<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Traffic safety analysis using long-term accident record for merging and diverging section in Ethiopian Toll road expressway</title>
      <link>https://trid.trb.org/View/2622023</link>
      <description><![CDATA[Traffic disruptions (including frequent and abrupt lane changes in critical merging, diverging and overtaking zones) often result in expressway accidents. This study analysed crash data from the Ethiopian Toll Road Enterprise (2015–2022) using statistical and multinomial logistic regression models to identify high-risk crash locations, assess the severity and investigate the contributing factors in key merging and diverging sections. The analysis considered risk factors such as driver behaviour, traffic patterns, vehicle types, road conditions and lighting. The results indicated a 22.5% increase in accidents on wet pavements compared to dry surfaces across the entire length of the expressway, for a 2.04% increase in traffic volume. Fatalities and severe injuries were more frequent in the merging areas. Over 308 days of rainy weather across 8 years, accidents in the merging and diverging zones were 9.24% more likely to occur on wet roads than on dry surfaces. These observations highlight the increased accident risk caused by frequent and abrupt lane changes under wet conditions, emphasizing the need for improved safety measures in critical areas.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622023</guid>
    </item>
    <item>
      <title>Optimizing Operation of Diverging Diamond Interchange with Automated Vehicles
</title>
      <link>https://trid.trb.org/View/2625312</link>
      <description><![CDATA[The proposed research introduces an innovative traffic control method to optimize the operation of a diverging diamond interchange (DDI) in an automated driving environment. The DDI design is gradually gaining acceptance in many parts of the country because of its advantages over the conventional diamond interchange design, this research offers a new look into the potential benefits by the emerging automated driving technology in mitigating the drawbacks in DDI operations. This work includes the development of the algorithm through math modeling and computer simulation to control traffic flows through the DDI. The proposed control system is built on the premise that DDIs reduce both crossing and merging conflict points and that there is no longer a need for signal control for the side intersections.]]></description>
      <pubDate>Thu, 13 Nov 2025 15:39:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625312</guid>
    </item>
    <item>
      <title>Learning-Based MPC for Autonomous Motion Planning at Freeway Off-Ramp Diverging</title>
      <link>https://trid.trb.org/View/2591649</link>
      <description><![CDATA[Off-ramp diverging road segment is the preparation area for vehicles driving away from the freeway, while it causes more traffic conflicts making it a typical safety bottleneck. Focusing on improving driving safety, a Learning-based Model Predictive Control (LMPC) strategy is proposed to deal with the motion planning of autonomous vehicles (AVs) considering the dynamic driving behavior of surrounding traffic occupants in this area. The traditional MPC is combined with the reinforcement learning (RL) method to impose constraints for limitations to ensure safety while reducing conservatism. The Q-network of RL is used to build a terminal cost function to dynamically adjust the scale of the prediction horizon, which improves the speed of convergence while maintaining exploration of LMPC. Experimental testing scenarios are setup with considering different driving behaviors of surrounding vehicles to validate the performance of the proposed approach. The simulation results show that the LMPC can stably and flexibly plan the motion behavior of AVs, achieving a planning accuracy of 99.31$\%$, which has the potential for practical application.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591649</guid>
    </item>
    <item>
      <title>Safety performance analysis of toll plaza diverging area based on an improved simulation platform for weak-constraint driving behaviors</title>
      <link>https://trid.trb.org/View/2583164</link>
      <description><![CDATA[Toll plaza diverging area is a typical non-lane-based high-risk area characterized by frequent weaving and complex vehicle interactions. While observation-based approaches are effective for analyzing current safety conditions, they lack the flexibility in evaluating the safety impacts of infrastructure designs and traffic control strategies under future scenarios. To address this limitation, this study proposes a microsimulation-based approach to analyze the safety performance of toll plaza diverging areas by simulating the realistic conflict distributions under various traffic conditions. Based on the perception-decision-action (PDA) framework, the proposed approach improves the conflict simulation accuracy by more accurately modeling the weak-constraint driving behavior, including non-lane-based perception, dynamic toll lane selection, and car-following under weak-constraint conditions. Validated on real-world trajectory data from two distinct toll plaza diverging areas, the simulated conflict distributions by the PDA approach closely align with the observed data, while SUMO significantly underestimates the safety risks in diverging areas. Furthermore, a simulation platform is developed based on the PDA approach to quantitatively analyze the safety performance of toll plaza diverging areas under different diverging lengths and traffic volumes. Results indicate that insufficient diverging lengths increase severe conflicts, whereas excessively long diverging areas lead to inefficiencies without substantial safety benefits. This study provides novel insights into safety performance analysis in non-lane-based areas, offering a reliable simulation tool for optimizing management strategies in complex weaving scenarios.]]></description>
      <pubDate>Tue, 19 Aug 2025 15:29:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2583164</guid>
    </item>
    <item>
      <title>Comparative safety analysis of take-over control mechanisms of conditionally automated vehicles</title>
      <link>https://trid.trb.org/View/2544254</link>
      <description><![CDATA[Conditionally Automated driving (CAD) represents a pivotal point in the evolution of automotive technology, bridging full automation and human intervention through effective control mechanisms that ensure safe driver-system transitions. This research consisted of a comparative analysis of take-over mechanisms, focusing on ordinary merging and diverging maneuvers and critical collision-avoidance scenarios. Three take-over control (TOC) methods, including (i) accelerating/braking, (ii) pressing a dedicated button, and (iii) steering, were investigated. Thirty participants were recruited using a mixed factorial design with both within- and between-subject factors. The experimental simulations were conducted on the fixed-base driving simulator. The participants completed three runs on a motorway track comprising ordinary merging and diverging sections, with the final run involving a sudden critical decision to avoid the collision against two crashed vehicles. Weibull accelerated failure time models with and without shared frailty, mixed effects linear regression and multiple linear regression were used to model TOC time, maximum resultant acceleration, and minimum time to collision values. The results indicate that the pedal mechanism generally provides faster and safer takeovers, especially in critical situations, while the button mechanism results in the longest TOC times, and lowest minimum time to collision values, indicating higher risks. The steering wheel mechanism, associated with the highest maximum resultant acceleration and TOC times in merging and diverging maneuvers, suggests that lateral control may be more cognitively demanding for drivers. These findings emphasize the importance of selecting appropriate TOC mechanisms to improve the safety and efficiency of CAD systems.]]></description>
      <pubDate>Wed, 28 May 2025 16:23:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2544254</guid>
    </item>
    <item>
      <title>Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments</title>
      <link>https://trid.trb.org/View/2548925</link>
      <description><![CDATA[Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an example, this study proposes a lateral motion strategy for AVs based on deep reinforcement learning (DRL) algorithms. First, a microscopic simulation platform is developed to simulate the realistic diverging trajectories of human-driven vehicles (HVs), providing AVs with a high-fidelity training environment. Next, a DRL-based self-efficient lateral motion strategy for AVs is proposed, with state and reward functions tailored to the environmental features of the diverging area. Simulation results indicate that the strategy can significantly reduce the diverging time of single vehicles. In addition, considering the long-term coexistence of AVs and HVs, the study further explores how the varying penetration of AVs with self-efficient strategy impacts traffic flow in the diverging area. Findings reveal that a moderate increase in AV penetration can improve overall traffic efficiency and safety. But an excessive penetration of AVs with self-efficient strategy leads to intense competition for limited road resources, further deteriorating operational conditions in the diverging area.]]></description>
      <pubDate>Sun, 04 May 2025 16:19:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548925</guid>
    </item>
    <item>
      <title>Driving behavior inertia in urban tunnel diverging areas: New findings based on task-switching perspective</title>
      <link>https://trid.trb.org/View/2502129</link>
      <description><![CDATA[Urban tunnel diverging areas are crucial for enhancing overall traffic efficiency. However, the complexity of driving tasks and drivers’ inaccurate perception and response to these tasks are primary contributors to accidents. This study categorizes tunnel diverging areas into three task segments: the approach segment, ramp discovery segment, and entry and navigation segment, with task switching occurring during segment transitions. The objective is to investigate drivers’ responses to driving tasks and their behavior during task switching, providing a foundation for optimizing traffic engineering in diverging areas. Data on speed, longitudinal acceleration, vehicle position, and steering wheel angle were collected from 44 drivers in field tests. Initially, a comparative analysis of driving behavior within task segments was conducted. ANOVA was then used to identify critical change points for each indicator during task switching. Finally, K-means clustering was employed to analyze multiple driving behavior indicators and explore response delays during task transitions. The results reveal that drivers exhibited a higher speeding ratio and delayed lane-change responses within task segments. During task switching, drivers tended to continue their previous driving state—termed ‘driving behavior inertia’, which led to later entries into deceleration lanes and more abrupt deceleration, highlighting drivers’ reduced task sensitivity and lower risk perception in tunnel diverging areas. Furthermore, driving behavior inertia was significantly influenced by route and gender. Drivers in the left lane showed weaker inertia than those in the right lane, while female drivers exhibited stronger inertia. These findings offer valuable insights for the design and optimization of traffic engineering facilities in tunnel diverging areas.]]></description>
      <pubDate>Mon, 24 Feb 2025 17:05:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2502129</guid>
    </item>
    <item>
      <title>Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer</title>
      <link>https://trid.trb.org/View/2459155</link>
      <description><![CDATA[Complex road sections without lane markings cannot constrain vehicles to follow the lane disciplines. As a result, vehicles often exhibit more disorderly rapid lateral movements (RLMs) in these areas, making it difficult to accurately predict vehicle trajectories. This study takes toll plaza diverging area as an example to propose a framework incorporated the Hidden Markov Model (HMM) and Temporal Fusion Transformer (TFT) for vehicle trajectory prediction in non-lane based complex road sections. The results demonstrate that the vehicles exhibit more RLMs when there are more toll lanes matching their toll collection types. Validated on two toll plaza diverging areas with different structures, the proposed framework achieves higher prediction accuracy than other state-of-the-art predictive methods, particularly in long prediction horizons. In addition, the interpretability of TFT suggests that incorporating RLM intention prediction and environmental factors specific to non-lane based areas into trajectory prediction is of great importance.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2459155</guid>
    </item>
    <item>
      <title>Läsbarhet och körbeteende vid vägmärket körfält upphör (F25) vid vägarbeten : en jämförande studie mellan lysande VMS och reflexfolie</title>
      <link>https://trid.trb.org/View/2491308</link>
      <description><![CDATA[The road sign F25 (Lane ends) is often used at road works, either as retroreflective sheeting or as an electronic variable message sign (VMS). Road authority documents often state that on larger roadsF25 must be of size large. When using a VMS there are however limitations concerning the size of the road sign, especially when there is an additional text board. Hence, it is important to know which dimensions and what text sizes are needed for the legibility of a VMS to not deteriorate compared to retroreflective sheeting. The overall aim of this project was to investigate which dimensions F25 needs to have when shown on an electronic VMS to have at least the same legibility as a retroreflective road sign of size large. The legibility of different text sizes on a text board of a VMS was also examined in relation to retroreflective sheeting. The project was divided into a literature review, a controlled legibility study and a verification study at actual road work conditions. The results showed that F25 of size large in a retroreflective sign can be replaced by a VMS of size 0,92×1,15 m or larger without deteriorated performance concerning legibility and driving behaviour. A VMS with black background and white symbol had at least as good legibility at night as a corresponding VMS with orange background and black symbol. In the verification study with road works on an urban expressway where the left lane ended, the speed profiles in the right lane were equal for the three different sign implementations. However, when F25 was implemented as a retroreflective sign, a larger share of lane changes was made closer to the road narrowing compared to VMS. The results from the project can be used as a basis for estimating requirements of dimensions of electronic VMS.]]></description>
      <pubDate>Fri, 17 Jan 2025 15:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491308</guid>
    </item>
    <item>
      <title>Lane Choice of Diverging Vehicles in Tunnels Adjacent to Downstream Interchanges</title>
      <link>https://trid.trb.org/View/2475474</link>
      <description><![CDATA[Tunnels adjacent to downstream interchanges are the sections with high driving risk on mountainous freeways, but lane-choice behaviors here are unknown. In this study, driving experiments were conducted based on a real vehicle, and vehicle trajectories were obtained in four tunnel-interchange sections. Lane-choice and lane-change behavior of diverging drivers were analyzed. Results show the drivers change lanes in front of and inside the tunnel although crossing the solid lane line is not allowed, but no drivers change to the left lane in the connections between the tunnels and the adjacent interchanges. As the connection length decreases, the share of the drivers changing lanes in Zone 1 and entering the right lane before Zone 5 increases; and the diverging length declines, which leads to increasing maneuver difficulty and diverging risk. Drivers may miss the mainline exit if the connection is too short. The findings can help enhance traffic safety.]]></description>
      <pubDate>Thu, 26 Dec 2024 09:35:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475474</guid>
    </item>
    <item>
      <title>Evaluation of Traffic Operation Status in Diverging Areas under Lane Control Measures</title>
      <link>https://trid.trb.org/View/2475568</link>
      <description><![CDATA[With the implementation of multi-lane highway lane control measures, lane-changing behavior has become more concentrated in the diverging area, so it is necessary to provide a dashed solid line in the diverging area to provide an interchangeable segment for passenger cars. A model of vehicle lane change trajectories in the diverging area is obtained through a driving simulation experiment, and the length of the dashed solid line is designed accordingly. Through traffic simulation experiments, five evaluation indicators, namely, average speed, average delay, lane-changing rate, number of rear-end conflicts, and number of lane-changing conflicts, are selected from the aspects of accessibility and safety. The evaluation system of the traffic operation status is constructed by using the Principal Component Analysis method. The evaluation system is applied through the actual reconstruction and expansion projects, and the optimal lane control measures are derived under the corresponding conditions.]]></description>
      <pubDate>Mon, 23 Dec 2024 10:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475568</guid>
    </item>
    <item>
      <title>A Two-Stage Behavioral Model Considering Vehicle Motion Fluctuations for Decision-Making During Lane Changes in Diverging Areas</title>
      <link>https://trid.trb.org/View/2452587</link>
      <description><![CDATA[Optimizing diverging areas is critical for traffic safety and operations management. Inherently, diverging areas require lane changes (i.e., mandatory lane changes), which can cause significant disruptions to local traffic flow. To describe a realistic and detailed diverging area traffic flow, this study developed a two-stage lane-change decision-making behavioral model considering vehicle motion fluctuations. Firstly, according to the geometric characteristics of the diverging area and the need for diverging vehicles to change lanes, a lane-changing rule combining time-headway and distance-headway was established. Meanwhile, a lane-changing probability expression reflecting vehicle movement characteristics and remaining distance was defined. Secondly, the speed fluctuation sensitivity coefficient and distance sensitivity coefficient of the proposed model were calibrated based on real-world mandatory lane-changing observations. Finally, numerical simulations were used to evaluate and verify the proposed model. The results indicate that: 1) macroscopically, the simulated traffic is consistent with the observed traffic in terms of density, flow and speed, and the time–space diagram reflects the similarity of the traffic state changes; 2) microscopically, the section velocity distribution and diversion location distribution indirectly express the driving decision and motion adjustment process of vehicles. This study contributes to in-depth understanding of the traffic flow evolution in diverging areas and facilitates mandatory lane-change modeling.]]></description>
      <pubDate>Wed, 11 Dec 2024 10:39:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452587</guid>
    </item>
    <item>
      <title>Empirical Analysis of Drivers’ Merging and Diverging Responses to Autonomous Truck Platooning on Freeway Weaving Segments</title>
      <link>https://trid.trb.org/View/2417331</link>
      <description><![CDATA[Autonomous truck platoons hold the potential for substantial economic and environmental advantages. However, there is a lack of comprehensive research on the interactions between drivers and different configurations of truck platoons at waving segments. This study aims to contribute to the literature by achieving three primary objectives: (1) examine the effects of various truck platoon configurations on merging and diverging behaviors [time to merge (TTM) and time to diverge (TTD)]; (2) explore the impact of individual characteristics such as age, gender, education level, and driving experience on TTM and TTD; and (3) investigate the decision-making associated with these maneuvers (merging or diverging in front of the platoon, behind the platoon or cut in through the platoon). A driving simulator study was conducted with 85 participants across 12 distinct scenarios, considering variations in platoon size, intraplatoon spacing, and platoon lane-change behavior. Several statistical methods were employed, including ANOVA, the Cox proportional hazards model, and machine-learning techniques, to analyze the factors impacting TTM and TTD. The results revealed that increasing the headway distances between trucks in a platoon to 13.72 m (45 ft) or 18.29 m (60 ft) substantially decreased TTM, enhancing traffic flow. Furthermore, splitting the truck platoon between two lanes of the freeway before the merging point significantly influenced other drivers’ merging decisions. When half of the trucks in a platoon switched to the left lane before the merging point, a larger proportion of participants chose to merge ahead of the platoon. Age, gender, education level, and self-assessment of driving skills were all found to significantly influence merging and diverging behaviors. Drivers with higher degrees took longer to merge, whereas older, male, and experienced drivers merged faster. The lowest average TTD was observed when half the platoon switched to the left lane before the diverging point.]]></description>
      <pubDate>Wed, 09 Oct 2024 15:17:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417331</guid>
    </item>
    <item>
      <title>Evaluation of driver’s situation awareness in freeway exit using backpropagation neural network</title>
      <link>https://trid.trb.org/View/2401196</link>
      <description><![CDATA[Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α / θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training’s mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters.]]></description>
      <pubDate>Mon, 29 Jul 2024 16:27:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2401196</guid>
    </item>
    <item>
      <title>Investigating the features of risky driving behaviors on expressway diverge area based on conflict and modeling analysis</title>
      <link>https://trid.trb.org/View/2402429</link>
      <description><![CDATA[Driving behaviors are important cause of expressway crash. In this study, method based on modified time-to-collision (MTTC) to identify risky driving behaviors on an expressway diverge area is proposed, thus investigating the impact of velocity and acceleration features of risky driving behavior. Firstly, MTTC is applied to judge whether the behavior is risky. Then, the relationships between velocity, acceleration and different driving behavior on the expressway diverge area were fit by binary logistic regression models (BLR) with L2 regularization and random forests (RF) models, and the models were interpreted by feature importance plots and partial dependency plots. The results show that the AUC metric of 4 RF models for 4 types of driving behaviors, namely, left lane change, right lane change, acceleration and deceleration, are 0.932, 0.845, 0.846 and 0.860 separately. The interpretation of models demonstrates that velocity and absolute value of acceleration greatly affect the risk of the driving behaviors. Different driving behaviors with a certain acceleration have a range of safety speed range. The range will get narrower with the growth of maximum absolute value of acceleration rate, and will be nearly non-exist when the acceleration is over 5 m/s2. In conclusion, this study provided a methodology to measure the risk of driving behaviors and establish a model to recognize of risky driving behaviors. The results can lay the foundation for making countermeasures to prevent risky driving behaviors by managing the vehicle speed.]]></description>
      <pubDate>Mon, 29 Jul 2024 16:27:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2402429</guid>
    </item>
  </channel>
</rss>