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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Robust Control of Vehicle Platoons Based on a Unified Spacing Policy</title>
      <link>https://trid.trb.org/View/2553431</link>
      <description><![CDATA[The authors propose a robust control method for vehicle platoons based on a unified spacing policy, which can be customized for different vehicles within the platoon. This spacing policy integrates the constant time headway (CTH) and constant spacing (CS) policies into a single framework with some modifications. The policy can be adjusted to resemble either a CTH-like or CS-like approach by simply tuning a weighting factor for each vehicle. To increase road throughput, the velocity difference between the current vehicle’s velocity and the preceding vehicle’s low-pass filtered velocity is introduced to the proposed policy. A second-order low-pass filter is used for the preceding vehicle’s velocity. As a result, the sliding variable for each vehicle’s spacing error only requires the current vehicle’s position, velocity, acceleration, and the preceding vehicle’s position and velocity, all of which can be measured by onboard sensors. This design reduces the platoon’s reliance on inter-vehicle communication. A predefined-time modification is introduced to eliminate nonzero initial conditions in spacing and velocity errors, thereby improving transient performance. The local controller for each vehicle is based on a third-order nonlinear system model that accounts for nonlinear disturbances and engine lag. A smoothed sliding mode robust controller is then designed for each vehicle to stabilize its spacing error. The input-to-state stability (ISS) property of the spacing error is established through rigorous theoretical analysis. Furthermore, the authors demonstrate the practical strong frequency domain string stability (PSFSS) of the entire vehicle platoon, focusing on signal oscillations along the platoon. Finally, extensive numerical studies are conducted to demonstrate the effectiveness of the proposed method.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:59:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553431</guid>
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    <item>
      <title>Optimal Sequential Merging Strategy Based on Adaptive Threshold for Ramp Traffic Involving Platoons</title>
      <link>https://trid.trb.org/View/2553319</link>
      <description><![CDATA[The platoon technology is gradually applied in the intelligent transportation systems to improve the safety, efficiency, energy saving, and emission reduction of vehicles during driving. However, in contrast to the traditional ramp merging scenario that consists of individual vehicles, the existing platoons from the upstream bring new challenges to the merging problem on ramps. Without an effective coordination strategy, the unnecessary congestion and disintegration of the existing platoons will lead to an increase in driving costs and safety risk. Therefore, this paper optimizes the merging strategy of vehicle sequences involving platoons on highway ramps. A merging strategy, based on an adaptive headway threshold, is first proposed, where the comprehensive economic benefit is considered as its optimization objective while taking into account the decrements of the time, fuel, and carbon emission costs. The vehicle sequence merging is then modeled as a Markov decision process to derive the optimal threshold, where the action set includes three actions: accelerating, decelerating, and maintaining the constant cruising speed. Afterwards, the constraints are brought to the decision-making to ensure the safety of the merging process. The calculation of the action value, combining the derived optimal threshold and constraint, is then detailed. Finally, the results of the simulation are evaluated, and a comparison between the proposed strategy and other existing methods is conducted, which demonstrates that the proposed approach improves the comprehensive economic benefits.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553319</guid>
    </item>
    <item>
      <title>Research on the strategy of cruise control system for urban traffic jam assistant</title>
      <link>https://trid.trb.org/View/2608028</link>
      <description><![CDATA[This paper proposes a hierarchical cruise control strategy that can adapt to urban traffic scenarios. Firstly, the perception layer defines a target selection strategy by using a novel trapezoidal dangerous area. Then, the upper controller contains speed follow mode and distance follow mode. An improved PI mode with integral separation and saturation terms is developed to achieve the desired speed following control, a safe distance mode via variable time headway is developed to facilitate the safe and smooth distance following control. The lower controller is designed based on feedforward and PI feedback. Finally, the experimental results demonstrate that the strategy can effectively select suitable target vehicles in complex urban scenarios. With the improved safe distance model and the third-order Bessel actuator response delay model, the strategy exhibits adaptability and stability. This study offers valuable suggestions and guidance for designing and applying the cruise control strategy for urban traffic scenarios.]]></description>
      <pubDate>Mon, 15 Dec 2025 16:50:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608028</guid>
    </item>
    <item>
      <title>From Manual to Automated Driving: Understanding the Shift in Driver Headway Preferences</title>
      <link>https://trid.trb.org/View/2572662</link>
      <description><![CDATA[A common feature in Automated Vehicles (AVs) at different levels of automation is Adaptive Cruise Control (ACC), which helps drivers to choose their desired headway to a preceding vehicle from pre-defined options, improving comfort and traffic efficiency. However, the acceptance and use of ACC depend on how well these options align with users’ expectations with different driving styles. Therefore, using a driving simulator and recruiting 28 participants, this study aimed to examine how the driver’s headway preferences differ from their manual driving behavior considering their personal driving styles and to identify the factors that shape this deviation. Additionally, the discrepancy between the available headway settings and the preferences of drivers and its impact on trust and perceived safety of users were explored. The participants experienced three scenarios, in the first they drove manually, in the second they defined their preferred time headway, and in the third they selected a desired headway setting from three available options. The results showed that drivers, in general, prefer a larger headway for ACC compared to their manual driving, although this preference depends on their personal driving styles. Along with the “dissociative” self-reported driving style, average manual time headway and conscientiousness personality trait affect the difference between drivers’ manual and preferred time headway. The study found that while exposure to ACC increases trust in AVs, perceived safety decreases if headway settings do not match drivers’ preferences, discouraging AVs use.]]></description>
      <pubDate>Fri, 05 Dec 2025 17:12:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572662</guid>
    </item>
    <item>
      <title>Research on collaborative adaptive cruise control based on MPC and improved spacing policy</title>
      <link>https://trid.trb.org/View/2563933</link>
      <description><![CDATA[A Cooperative Adaptive Cruise Control (CACC) algorithm based on Model Predictive Control (MPC) and an improved spacing policy is proposed in this study to address the current issues of low road utilization and inadequate dynamic regulation during platoon driving. First and foremost, the state of the leader vehicle and the minimum safe following distance are incorporated into the spacing policy to create an enhanced constant time headway (CTH) spacing policy. Secondly, following the MPC principle, the optimization problem of vehicle platoon is converted into a constrained quadratic programming problem that fulfills the requirements of driving safety, following distance, and ride comfort in a platoon. Finally, six-homogeneous-vehicle platoon is constructed for simulation verification, and the results show that the designed algorithm can not only ensure the string stability of platoon, but also effectively improve the road utilization rate. And in the vehicle of platoon cut-in/cut-out conditions, CACC is proven to have good ability of dynamically adjusting and restoring platoon stability.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563933</guid>
    </item>
    <item>
      <title>Resilient Platoon Operation Control of Heterogeneous Heavy-Haul Trains in Complex Environments</title>
      <link>https://trid.trb.org/View/2591923</link>
      <description><![CDATA[The burgeoning demand for railway logistics is propelling advancements in train operation control technology, aiming to reduce headways through the adoption of platoon operation. This study focuses on resilient platoon operation control of heterogeneous heavy-haul trains in intricate driving environments. The primary objective is to develop an innovative planning and control co-design scheme for real-time coordination of train departure headway and dynamic spacing. This approach aims to simulate real-world railway scenarios, validating system-level stability, real-time capability, and resilience against uncertainties and delays. The comprehensive analysis reveals that in platoon operation mode, the minimum departure headway for 5,000-ton heavy-haul train can be reduced to 95 seconds while maintaining an average spacing of 782.13 meters between trains on downhill sections with slopes less than 10%. This substantial enhancement in transportation efficiency exceeds that achieved by the existing fixed block mode.]]></description>
      <pubDate>Thu, 13 Nov 2025 16:59:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591923</guid>
    </item>
    <item>
      <title>Selection of Time Headway in Connected and Autonomous Vehicle Platoons Under Noisy V2V Communication</title>
      <link>https://trid.trb.org/View/2512339</link>
      <description><![CDATA[In this paper, the authors investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, they consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the selection of the time headway in predecessor-follower type vehicle platooning with a Constant Time Headway Policy (CTHP). Employing a CTHP based control law for each vehicle that utilizes onboard sensors for measurement of position and velocity of the predecessor vehicle and wireless communication network for obtaining the acceleration of the predecessor vehicle, they investigate how the implementable time headway is affected by communicated signal noise. They derive constraints on the CTHP controller gains for predecessor acceleration, velocity error and spacing error and a lower bound on the time headway which will ensure robust string stability of the platoon against signal noise. They perform comparative numerical simulations on an example to illustrate the main results.]]></description>
      <pubDate>Fri, 17 Oct 2025 16:49:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512339</guid>
    </item>
    <item>
      <title>Modeling Traffic Flow Mixed with Automated Vehicles Considering Dynamic Safety Distance</title>
      <link>https://trid.trb.org/View/2593213</link>
      <description><![CDATA[Autonomous driving technology is hailed as a pivotal solution for impending transportation challenges. Current research primarily analyzes the impact of autonomous driving technology through numerical simulation to better develop autonomous driving application scenarios. To elaborate on the driving behavior of various vehicle types, a concept of dynamic safety distance has been proposed in this paper for a detailed analysis of the impact of road traffic conditions on vehicles during driving. By revising car-following and lane-changing rules, a mixed traffic flow analysis model for autonomous and manual driving vehicles has been established. Through a small-scale cellular automaton model, congestion ratio (CR) and lane-changing ratio (LCR) are defined to discuss the impact of parameters such as response time and the penetration rates of autonomous vehicles on the lane traffic flow. The numerical simulations show that the traffic flow and average velocity of vehicles on the lane have significantly improved as the penetration rates of autonomous vehicles increase. This is because autonomous vehicles excel in controlling response time and distance factors compared with traditional manual driving vehicles. As seen from the vehicle-following response, autonomous vehicles maintain a shorter headway distance with the preceding vehicle and also improve lane utilization by changing lanes. When the penetration rate of autonomous vehicles increases from 0.2 to 0.6 in the 60% density lane, the congestion situation of the lane can be reduced by approximately 25%.]]></description>
      <pubDate>Wed, 27 Aug 2025 14:04:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593213</guid>
    </item>
    <item>
      <title>Gain-scheduled model predictive controller for vehicle-following trajectory generation for autonomous vehicles</title>
      <link>https://trid.trb.org/View/2558459</link>
      <description><![CDATA[As the development of autonomous vehicles accelerates, the need to enhance the comfort characteristics for those vehicles has become important. In the present article, an enhanced vehicle-following motion planner algorithm is presented. The aim of the algorithm is to smoothen the repetitive braking and acceleration behaviour during vehicle following in traffic jam situations. The algorithm uses the information gathered from Lidar sensor, cameras and vehicle-embedded sensors in real time to construct the range vs. range-rate diagram, and it computes the desired velocity trajectory for the speed controller. The algorithm is based on the Gain-Scheduled Model Predictive Controller (MPC), where at least one MPC controller is designed to handle one of the three vehicle-following operating conditions: speed control, headway control and emergency brake control. The algorithm allows the designer to manipulate two vehicle following variables: standstill distance between lead vehicle and ego vehicle, and the headway time gap. The algorithm is experimentally validated on a full-size passenger vehicle.]]></description>
      <pubDate>Fri, 18 Jul 2025 15:10:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2558459</guid>
    </item>
    <item>
      <title>Real Time Implementation of Inter-Car Distance Based on an Intelligent Stereovision System for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2525356</link>
      <description><![CDATA[In recent years, the fusion of deep learning and computer vision technologies has significantly advanced the development of autonomous vehicles that are present more and more in road traffic. In this context, this paper proposes a vehicle vision system that combines two techniques, the first uses artificial intelligence algorithm to accurately identify vehicles in the path of vehicle’s trajectory, the second uses stereovision algorithm to precisely estimate inter-vehicle distances. This solution effectively reduces overall processing time by exploiting the advantages of the You Only Look Once real-time vehicle detection and limiting the region of interest in image to the computation area of the disparity map for the stereovision. Detection and distance estimation of numerous vehicles consumes an important computation time; therefore a parallel data processing based on the Open Multi-Processing library is used to optimize data processing performance. The proposed solution is implemented on an embedded platform, the experiment results show that the system successfully detects vehicle and estimate distance with an error rate of less than 10%, achieving a real-time processing of 30 frames per second.]]></description>
      <pubDate>Thu, 17 Apr 2025 09:14:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2525356</guid>
    </item>
    <item>
      <title>Examining the effects of texting, web surfing, and navigating apps on urban driving behavior and crash risk</title>
      <link>https://trid.trb.org/View/2533899</link>
      <description><![CDATA[This research aims to assess the impact of using texting, web surfing and navigating applications on driving behavior and road safety in urban environments. The study involved collecting driving data from 36 young adult drivers through a driving simulator experiment, supplemented by a survey to gather participant characteristics and driving profiles. The driving experiment included periods of distraction-free driving and intervals when drivers used Facebook (scrolling through the feed), Google Maps (searching for specific locations), and Facebook Messenger (texting). Data analysis utilized linear and binary logistic mixed models to explore the effects of texting and web surfing on speed and its deviation, headway distance and its deviation, and crash risk. Results indicate that using texting, web surfing and navigating applications while driving elevate crash risk by 10% and decrease speed, speed deviation, headway, and headway deviation by 9%, 23%, 6%, and 18%, respectively. These findings underscore the crucial role of specific smartphone applications in shaping driving behavior and emphasize the need for targeted interventions to mitigate the associated risks in urban driving scenarios.]]></description>
      <pubDate>Wed, 02 Apr 2025 09:34:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2533899</guid>
    </item>
    <item>
      <title>Revisiting desired headway to estimate rear-end crash risk</title>
      <link>https://trid.trb.org/View/2509214</link>
      <description><![CDATA[Driver capability is a potential metric for integrating driver-level heterogeneity in crash risk assessment. However, it is challenging to evaluate due to the impact of various unobservable elements. In car-following (CF) events, a link between driver capability and desired headway exists, but it is difficult to estimate from the traffic data because the desired headway needs to be observed in steady-state CF. This study proposes a methodology for reliably estimating desired headway to estimate rear-end crash risks by developing suitable conditions for acceleration range and relative speed with a time window of sustenance. Results show that 3.5 seconds of sustenance time window with an acceleration range of ±0.75 m/s2 and a relative speed of ±1.52 m/s is required to observe the steady-state car-following event to measure desired headway. Any headway shorter than the desired headway for a driver would create an extreme event, which could be further utilized to estimate rear-end crash risks in car-following interactions.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:06:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509214</guid>
    </item>
    <item>
      <title>Nudging to improve driver behaviour</title>
      <link>https://trid.trb.org/View/2509200</link>
      <description><![CDATA[Under Queensland's Road Safety Action Plan 2022–24, the Department of Transport and Main Roads (TMR) funded a trial of new roadside technology which detects dangerous driving behaviours and provides real time feedback to drivers. This paper outlines Study 1 of a program of research that developed and tested message concepts for use in Study 2, onroad trials. The trial comprised roadside cameras, variable message signs (VMSs), and machine learning to identify motorists engaging or not engaging in this behaviour and to provide a targeted message. Focus groups were conducted with licensed drivers in Queensland to pilot message concepts addressing two behaviours - phone use while driving and tailgating. At the time of preparing this abstract, the on-road trials (i.e., Study 2) had not yet commenced but it is anticipated that preliminary findings may be shared from the on-road trials as part of the presentation later in 2024.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:06:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509200</guid>
    </item>
    <item>
      <title>Physics-Inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems</title>
      <link>https://trid.trb.org/View/2475895</link>
      <description><![CDATA[This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotive industry. To emulate the core functionality of stock ACC systems, which have proprietary control logic and undisclosed parameters, the constant time-headway policy (CTHP) is adopted. Leveraging the multi-layer artificial neural networks as universal approximators, the developed PiNN serves as a surrogate model for the longitudinal dynamics of ACC-engaged vehicles, efficiently learning the unknown parameters of the CTHP. The PiNNs allow the integration of physical laws directly into the learning process. The ability of the PiNN to infer the unknown ACC parameters is meticulously assessed using both synthetic and high-fidelity empirical data of space-gap and relative velocity involving ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PiNN in learning the unknown design parameters of stock ACC systems from different car manufacturers. The set of ACC model parameters obtained from the PiNN revealed that the stock ACC systems of the considered vehicles in three experimental campaigns are neither L₂ nor L[subscript (x)] string stable.]]></description>
      <pubDate>Mon, 23 Dec 2024 10:35:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475895</guid>
    </item>
    <item>
      <title>Does connected environment contribute to the driving safety and traffic efficiency improvement in emergency events?</title>
      <link>https://trid.trb.org/View/2442276</link>
      <description><![CDATA[A connected environment is crucial for improving road traffic safety and efficiency. However, it remains unclear how different connected environments affect the interaction between vehicles and their impact on driving safety and traffic efficiency in scenarios with potential risks, such as forced lane changes during emergency events. To investigate the effects of different connected environments on drivers’ interaction characteristics and their impact on driving safety and traffic efficiency, a group of simulated driving test was implemented in a multi-agent interactive intelligent connected vehicle driving simulation platform. Four types of connected environments were designed, Non-Connected Vehicles (NCV), Front Vehicle Single-Connected Vehicles (FCV), Rear Vehicle Single-Connected Vehicles (RCV), and Double-Connected Vehicles (DCV). Additionally, four different initial headways were tested (10 m, 20 m, 30 m, and 40 m). 40 drivers were recruited to participate in driving simulation experiments, and simulated driving data were collected. The research results indicate that for the front vehicle (FV), connectivity significantly reduces the collision risk with the accident vehicle (TTCFCV = 4.238 s, TTCDCV = 4.385 s), decreases the maximum longitudinal deceleration of FV (FCV = −1.212 m/s2, DCV = −1.022 m/s2), and reduces the speed fluctuation of FV (FCV = 4.748 km/h, DCV = 3.784 km/h). For the rear vehicle (RV), benefits are observed only in the FCV environment, where connectivity helps reduce the maximum deceleration of RV (FCV = −1.545 m/s2), decrease its speed fluctuation (FCV = 3.852 km/h), and enhance overall traffic efficiency (FCV = 12.133 s). Additionally, the minimum time difference to collision (TDTC) in the RCV environment (2.679 s) is significantly higher compared to other connected environments, and the number of cases with TDTC < 1.5 s (49) is notably lower than in other connected environments (NCV = 101, FCV = 107, DCV = 80). This suggests that the RCV environment effectively reduces the lateral collision risk during lane changes. Overall, while single-vehicle connectivity may help reduce driving risks and improve traffic efficiency, DCV may not significantly enhance vehicle safety and traffic efficiency. When the vehicle headway between FV and RV is 20 m (1.651 s), lateral conflicts between the vehicles are most severe. The maximum longitudinal deceleration of FV and RV also significantly decreases with increasing vehicle headway, and when the vehicle headway exceeds 30 m, the maximum longitudinal deceleration of RV nearly ceases to decrease (−1.993 m/s2 at 30 m, −1.948 m/s2 at 40 m). As the distance between the front and rear vehicles (DHWFV-RV) increases, the speed of FV becomes more stable, particularly when DHWFV-RV is 40 m (M = 4.204 km/h), where the speed fluctuations of FV are significantly lower compared to other vehicle headways. A 30-meter vehicle headway (M = 5.684 km/h) is more effective in maintaining speed stability for RV. Although travel time increases with the increase in DHWFV-RV, this change does not show a significant difference. Overall, to ensure traffic efficiency, a vehicle headway of 30 m generally satisfies lane-change safety requirements and provides more stable vehicle speed and acceleration.]]></description>
      <pubDate>Fri, 08 Nov 2024 15:49:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2442276</guid>
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