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    <title>Research in Progress (RIP)</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>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Data for Forecasting Truck Parking and Land Use

</title>
      <link>https://rip.trb.org/View/2558396</link>
      <description><![CDATA[The United States faces a growing shortage of truck parking, a problem that affects safety, efficiency, and community livability. Despite efforts by the U.S. Department of Transportation (DOT) and state DOTs to address this issue, demand for safe and adequate truck parking exceeds supply.

Truck parking is inherently a multijurisdictional challenge that requires coordination among federal, state, regional, and local agencies. While NCHRP Project 08-141, A Guidebook for Local Truck Parking Regulations, is developing model ordinances to assist local jurisdictions, there is no consistent method to determine how much truck parking should be required for truck-generating development projects.

A variety of public and private datasets exist to estimate truck parking demand, but each has limitations. Developing a national framework is further complicated by diverse land-use requirements, regulations, and economic conditions across states.

Research is needed to develop an analytically sound, data-driven framework that will enable transportation agencies to estimate current and future truck parking demand and integrate this information into land-use and freight-planning decisions.

The objective of this research is to develop and validate a framework for transportation agencies to estimate truck parking demand for current and future conditions.]]></description>
      <pubDate>Wed, 28 May 2025 10:14:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558396</guid>
    </item>
    <item>
      <title>Enhancing Traffic Delay Prediction Utilizing Data-Driven Techniques</title>
      <link>https://rip.trb.org/View/2485374</link>
      <description><![CDATA[A model that accurately predicts both traffic delays and the queues that result from work zones would be a valuable tool to Arizona Department of Transportation (ADOT), helping the agency to manage traffic, enhance work zone planning, reduce congestion, and improve road safety. Currently, ADOT lacks the ability to generate estimates of congestion and delays that result from lane closures and other forms of planned or unplanned roadway capacity reduction. Instead, the agency relies on rough generalities to manage traffic and maintain safe operating conditions around work zones. 

Integrating a data-driven model—one that is based on roadway capacity and travel demand—into the work-zone management process would help the Traffic Operations Center (TOC) and other ADOT groups respond to both planned and unplanned traffic-delay events. Information that predicts potential problems before they occur could help the TOC prepare more efficiently for closures and other events by anticipating messaging and communication needs to the traveling public.]]></description>
      <pubDate>Fri, 03 Jan 2025 16:08:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2485374</guid>
    </item>
    <item>
      <title>Safe and efficient automated freeway traffic control- Phase 2</title>
      <link>https://rip.trb.org/View/2440020</link>
      <description><![CDATA[Shockwaves are a naturally emerging phenomena in freeway traffic, but they represent one of the largest safety risks on freeways. Freeway drivers do not expect to encounter abrupt drops in speed or stopped traffic, as a result, shockwaves sharply increase the accident rates, particularly in the context of rear end collisions. For example, US interstate highways in 2021 saw the following rear-end collision numbers: Fatality 985, Injury-Only 71,408, Property-Damage-Only 152,011. Rear end collision severity is directly related to the relative speed between the involved vehicles, shockwaves increase these relative speeds, and thus, they also increase accident severity. Shockwaves also reduce freeway capacity and have a detrimental impact on fuel consumption and emissions because accelerating engines are less efficient than when cruising.

Connected and autonomous vehicles (CAV) hold the promise to attenuate and eliminate shockwaves (and thus, also reduce the severity and number of accidents), but only if the system is explicitly designed to do so. The very factors that give rise shockwaves in human driven vehicles (HDV) will also do so in CAV. While CAV offer new ways to manage traffic dynamics, an automated freeway will still be subject to traffic dynamics. The real challenge is designing the CAV system so that it ensures the safest possible operation, and then within those bounds, the greatest operational efficiency (maximizing capacity, minimizing delays, etc.).

This research essentially seeks to take conventionally unstable queued traffic and bring it to a stable flow while queued. It will approach CAV traffic control by first establishing the desired macroscopic traffic states along a freeway corridor and will use a rolling horizon to continually update the desired states in response to perturbations in the macroscopic traffic stream. Under this macroscopic framework, the CAV will know what behavior they should take simply by knowing where they are in space relative to the set of desired states. The main objective of the macro to micro control scheme is that the system can efficiently anticipate and respond to disturbances over large distances. It is this macroscopic look-ahead that will allow the system to detect and attenuate shockwaves. Although communications bandwidth is not the focus of this work, the macro to micro control scheme also has the potential to greatly reduce the necessary communication bandwidth to control the freeway traffic.

This proposal is for a continuation of a first year project. Year 1 is focusing on the initial transition from unstable stop and go traffic to the first follower with a smooth trajectory. It uses current conditions along the corridor to estimate the trajectory of a vehicle just entering the corridor and calculate the optimal trajectory that maintains the same departure time while reducing acceleration/deceleration. Then continually update the forecasted trajectory over time based on evolving conditions. The year 2 focus will be extending and refining the smooth trajectories upstream of the first vehicle, addressing unanticipated disturbances (from minor lane change maneuvers to major vehicle failures), and anticipating the management strategies needed to maintain the smoothly flowing queued traffic.
]]></description>
      <pubDate>Sun, 13 Oct 2024 09:24:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440020</guid>
    </item>
    <item>
      <title>Integrating Progression Band and Delay Optimization for Arterials with Unbalanced Directional Traffic</title>
      <link>https://rip.trb.org/View/2343710</link>
      <description><![CDATA[Unbalanced directional traffic is commonly observed in commuting corridors, where the high-volume direction may experience queue spillbacks and turning bay blockages that significantly downgrade the traffic efficiency. The traditional wisdom that naturally favors the high-volume direction often neglect the needs of the low-volume direction, incurring unnecessary delays. Such a dilemma raises a challenging need for a signal plan that concurrently ensures the traffic efficiency of both directions with distinct traffic features.
Fully recognizing the achievement of two major families of signal optimization models, delay minimization and progression maximization, this project intend to integrate their merits, and present a novel traffic signal model to minimize the through delay in the high-volume direction while preserving the progression in the low-volume direction. To achieve such an objective, this project will develop a mathematical programming framework with essential formulations. Especially, to estimate the queueing delay accurately with signal related parameters, unlike most existing studies assuming the uniformly distributed incoming traffic flow, the project will explicitly formulate the queue evolution process by accounting for the time-varying vehicle arrival rates resulting from the distinct upstream traffic streams. Such a detailed formulation shall enable the model to flexibly select the optimal phase sequences that allow low-volume traffic streams to
join the queue prior to those high-volume ones, thus minimizing queuing delays despite the maximum queue length being inevitably long. Moreover, the negative impacts of left turn vehicles merging into through queues due to the left-turn bay blockage by the expanding through queue should be also taken into consideration in the through delay computation. The proposed model with carefully designed formulations is expected to yield improved network-wide delay, fewer vehicle stops, and a shorter duration of left-turn bay blockage.]]></description>
      <pubDate>Thu, 22 Feb 2024 15:54:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343710</guid>
    </item>
    <item>
      <title>Methodologies for Monitoring Work Zone Performance Measures across State Border Highways using Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2329130</link>
      <description><![CDATA[Systematic monitoring of work zone queuing, and associated safety impacts, is a challenge for nearly all states, but particularly acute in the Midwest with the large volume of commercial vehicles. A few states are starting to evaluate work zone queuing on Interstates and associated safety implications within their own borders. However, it is particularly challenging to monitor the occurrence of queuing that extends across state lines. It is commonly observed that construction queues and unfortunately crash incidents occurring in Indiana, Illinois, Ohio, and Michigan due to construction activity in adjacent states. This project will identify connected vehicle data sources (cars and trucks) and develop scalable methodologies for effective monitoring of work zones that have impact beyond state borders. These “work zone analytics” will be used as framework for state agencies to develop objective, scalable and systematic methods for coordinating work zones whose impact extends across state borders.]]></description>
      <pubDate>Sun, 28 Jan 2024 13:11:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329130</guid>
    </item>
    <item>
      <title>Monitoring of Urban Roadway Safety Hazards from Existing Bus-based Video Imagery</title>
      <link>https://rip.trb.org/View/2292654</link>
      <description><![CDATA[Contributors to traffic safety hazards include lane changes in the presence of queues at signalized intersections and bus stops, mixes of vehicle classes, speed variability, and vehicle overtaking. Assessing locations with increased recurring traffic safety hazards requires extensive and ongoing collection of data on these contributors. Traditional methods entail using sensors at permanent or temporary fixed locations, which are costly, labor intensive, provide limited coverage over time and space, and only provide information on some contributors. Moreover, the location of these sensors may be influenced by factors other than optimal sampling, such as requests from well-organized constituencies. Therefore, relying on such data to assess traffic safety hazards to support improved policies and designs could lead to missing high-risk conditions resulting in inequitable outcomes.  Clearly, low cost and extensive, equitable data collection is desirable. Transit buses operate regularly over wide networks. Most agencies have equipped their bus fleet with cameras that record the environment inside and outside buses for liability, security, and safety purposes. Consequently, the imagery is available for other uses at near-zero marginal cost and the coverage provides comprehensive views that could potentially be used to determine times and locations of regularly occurring safety hazards. Moreover, this imagery has been shown by the principal investigators (PIs) to be effective in monitoring traffic volumes across time and space, information that provides exposure-based context for identifying safety hazards. In this project the PIs would investigate if the available, repeated, and extensive imagery recorded by cameras mounted on transit buses in regular operation could be used to identify “hazardous hotspots”.  The PIs have been obtaining transit bus-based video imagery to estimate traffic flows across the OSU campus and provide summary results to campus planners and operators on a regular basis. The OSU campus will again be used as a living lab testbed. The size and diversity of land uses make the campus representative of urban areas. Moreover, the campus is undergoing major construction activities, which would allow investigation of different infrastructure conditions that could influence traffic safety. The use of the campus as an experimental testbed would also allow for in-situ ground-truth observations to assess the accuracy of the video-based results.  The hazards to be considered in the proposed one-year project (phase 1) include lane specific queue lengths at intersections and bus stops, frequency of lane-changing in the presence of queues, and vehicle type mix with an emphasis on vulnerable vehicles (e.g., bicycles, scooters, and motorcycles). Speeds and their variation where autos conflict with vulnerable vehicles are also important safety factors. In phase 1, exploring the ability to measure speeds from the imagery will begin. In addition, imagery of traffic conditions at construction zones will be collected for future investigations of speed changes and other safety related hazards. The identification of hazards will be demonstrated using semi-automatic techniques based on an extension of a Graphical User Interface used in a previous project focused on traffic volumes. Moreover, steps toward automation for large scale analysis will be taken during phase 1.]]></description>
      <pubDate>Tue, 21 Nov 2023 20:40:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292654</guid>
    </item>
    <item>
      <title>Design and evaluation of an Arterial-Friendly Local Ramp Metering System</title>
      <link>https://rip.trb.org/View/2250737</link>
      <description><![CDATA[To address the widespread concern of an on-ramp control’s negative impacts (e.g., ramp queue spillover) on a neighboring arterial’s traffic conditions, the research team, with the support of Maryland Department of Transportation State Highway Administration's (MDOT SHA’s) staff/engineers, has recently developed an innovative Arterial-Friendly Ramp (AF-Ramp) metering system, which aims to maximize the total benefits of the freeway and arterial users with both an optimized ramp metering rate and a coordinated signal plan for intersections within the ramp-impact area. Different from current ramp-metering models in the literature and in practice, the proposed AF-Ramp system includes the target freeway segment, its on-ramp, and neighboring intersections in one integrated control environment, and converts them from competing for available roadway capacity to a coordinated mode that maximizes their total benefits. To ensure the AF-Ramp system’s effectiveness in addressing complex, local-specific traffic patterns, it is essential that a rigorous and extensive field evaluation be conducted with respect to the system’s key model parameters and embedded assumptions. The coordinated relationship between the system’s three primary models, from estimating the freeway’s remaining capacity for ramp flows to the optimization of ramp metering rate and intersection signal plans, can also be assessed with the field data. 
]]></description>
      <pubDate>Thu, 21 Sep 2023 14:59:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2250737</guid>
    </item>
    <item>
      <title>Enabling GLOSA through Domain Knowledge Aware SPAT Prediction and Queue Length Aware Trajectory Optimization</title>
      <link>https://rip.trb.org/View/2250699</link>
      <description><![CDATA[This study extends efforts in improving Signal Phase and Timing (SPAT) prediction and Trajectory Optimization near signalized intersections. It focuses on efficiently modeling and addressing uncertainties in intersections controlled by actuated traffic signals. One major uncertainty is using deep learning for SPAT prediction. While deep learning performs well most of the time, there are instances of faulty predictions. To address this, the study combines deep learning with traffic signal domain expertise to ensure accurate predictions aligned with traffic signal controller logic. In trajectory optimization, uncertainties arise from predicting the waiting queue and its clearance time at traffic signals. This task involves complex factors like traffic conditions, vehicle dynamics, and driver behavior, including perception reaction time. Incorporating queue length estimation and clearance time into the trajectory planning algorithm will enable fuel-efficient optimization, particularly beneficial for Green Light Optimal Speed Advisory (GLOSA) during high traffic demands when queues have a significant impact. This study will conduct a comprehensive literature review to assess the current state of SPAT prediction and queue estimation, considering relevant publications in traffic signal control, machine learning, and optimization. The aim is to identify optimal approaches for incorporating domain knowledge into SPAT prediction and integrating queue estimation into trajectory planning. ]]></description>
      <pubDate>Thu, 21 Sep 2023 14:18:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2250699</guid>
    </item>
    <item>
      <title>Develop Improved Queue Warning System Combining Multiple Data Sources</title>
      <link>https://rip.trb.org/View/2055963</link>
      <description><![CDATA[Existing queue warning (QW) systems predominantly use infrastructure-based sensors to detect the formation of queues and use dynamic message signs (DMS) to warn drivers. These QW systems, may be inadequate where the required number or density of sensors are not available. Research has shown that crowd sourced probe and connected vehicle (CV) trajectory data used in combination with sensor data can significantly improve the accuracy and latency of queue detection. Furthermore, because of gaps between fixed location of DMSs a subset of drivers can encounter a queue without seeing any warning. Sharing queue information through third-party providers and providing vehicle-specific queue warning in CVs can ensure provision of queue warning to a broader audience in a timely manner. The research team will: (1) Develop detailed design of an enhanced queue detection and warning system that combine point-, probe-, and vehicle trajectory data. (2) Test and fine-tune the queue detection algorithm design using computer simulation. (3) Conduct a proof of concept, prototype deployment and field evaluation of the new QW system design on a freeway segment where vehicle queues frequently form. (4) Document the systems engineering and algorithm for the QW system as a potential enhancement to the Lonestar™ advanced traffic management system software application.]]></description>
      <pubDate>Thu, 03 Nov 2022 14:33:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2055963</guid>
    </item>
    <item>
      <title>RES2019-10: Quantify Freeway Safety Service Patrol and Protect the Queue Impact 
on Transportation Network Reliability</title>
      <link>https://rip.trb.org/View/1851955</link>
      <description><![CDATA[The primary goal of this study is to better understand and quantify the benefits of 
Tennessee Department of Transportation's (TDOT’s) traffic incident management programs, e.g., HELP and PTQ.  The challenge of such an undertaking is in the accurate and fair estimation of the adverse effects of “bad things” that did not happen or were responded to promptly and removed effectively because of HELP and PTQ.  Examples of such include secondary crashes avoided, fatalities and injuries prevented, major delay significantly reduced, and emission/fuel consumption minimized.  To that end, five phased objectives are identified: (Objective 1) Attain Better Understanding of Incidents/Service Calls and Level of Service.  TDOT has good incident data in its Locate/IM and SWIFT.  TDOT currently reports the aggregated incident statistics by region on quarterly basis.  With the access to WAZE and its crowdsourced incident information and travel time variations, an in-depth spatiotemporal study with much higher granularity would provide new insights in terms of geographical incident distributions, more accurate incident and response times, temporal and spatial clustering of service calls types and incidents, isochronal service information for each region, level of service and response time at different locations, and so on. (Objective 2) Comprehensive Benefits Estimation for HELP Program.  Previous studies performed by UTK focused primarily on delay reduction aspects of the HELP program, based on the findings of dozens of prior studies where near 90% of the benefits derive from delay reduction.  With new data from WAZE and new models on fuel/emission and secondary crashes, a more thorough benefits estimation for all TDOT regions will be conducted. (Objective 3) Comprehensive Benefits Estimation for PTQ Program.  TDOT’s Protect the Queue program is a proactive effort towards better safety and efficiency.  Only a handful of DOTs have implemented some flavors of such initiative and reported different levels of reduction in crashes or near-crashes.  The reduction in delay is relatively low, if any, in comparison to the benefits of reduction of secondary crashes.  A thorough analysis and estimation for Tennessee’s unique circumstance would be a major outcome of this project. (Objective 4) Potential Benefit/Cost Estimation for Rural HELP Deployment/Expansion.  The efficient response and service of HELP program is primarily deployed in the State’s four major metropolitan areas, where in 2017 lane blockage incidents were promptly cleared in less than 30 minutes some 85% of the time and less than 2% of time it took more than 90 minutes to clear.  The promptness was less pronounced during the same year for the fringe/rural areas of these metropolitans where less than half of the time incidents were cleared in 30 minutes and more than 20% of the time it took more than 90 minutes [TDOT TMC/HELP Annual Report 2017].  Using the WAZE reports, which covers the entire State of Tennessee, one could identify rural areas where expansions and additional services could result in significant savings in lives and reduction in delay.  The benefits and costs for these potential expansions and rural deployment will be helpful for the investment decision-making. (Objective 5) Automation for Annual B/C Analysis.  It is logical to automate the preceding data-based analysis process and generate benefit/cost reports on yearly basis.  This automated tool should be able to interface with the incident database and generate statistics for reporting purposes.  Some flexibility for the user to modify and test different scenarios should be considered.

]]></description>
      <pubDate>Tue, 11 May 2021 16:09:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1851955</guid>
    </item>
    <item>
      <title>SPR-4600: Impacts to Traffic Behavior from Queue Warning Truck – Current Pilot 
Project</title>
      <link>https://rip.trb.org/View/1849175</link>
      <description><![CDATA[Indiana Department of Transportation (INDOT) is implementing a pilot project using traffic queue warning (TQW) trucks for advanced warning of traffic queues on selected interstate projects The objective of this proposal is to develop guidelines for placement considerations adjacent to exits, procedures for monitoring impact of diverting traffic, and assessment protocols for monitoring the effectiveness of TQW on the Interstate.]]></description>
      <pubDate>Mon, 26 Apr 2021 20:22:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/1849175</guid>
    </item>
    <item>
      <title>Evaluation of School Travel Patterns and Preferences</title>
      <link>https://rip.trb.org/View/1844084</link>
      <description><![CDATA[This research proposal includes several efforts to support the use of the North Carolina Department of Transportation (NCDOT) MSTA School Traffic Calculator (STC), with specific focus on estimates generated for vehicular demand rates and queue length.  The specific efforts include expanding the sample size of locations with highly variable estimates, evaluating trends related to school travel, comparing loading/unloading zone techniques, and developing recommendations for modeling school locations in Synchro.]]></description>
      <pubDate>Tue, 30 Mar 2021 08:26:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844084</guid>
    </item>
    <item>
      <title>Development of a Traffic Management Decision Support Tool (DST) for Freeway Incident Traffic Management (FITM) Plan Deployment – Phase 2: An enhanced DST-I95, including I495, I695, I70, US29</title>
      <link>https://rip.trb.org/View/1715293</link>
      <description><![CDATA[As a part of phase 1 (MD-17- SHA/UM/4-19), research was conducted in Fiscal Year 2016 for I-95 to develop a multi-criteria decision support tool for estimating incident-incurred delay and queue length for non-recurring congestion. Prior to this research, and due to the lack of statistical analyses on all the factors that affect an incident duration, CHART operations relied on their personal knowledge and experience to arrive at a conclusion on whether it is beneficial to deploy a FITM detour plan. Since its development, operators have extensively used the decision support tool to make operations decisions for I-95. Unfortunately, validation of the historical incident data using the decision support tool’s traffic flow modules was limited to I-95 only. The proposed research will leverage the recently developed statistical, traffic-flow, cost effectiveness, and artificial learning modules to expand the project limits to other corridors of interest, especially the I-695, I-70 and US-29.  The expanded geographic coverage would lead to better operations and incident management across the state of Maryland.  The Maryland Department of Transportation State Highway Administration (MDOT SHA) will be able to use the data-driven results from this study to make well-informed decisions when deploying FITM plans along major highways in the State. Better and faster decision-making will help reduce traffic delays as well as user costs, which can be extensive when they are caused by a major traffic incident/event.]]></description>
      <pubDate>Fri, 19 Jun 2020 13:33:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1715293</guid>
    </item>
    <item>
      <title>Driving Simulator Assessment of Dynamic Lane Grouping at Signalized intersections</title>
      <link>https://rip.trb.org/View/1521567</link>
      <description><![CDATA[This project involves using sensors and technology for dynamic changes.  How do drivers react to it? The cases and scenarios have already been developed. Are there technical barriers?]]></description>
      <pubDate>Tue, 03 Jul 2018 10:03:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/1521567</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 48-08. Connected Work Zones - Integrating Technology into Work Zones, Vehicles, and Equipment to Improve Worker, Driver, Pedestrian, and Bicycle Safety</title>
      <link>https://rip.trb.org/View/1407103</link>
      <description><![CDATA[Significant research, development and testing have been invested into safe, automated vehicle, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) technologies. Roadside construction and maintenance projects can be used to immediately test and enhance these technologies, improve internal work zone safety, reduce work zone crashes, and provide other safety and management benefits. V2V, V2I, and V2P technologies can be combined to create work zones where worker roll-over deaths from dump trucks, rollers, and other equipment are avoided, where alarms for vehicle-to-work zone intrusion are provided in time for employees to take refuge, and where productivity increases. Additionally this technology might be able to be used beyond the “internal work zone” and include areas peripheral to moving maintenance operations such as snowplow/snow blower echelons, sweeper trains, etc. to provide a warning to the traffic queue, pedestrians, or bicyclists behind these operations. These are zones of high incident risk, and there have been advances in warning technology in the recent past. The extent that these technologies are being adopted and implemented by departments of transportation (DOTs) is unknown. The purpose of this synthesis is to gather information on current practices, identify effective practices, and determine technology availability for connected work zone systems that integrate technology into work zones, vehicles, and equipment to communicate enhanced-precision location information to connected devices that provide visual, auditory, and haptic feedback to prevent work zone incidents and improve work zone safety by leveraging connected maintenance vehicles, equipment, and workers. The core of what is to be studied is the connection/communication from erratic vehicles to directly to workers (and other individuals potentially in harm’s way) in real time. This may include work vehicles in the work zone. The study will also identify any barriers to implementing a connected work zone system to improve worker/driver safety. Information from this synthesis could be used to help agencies implement connected work zone systems if the state of the practice and technology are deployable. The results of the synthesis could also lead to a research project for further deployment, such as developing and testing equipment and applications for work zones to provide a proof-of-concept and validation environment for the greater automated vehicle, V2V, V2I, and V2P technologies before they are deployed en masse. The synthesis will include a literature review and surveys and interviews to gather information from North American transportation and transit agencies, equipment and vehicle manufacturers, computer application developers, and professional organizations in North America, Europe, and Japan. A possible configuration or example of a connected work zone system could be as follows: (1) Secure communication and application technology so they do not provide an intrusion point for nefarious purposes or create security and safety vulnerabilities. 
(2)  Position (x,y,z) accuracy increased with the deployment of one or more known points and wireless communications throughout the work zone. (3) Display screens mounted in vehicles and equipment with a two-step sequence required to authorize movement when the application senses an imminent worker, pedestrian, bicyclist, or another vehicle/equipment collision. (4) Smartphone or dedicated device apps to communicate with workers and provide early danger alerts. (5) Wireless communication with passing motorists, pedestrians, bicyclists, etc.; lidar to detect those not yet equipped with V2I or V2V equipment; and supplemental audible/visual warnings. (6) Geofencing to identify work zone boundaries and alerts for boundary penetration. (7) Management functions to automate creating time, attendance, equipment use, and production records. ]]></description>
      <pubDate>Wed, 11 May 2016 15:50:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1407103</guid>
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