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    <title>Transport Research International Documentation (TRID)</title>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase III: Exploration of the Implementation of Using Unmanned Aircraft Systems for Freeway Incident Detection and Management: Part A</title>
      <link>https://trid.trb.org/View/2594043</link>
      <description><![CDATA[The research initiative focuses on exploring the feasibility of using Unmanned Aerial Vehicles (UAVs) for incident detection in high-speed multi-lane and freeway corridors. Previous phases of this research have laid the groundwork for developing and validating UAV-based incident detection methodologies. The research methodology involved developing an incident detection methodology using real-time video data from single and multiple flying UAVs, UAV path planning for corridor incident detection, and designing experiments to establish protocols, standards, and guidance for using UAVs in accordance with Federal Aviation Administration (FAA) regulations. The study's conclusions highlights key insights into the performance of the YOLO7 algorithm, emphasizing the impact of factors such as drone elevation and type on its efficiency. The findings indicate that UAVs offer a promising alternative to traditional manual patrolling methods. The validation of object detection and incident detection algorithms further validates the feasibility of using UAVs for real-time traffic monitoring, emphasizing the importance of meticulous site selection and data collection processes.]]></description>
      <pubDate>Mon, 15 Sep 2025 17:05:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594043</guid>
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    <item>
      <title>GPS-based incident detection algorithm for two-lane bus rapid transit systems: case study of Istanbul Metrobus</title>
      <link>https://trid.trb.org/View/2483258</link>
      <description><![CDATA[Bus rapid transit (BRT) systems have been gaining popularity in both developed and developing countries, having system deployments on varying scales. Especially in developing economies, BRT systems provide an easy solution to mobility needs. However, depending on their geometric design and operational characteristics, BRT systems may be vulnerable to incidents within their right-of-way. Even combined with excessive demand and exclusive corridor design, an incident inside the BRT corridor can cause significant delays to the commuters. Through this paper, we aim to propose a GPS-based incident detection algorithm for BRT systems. The proposed detection scheme is tested through a real-world case study conducted on the Istanbul Metrobus system through 19 real-world incident records. The results for the proposed algorithm are comparatively evaluated with another GPS-based incident detection scheme from the literature. The resulting performance measures of the proposed algorithm obtained as 100% detection rate, 0.74% false alarm rate, and 2.9-min mean time to detection.]]></description>
      <pubDate>Mon, 13 Jan 2025 09:14:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2483258</guid>
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    <item>
      <title>Stochastic conformal anomaly detection and resolution for air traffic control</title>
      <link>https://trid.trb.org/View/2219622</link>
      <description><![CDATA[Safety is of the utmost importance in the air traffic system. In recent years, data-driven algorithms have emerged to identify anomalous and potentially unsafe operations based on machine learning techniques. Although many algorithms have shown notable progress in anomaly detection, they have hardly considered the fact that data can be corrupted by noise and uncertainty (e.g., navigation system error) can lead to frequent misdetection and false alarms, which could disturb air traffic controllers and result in system performance degradation. Therefore, an accurate and reliable assessment of emerging safety risks that accounts for and alleviates the effect of uncertainty in data is required for safe and efficient airspace operations. To achieve this goal, this paper proposes a conformal prediction-based framework that explicitly examines uncertainty for reliable anomaly detection and learns online from new streaming data. In addition to supporting the monitoring task of air traffic controllers, the proposed method takes one step forward and provides support for the control task, by offering a resolution strategy when anomaly probability violates the predefined threshold. The proposed method is demonstrated with real air traffic data, called automatic dependent surveillance-broadcast data.]]></description>
      <pubDate>Mon, 14 Aug 2023 08:44:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2219622</guid>
    </item>
    <item>
      <title>Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase II: Freeway Incident Detection using Unmanned Aircraft Systems Part A</title>
      <link>https://trid.trb.org/View/2204485</link>
      <description><![CDATA[During the second phase of this study, the team collected field data with unmanned aerial vehicles (UAVs) at different elevations and distances from the road to analyze the performance of a background subtraction algorithm for vehicle detection. Validation analyses were carried out and their results indicated that a detection rate with an accuracy of up to 92% can be reached using the background subtraction algorithm. The results of the ANOVA test confirmed that the drone’s distance from the road was the only main factor associated with vehicle detection percentage (at the 95% confidence level). It was also determined that, depending on drone type, elevation can affect the detection rate based on the interaction plots created. The experiences from the field activities that took place during this phase of the project were incorporated into the previously developed protocol for the use of UAVs in corridor surveillance. The protocol was also updated with the steps that must be followed for several scenarios and these can be incorporated in future studies on the use of drones in transportation applications.]]></description>
      <pubDate>Mon, 17 Jul 2023 09:13:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2204485</guid>
    </item>
    <item>
      <title>Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase II: Freeway Incident Detection using Unmanned Aircraft Systems Part B
</title>
      <link>https://trid.trb.org/View/2120992</link>
      <description><![CDATA[Unmanned aerial vehicles (UAVs) provide a platform that can carry cameras and sensors for collecting real-time traffic information, especially for corridors under congested conditions, when the traditional loop detectors do not work properly and where there is a lack of other means of traffic monitoring. As an alternative, Road Rangers continuously patrol the roadways monitoring for traffic crashes and stranded motorists and then respond to those incidents. Continuously patrolling along the roadways is costly and man-power consuming. In this study, the researchers will explore the possibilities of replacing the patrolling tasks of Road Rangers with UAVs. The challenging research problems include: (1) development of on-line incident detection methodology with video data from multiple flying UAVs; (2) UAV path planning for corridor incident detection; (3) design and conduct experiments aimed at establishing protocols, standards, and guidance for safely using multiple UAVs for monitoring corridor-wide traffic conditions to complement Part 107 of FAA regulations, as amended. This research requires three phases. Phase I focused on design and test of the operations of multiple UAVs for collecting traffic information and development of incident detection methodology (see NICR Project 4-3: Corridor-Wide Surveillance Using Unmanned Aircraft Systems). Phase II will involve two separate but related research efforts by University of Puerto Rico, Mayaguez (Part A) and The University of South Florida (Part B). The University of South Florida research team will conduct experiments along I-75 and I-275 freeway corridors in Tampa, Florida to verify the protocols, standards and guidance, as well as the methodologies developed in Phase I. In Phase III of this project, the research team will focus on the validation of the algorithms developed in the previous phases and implementation matters of Phase II.

]]></description>
      <pubDate>Tue, 21 Feb 2023 14:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2120992</guid>
    </item>
    <item>
      <title>Freeway Incident Detection and Arterial Systems Management for the I-84 Corridor</title>
      <link>https://trid.trb.org/View/2005215</link>
      <description><![CDATA[The Idaho Transportation Department (ITD), in collaboration with other transportation agencies in the Treasure Valley, has participated in an Intelligent Transportation Systems (ITS) planning process over the past two years. As a result of this planning process, ITD and other agencies have submitted a grant request to the Federal Highway Administration (FHWA) to integrate real time traffic information from the I-84 freeway corridor into a regional traffic management system. The project that would be funded by this FHWA ITS grant has one objective: to integrate the data needed to make real time decisions: (1) by transportation agencies in the Treasure Valley so that they can more effectively operate and manage the region's transportation system and (2) by travelers in the Treasure Valley so that they can make optimal use of the region's transportation system. The integration of real time data that can be accessed by a variety of users each with different needs is a complex task. The integration will require deployment of new sensors and communication linkages, and a data base management system that is able to continuously accept a large number of transactions and queries. For example, data from loops on a twelve-mile section of I-84 will be transmitted to the center every 30 seconds; this information will be processed and made available to travelers on an Internet web site. And, these same data will be used to identify when an incident has occurred so that the appropriate agencies can be notified to deal with the problem as rapidly as possible. For effective incident detection and freeway management, various automatic incident detection algorithms (AIDs) are currently available. But most AIDs need calibration before they can be applied to a particular area. Each system differs in terms of detection rates, false alarm rates, and times to detection. Off-line testing of the detection systems to detect incidents and false alarm rates will be required before they can be implemented online. If an adequate quantity of incident data is not available some off-line testing may need to be done using simulated data. There are simulation programs available to simulate the operation of freeways as well as arterial streets. Finally, after adequate testing of the available AIDs are conducted with real and/or simulated data, ITD personnel will need to be trained to apply these systems on a day-to-day basis and to continually update and improve them. The purpose of this project is to enhance and build upon the work that will be completed as part of the Treasure Valley ITS integration project.]]></description>
      <pubDate>Tue, 23 Aug 2022 15:01:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2005215</guid>
    </item>
    <item>
      <title>Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase III: Exploration of the Implementation of Using Unmanned Aircraft Systems for Freeway Incident Detection and Management: Part A</title>
      <link>https://trid.trb.org/View/2004402</link>
      <description><![CDATA[In the last two phases of the project, the collaborative UPRM and USF research team (the research team hereafter) designed traffic data collection experiments on freeways with unmanned aerial systems (UAV with RGB and thermal cameras). The parameters of experiments included the height and speed of the drones, camera angles, congestion and non-congested traffic conditions, etc. The research team developed a learning-based object detection algorithm and evaluated the performance of the algorithm for RGB and thermal videos with different parameter settings and identified the settings with consistent high performance. In addition, the research team developed automated incident detection algorithms by identifying abnormal traffic characteristics. In Phase III of this project, the research team will focus on the validation of the algorithms developed in the previous phases and implementation matters of Phase II. Two main tasks of the research include (1) validating the object detection algorithm and automated incident detection algorithm developed in previous phases; (2) exploring the integration of UAS with the traffic management center. For the first task, researchers from UPRM will focus on incident detection with CCTV video. Traffic data during the incident will be collected with drones and shared with researchers from USF. Researchers from USF will focus on applying the incident detection algorithm for the data collected during the incident. Results from both analyses will be compared and insights from the comparison will be drawn. The same procedure will be applied to analyzing traffic data from the Tampa area. For the second task, researchers from UPRM and USF will work closely with Puerto Rico DTPW and Metric Engineering, and Florida DOT District 7. By understanding the barriers and challenges of implementing emerging technologies in automatic incident detection, the research team will work on implementation recommendations. (See also Phase I NICR Project 4-3: Corridor-Wide Surveillance Using Unmanned Aircraft Systems, Phase II Part A NICR Project 4-4.1 Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase II: Freeway Incident Detection using Unmanned Aircraft Systems Part A, and Phase II Part B NICR Project 4-4.1 Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase II: Freeway Incident Detection using Unmanned Aircraft Systems Part B).]]></description>
      <pubDate>Sun, 07 Aug 2022 09:50:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2004402</guid>
    </item>
    <item>
      <title>Dealing with latency effects in travel time prediction on motorways</title>
      <link>https://trid.trb.org/View/1730458</link>
      <description><![CDATA[Real-time traffic information is now a crucial part of operating a road network. The quality, accuracy and reliability of such information is critical to the road operators and users. Real-time travel time prediction methods using Automatic Number Plate Recognition cameras or Bluetooth/Wi-Fi readers that use matching algorithms to generate travel times in real-time can be vulnerable to an inherent latency issue. Measured travel times are based on vehicles that have already completed the journey and may not be representative for users about to commence that same journey. The aim of this research was to identify the latency in travel time prediction, quantify its effect, and develop a model to remove it. Datasets for the M50 motorway in Dublin, Ireland, were used to conduct the analysis. The results show that real-time travel times can be more accurately predicted when combined with historical travel time information. The approach was found to be valid and achievable and the developed tool can predict and inform both road operators and users during regular periods of congestion. The project also identified other data sources, such as real-time Automated Incident Detection (AID) loop data, incident and weather data, that can further enhance the predicted travel time calculation.]]></description>
      <pubDate>Thu, 17 Sep 2020 17:53:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1730458</guid>
    </item>
    <item>
      <title>A Random Forest Incident Detection Algorithm that Incorporates Contexts</title>
      <link>https://trid.trb.org/View/1720915</link>
      <description><![CDATA[A major problem faced by state of the art incident detection algorithms is their high false alert rates, which are caused in part by failing to differentiate incidents from contexts. Contexts are referred to as external factors that could be expected to influence traffic conditions, such as sporting events, public holidays and weather conditions. This paper presents RoadCast Incident Detection (RCID), an algorithm that aims to make this differentiation by gaining a better understanding of conditions that could be expected during contexts’ disruption. RCID was found to outperform RAID in terms of detection rate and false alert rate, and had a 25% lower false alert rate when incorporating contextual data. This improvement suggests that if RCID were to be implemented in a Traffic Management Centre, operators would be distracted by far fewer false alerts from contexts than is currently the case with state of the art algorithms, and so could detect incidents more effectively.]]></description>
      <pubDate>Mon, 27 Jul 2020 09:39:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1720915</guid>
    </item>
    <item>
      <title>Pedestrian Incident Detection in the Rail Right-of-Way using Artificial Intelligence</title>
      <link>https://trid.trb.org/View/1687981</link>
      <description><![CDATA[This research project builds on North Carolina Department of Transportation (NCDOT) RP 2015-18 (“Reduction in Railroad Right-of-Way Incidents”) and 2017-15 (“Rail Corridor Trespass Severity Assessment”). This effort focused on the development of a working prototype train-mounted camera system that will capture trespassing events in the nearby vicinity of moving or stopped trains. This dynamic system captures real-time trespassing data along any rail line, which will be used to better define trespassing issues. In the short term, the tools explored as part of this project will allow rail personnel to explain the extent of trespassing to municipal and law enforcement personnel, as well as the public. Prototype machine learning algorithms, sometimes referred to as “artificial intelligence”, were developed as a part of this project. The algorithms developed showed a lot of promise, even with a very limited library of thermal imagery in its database. Future research efforts should look to increase the image database to continue to increase the confidence in the algorithms ability to capture pedestrian events. Even with such a limited database, the team was able to capture a significant number of events on its test track.]]></description>
      <pubDate>Mon, 24 Feb 2020 11:51:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1687981</guid>
    </item>
    <item>
      <title>Incident detection methods using probe vehicles with on-board GPS equipment</title>
      <link>https://trid.trb.org/View/1473684</link>
      <description><![CDATA[Mobile communication instruments have made detecting traffic incidents possible by using floating traffic data. This paper studies the properties of traffic flow dynamics during incidents and proposes incident detection methods using floating data collected by probe vehicles equipped with on-board global positioning system (GPS) equipment. The proposed algorithms predict the time and location of traffic congestion caused by an incident. The detection rate and false rate of the models are examined using a traffic flow simulator, and the performance measures of the proposed methods are compared with those of previous methods.]]></description>
      <pubDate>Thu, 27 Jul 2017 14:14:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1473684</guid>
    </item>
    <item>
      <title>Efficient multiple model particle filtering for joint traffic state estimation and incident detection</title>
      <link>https://trid.trb.org/View/1424433</link>
      <description><![CDATA[This article proposes an efficient multiple model particle filter (EMMPF) to solve the problems of traffic state estimation and incident detection, which requires significantly less computation time compared to existing multiple model nonlinear filters. To incorporate the on ramps and off ramps on the highway, junction solvers for a traffic flow model with incident dynamics are developed. The effectiveness of the proposed EMMPF is assessed using a benchmark hybrid state estimation problem, and using synthetic traffic data generated by a micro-simulation software. Then, the traffic estimation framework is implemented using field data collected on Interstate 880 in California. The results show the EMMPF is capable of estimating the traffic state and detecting incidents and requires an order of magnitude less computation time compared to existing algorithms, especially when the hybrid system has a large number of rare models.]]></description>
      <pubDate>Fri, 21 Oct 2016 16:32:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1424433</guid>
    </item>
    <item>
      <title>Automatic Incident Detection at Intersections with Use of Telematics</title>
      <link>https://trid.trb.org/View/1414216</link>
      <description><![CDATA[While there are many examples of Intelligent Transport System deployments in Poland, more attention should be paid to traffic incident management and detection on dual-carriageways and urban street networks. One of the aims of CIVITAS DYN@MO, a European Union funded project, is to use TRISTAR (an Urban Transport Management System) detection modules to detect incidents at junctions equipped with traffic signals. The first part of the paper provides an overview of urban incident detection methods and algorithms. The second part of the paper describes how the TRISTAR system infrastructure and software are currently used for detecting incidents on urban artery sections (with higher speed limit). Because the need to detect incidents on other arteries was identified, research was undertaken that will lead to the development of algorithms for the detection of incidents on the streets equipped with traffic signals. The initial results of simulation studies (using VISSIM software) are presented in the third part of the paper. The authors' objective was to initially select parameters for detecting incidents at junctions equipped with traffic signals. Further research will look at a fusion of variables and possible other variables that may develop the algorithms.]]></description>
      <pubDate>Tue, 13 Sep 2016 19:08:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1414216</guid>
    </item>
    <item>
      <title>Automatic Incident Detection Based on Bluetooth Detection in Northern Bavaria</title>
      <link>https://trid.trb.org/View/1411531</link>
      <description><![CDATA[This work describes an approach to determine the current travel times on freeways based on the detection and re-identification of Bluetooth devices onboard of vehicles using stationary roadside Bluetooth detection technology. It also aims at using this information for the traffic state determination of a whole freeway network with the goal of a fast and reliable dynamic net control in incident situations. Based on a four year experience in a Bluetooth detector test bed in Northern Bavaria, Germany, and after the evaluation of hundreds of millions of single detections, the technology as well as the developed algorithms for validation and evaluation of the data show their feasibility in practical use, especially in areas with a low density of stationary detectors like inductive loops. The data-driven part of the approach is divided into three subsequent steps. These steps are the determination of travel times, the data filtering and validation of plausible travel times and the automatic incident detection. The determination of travel time is based on the time stamp of the detection of a Bluetooth device with the shortest estimated distance to the position of the Bluetooth detector. For the data filtering the “Time Dependent Comparison to Neighbor Values Filter” will be applied. This filter allows for a fast and reliable differentiation between unrealistic (due to stops, detours, back and return trips etc.) and plausible travel time values for a certain segment of the freeway and is based on a method to validate if the determined travel time is in a plausibility threshold corridor defined by the values of the previous and the next neighboring travel times. The outcome is a detailed travel time information for the whole freeway network which is used for an automatic incident detection, which was developed and calibrated within this research project. This includes the detection of the start of an incident as well as the end of an incident. The result is continuous information about the prevailing travel times and a fast and reliable traffic state information for all segments, which allows for a dynamic large-scale re-routing in the Bavarian freeway network in case of congestions or other disturbances of the traffic flow.]]></description>
      <pubDate>Thu, 28 Jul 2016 10:03:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1411531</guid>
    </item>
    <item>
      <title>Development of an Automated Approach for Quantifying Spatiotemporal Impact of Traffic Incidents</title>
      <link>https://trid.trb.org/View/1394233</link>
      <description><![CDATA[Traffic congestion on roadways seriously affect travel experience and cause economic and environmental problems. Part of the recurrent congestion is due to roadway bottlenecks such as lane drops or exit/entry ramps. Another major type of congestion is induced by traffic incidents such as traffic crashes. The former can be remedied by removing physical bottlenecks through the improvement of roadway capacity, geometry etc. However, the latter usually randomly occurs due to the high level of stochasticity of incident events. Therefore, it is a challenge to capture these non-recurrent congestion hot spots due to incidents. Nevertheless, the availability of real-time traffic sensor data provides the opportunity to address this issue through the use of data-driven solutions. Thus, the main objective of this study is to develop an automated approach to quantify incident induced congestion using sensor data. A practice-ready data-driven non-recurrent congestion quantification algorithm is developed and its implementation is demonstrated through real-world case study. It has been shown that the proposed automated approach can be used to efficiently identify incident-induced congestion.]]></description>
      <pubDate>Mon, 21 Mar 2016 16:40:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1394233</guid>
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