improving public transportation operations. The interactions between passenger car drivers in target lanes and bus drivers with different driving styles (e.g., aggressive or conservative style) are analyzed based on the game theory. A two-player, non-zero-sum, non-cooperative game model with incomplete information is formulated to examine the impacts of driving styles on the lane changing behavioral decision makings. The bi-level programming approach is applied to calibrate the parameters in the formulated model with solving the Bayesian equilibria and minimizing the prediction errors. The vehicle trajectory data extracted from Lvboqiao bus bay stop, Dalian, China are used for model calibration and validation. The results reveal that the formulated model has 73.01%, 86.60% and 83.54% accuracy rates on average for predicting the passenger car, aggressive and conservative bus drivers’ behaviors, respectively. At a 95% confidence level, the time error of a lane changing behavior is statistically significant by the paired t-test. Compared with the previous research, it can be proven that the consideration of driving styles is helpful to deeply explore the interactions between passenger car and bus drivers’ behaviors. The proposed model provides decent theoretical support for the drivers to make better decision-makings, and improves the traffic safety and efficiency around bus bay stops.]]></description>
      <pubDate>Thu, 15 Sep 2022 14:26:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2002422</guid>
    </item>
    <item>
      <title>Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines</title>
      <link>https://trid.trb.org/View/2005805</link>
      <description><![CDATA[The problems of bus bunching mitigation and the energy management of groups of vehicles have traditionally been treated separately in the literature and been formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program . Since this nonlinear program has only a few coupling variables, it is shown how it can be solved in parallel aboard each bus, such that only a marginal amount of computations need to be carried out centrally. This procedure leverages the structure of the bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in a model predictive control . Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. This method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines.]]></description>
      <pubDate>Mon, 12 Sep 2022 10:18:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2005805</guid>
    </item>
    <item>
      <title>Development of a City Bus Driving Cycle in Seoul Based on the Actual Patterns of Urban Bus Driving</title>
      <link>https://trid.trb.org/View/1819373</link>
      <description><![CDATA[Studies of driving cycles for buses have been published in a number of papers, e.g., the Central Business District (CBD) and New York Bus (NY Bus) driving cycles. Such studies, however, cannot represent the actual driving environment of Seoul because of differences in road conditions and the volume of traffic. Thus, this study presents the development of a driving cycle for the city bus system of Seoul, the capital city of Korea.         A representative route is selected by means of a statistical analysis of the city bus routes in Seoul. Experiments are performed to measure velocity, road grade, engine speed, load conditions, gear-shift patterns, and vehicle acceleration in actual Seoul traffic. A simulation model is developed to evaluate a driving cycle on the basis of the measured data obtained. The coupling effect between velocity and acceleration is analyzed, as well as the coupling effect between road grade and vehicle acceleration. A driving cycle is then proposed based upon an analysis of parameters drawn from the experimental data.         The cycle is evaluated by means of simulation and a test that employs a chassis dynamometer. The evaluation is performed in relation to fuel economy, drivability, and engine operating conditions in both the proposed driving cycle and actual driving conditions. After several rounds of reconstruction, a city bus driving cycle for Seoul has finally been proposed. The final driving cycle consists of a velocity profile, road grade factors, and a timed approach to gear shift timing.         This final product is called the Seoul City Bus Driving Cycle (SCBDC). The running time of the cycle is 1,320 s with 344 s of idle time. The average velocity is 29.2 km/h, with a maximum of 67.0 km/h over a total distance of 7.86 km. The fuel economy in the SCBDC is 2.36 km/I, as opposed to 2.32 km/I for actual Seoul traffic. There were few differences in terms of velocity distribution, acceleration, road grade, and engine operating conditions between the SCBDC and actual Seoul traffic.]]></description>
      <pubDate>Mon, 22 Aug 2022 16:12:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1819373</guid>
    </item>
    <item>
      <title>Pedal Application Error: Pedal Indicator Light - Human Machine Interface (HMI)</title>
      <link>https://trid.trb.org/View/1906666</link>
      <description><![CDATA[Pedal Application Error (PAE) is the term for when a driver mistakenly presses the accelerator instead of the brake pedal, causing unintended acceleration. Because drivers are often unaware of how they made the error, understanding such events is difficult. The authors aimed to determine and standardize a method of warning bus drivers that they are engaging the accelerator pedal. The authors conducted a review of relevant standards to identify the design requirements for warning icons in the bus driver information screen. Discussions were held with bus manufacturers on the design restrictions of bus cabs and Human Machine Interfaces (HMIs). Several designs were developed, and manufacturers were consulted on their technical feasibility, while bus drivers were surveyed for their preferences among the designs. Drivers rated Design 2 as the best and most intuitive, while manufacturers showed a clear preference for implementing it in the driver information screen. The recommendations for the HMI of the icon, and the selected design, are presented.]]></description>
      <pubDate>Mon, 28 Feb 2022 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1906666</guid>
    </item>
    <item>
      <title>Autonomous Bus Driving: A Novel Motion-Planning Approach</title>
      <link>https://trid.trb.org/View/1873104</link>
      <description><![CDATA[In this article, the authors present a motion-planning framework that leverages expert bus driver behavior, increasing the safety and maneuverability of autonomous buses. Autonomous vehicles will increase the safety, quality, and efficiency of transportation systems. However, to deploy this technology in urban public transport, many challenges related to self-driving buses still need to be addressed. Unlike passenger cars, buses have long and wide dimensions and a distinct chassis configuration, which significantly challenges their maneuverability. To deal with their special dimensions, the authors introduce a novel optimization objective that centers their whole body as they travel along a road. Furthermore, the authors present new environment classification schemes that enable self-driving buses to take advantage of their distinct chassis configuration, namely, the elevated overhangs, to increase maneuverability. Finally, the authors offer a novel collision checking method that explicitly considers a bus's front wheels and how they can protrude from beneath the chassis when maneuvering near stops. The authors demonstrate the benefits of their framework through experiments using an autonomous bus in real road scenarios.]]></description>
      <pubDate>Fri, 15 Oct 2021 09:27:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1873104</guid>
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