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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnM+PGZpbHRlciBmaWVsZD0iaW5kZXh0ZXJtcyIgdmFsdWU9IiZxdW90O1JhaWxyb2FkIHJhaWxzJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7UmFpbHJvYWQgcmFpbHMmcXVvdDsiIC8+PC9maWx0ZXJzPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
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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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Experimental Determination of Crack Growth in Rails Subjected to Long-Term Cyclic Fatigue Loading</title>
      <link>https://rip.trb.org/View/2573184</link>
      <description><![CDATA[It is well known that one of the most significant causes of train derailments within the U.S. is due to rail fracture. Despite this fact, a reliable model for predicting fatigue fracture in rails has not yet been deployed within the U.S. The research team has recently been developing a multiscale computational algorithm for predicting crack evolution in ductile solids subjected to long-term cyclic loading. In this part of the UTCRS the team will perform intricate experiments on rails with internal cracks as a means of both obtaining material properties and validating an advanced computational model under development in their companion proposal entitled Computational Model for Predicting Fracture in Rails Subjected to Long-Term Cyclic Fatigue Loading. Furthermore, with funding provided by MxV, the team has recently completed cyclic crack growth experiments on seven bi-axially loaded rails with internal cracks that had previously been in service. The team is therefore in this research developing the ability to: a) characterize fracture parameters for deploying their advanced fracture mechanics model; b) utilize these parameters to predict crack growth due to cyclic fatigue in rails; and c) utilize the experimental results obtained over the previous decade of testing to validate the computational predictive methodology. Should this model development prove to be useful, it is the team’s ultimate intention to utilize this new advanced technology as a tool for determining how long rails in which flaws have been detected can be safely retained in service.]]></description>
      <pubDate>Mon, 14 Jul 2025 13:01:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573184</guid>
    </item>
    <item>
      <title>Railway Right of Way Monitoring and Early Warning System (RailMEWS) Based on Satellite and Aerial Imagery</title>
      <link>https://rip.trb.org/View/2353427</link>
      <description><![CDATA[Description: In this Phase-I, one-year project the research team proposes to conduct feasibility studies and provide recommendations for the development of a Railway Right of Way Monitoring and Early Warning System (RailMEWS). The feasibility study will answer the following questions:

How can we use drones and satellites to monitor the railway infrastructure?
What infrastructure components can be monitored effectively and what are the potential limitations of a RailMEWS in each case?
What railway Infrastructure Monitoring Systems (IMS) are available today for integration with satellite and drone data?
What are the desired functions and design parameters of an Early Warning System? (e.g. connectivity to the signaling system, real-time vs. centralized processing, etc)
What is the incremental investment needed to develop the RailMEWS system?
Intellectual Merit: The team proposes to process information obtained from commercially available satellite and aerial imagery and combine with information obtained by conventional monitoring systems to develop maps that show the kinematic behavior of the railway infrastructure at the terrain, sub-structure and track scales

Broader Impacts: The proposed work will set the foundations for the development and implementation of a RailMEWS to the railway network, its expansion to other transportation modes and its implementation to the transportation network statewide and beyond. This research will set the framework for larger research projects with diverse research partners and will be ex-tended in future studies to monitor the roadway network, ports, and inland ports simultaneously and will be integrated with their respective signaling systems.]]></description>
      <pubDate>Mon, 25 Mar 2024 15:46:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2353427</guid>
    </item>
    <item>
      <title>Monitoring Rail Bed Infrastructure Using Wireless Passive Sensing (1.20)</title>
      <link>https://rip.trb.org/View/1994175</link>
      <description><![CDATA[Assessment and monitoring the performance and health of rail structures is an essential aspect of prioritizing the repair and replacement, as well as extending service life, of these transportation infrastructures. Intrusion of fines from the subgrade or surface leads to ballast fouling and consequently impairs track drainage and adversely affects serviceability [1]. Development and implementation of novel strategies for the assessment and health monitoring of this infrastructure, particularly fouling phenomena at its early stage, can significantly extend railbed service life. New fouling assessment/detection techniques have been investigated, e.g., ground penetration radar (GPR), thermal imaging techniques, but have several limitations [2, 3]. This project focuses on using embedded and passive wireless sensing to meet practical needs of railbed monitoring. Preliminary results by the Co-PI indicate that a passive wireless sensor, based on a harmonic transponder design, can be embedded within railroad ballast and when interrogated can produce a measurable signal. At scale, such devices could be produced at very low cost and thus liberally embedded in ballast. Signatures from these sensors, once interrogated, can be tracked over time to detect fouling and thus non-destructively assess and monitor the integrity of the railbed. The proposed work will conduct a series of controlled experiments to characterize the response of the sensing device when embedded in a ballast stack. The ballast will be fouled with different levels of coal dust having different levels of moisture. The response data will be used to develop inverse models to extract railbed fouling from measured device responses. The outcomes of this project will advance this novel assessment and monitoring technique for railbed structure and thus extend the service life of these infrastructure assets.]]></description>
      <pubDate>Fri, 15 Jul 2022 14:59:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/1994175</guid>
    </item>
    <item>
      <title>Field Demonstration of Advance Landslide Warning Index for Railroad Tracks on Amtrak’s Harrisburg Line in Pennsylvania</title>
      <link>https://rip.trb.org/View/1907167</link>
      <description><![CDATA[A previous CIAMTIS project, performed jointly by Penn State University and the University of Delaware looked at the use of “Artificial Intelligence for Advance Landslide Warning along Railroad Tracks in Pennsylvania and Delaware”. This activity, which is in the process of being completed this academic year, used video footage from Amtrak’s track geometry inspection car to examine several segments of track along Amtrak’s Harrisburg right of way for approximately 10 out of the total 195-mile route. Approximately 17 videos, taken over a period of 4 years were examined. This data included high landslide risk zones, as defined by Amtrak’s geotechnical engineer and before and after videos of a recent landslide on the line (June 2021). The results of the previous project allowed for the development of an AI based landslide risk index, based on the analysis of approximately 14,000 frames from the right-of-way video. The methodology used these video files together with data from other sources to include railway identified geo-hazards (and attributes) and USGS open-source data.
Based on discussions with Amtrak’s geotechnical engineer, there is very serious interest in this methodology, which has the potential for allowing Amtrak to assess landslide risk across its entire range of national operations; approximately 21,000 miles of track. Since it is based on analysis of video images taken from Amtrak’s current track geometry inspection car, it can be readily implemented by Amtrak on all its routes, since Amtrak inspects all of its US and Canadian operating routes with its track geometry inspection car. In order to refine and validate the methodology and to provide Amtrak with this ability, it is proposed that a comprehensive landslide risk evaluation be performed on the entire Harrisburg line, approximately 195 route miles in length (double track the entire way). Amtrak has agreed to provide the additional video data files (it provided the 17 files used in the previous activity) and to work with our team to help determine risk criterion that can be applied.
Using this data, it will be possible to further extend the existing analysis to provide a more comprehensive risk index and to determine if landslide precursors can be developed such as: leaning trees, tension cracks, overtopping, debris, slope angle (pitch), slope composition, cut height, etc.
]]></description>
      <pubDate>Mon, 31 Jan 2022 15:16:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/1907167</guid>
    </item>
    <item>
      <title>The Feasibility of Promoting Local Rail Vibrations Using Electromechanical Impedance Method</title>
      <link>https://rip.trb.org/View/1890169</link>
      <description><![CDATA[The mission of this project is to serve the rail industry by improving infrastructure safety and reliability with minimized risks of internal rail defects and rail thermal buckling. The team will develop an electromechanical impedance (EMI) measurement system to promote local rail vibrations, which were recently found to be promising tools for both rail structural integrity inspection and RNT estimation.

The local rail vibrations are the vibrational modes that are easy to promote, highly localized, and immune from boundary conditions. The fundamental mechanism of this phenomenon is deeply rooted from guided wave propagation in rails. Previously, local rail vibrations were promoted by impulse excitation, such as impactor and pulse laser, which lack a control flexibility on input energy and frequency. The team proposes to investigate the usage of EMI method for a consistent local rail vibration promotion, and successfully conducted preliminary numerical simulation to prove its feasibility. The proposed mission will be accomplished by developing an innovative capability of consistent excitation and detection of local rail vibrations, and advancing the state-of-the-art of rail defect detection rail neutral temperature (RNT) measurement.]]></description>
      <pubDate>Thu, 04 Nov 2021 14:50:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/1890169</guid>
    </item>
    <item>
      <title>Development of A Turnout Rail Break Warning (TRBW) System
Based on Distributed Optical Fiber Sensing (DOFS) Technologies</title>
      <link>https://rip.trb.org/View/1688106</link>
      <description><![CDATA[Recently, rail breaks at turnouts have been identified more and
more frequently. An investigation by the European Union
found that among turnout rail breaks more than 40% occurred
at the switch rails (Figure 2). In the past decades, around 20%
of track maintenance and renewal budgets have been spent on
switches, not counting the cost associated with
disrupted/delayed services (Kassa, et al. 2019). Currently, the
rail industry relies primarily on periodic checks to detect rail
cracks, using either ultrasonic vehicles (such as Sperry) or
visual inspections. To improve railroad operation safety,
realtime rail-break detection for turnouts is a great need. In
this proposal, a turnout rail break warning (TRBW) system
based on fiber optical and NB-IoT technologies is proposed.
Although many optical sensing technologies have been
developed, those designated as Distributed Optical Fiber
Sensing (DOFS) are particularly attractive. By attaching a
fiber-optic cable directly to a rail, DOFS is capable of making
strain measurements continuously along the entire length.]]></description>
      <pubDate>Thu, 20 Feb 2020 17:06:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1688106</guid>
    </item>
    <item>
      <title>Efficacy and Durability of Advanced Retrofitted Emissions Controls for Passenger Service Diesel Locomotives</title>
      <link>https://rip.trb.org/View/1651044</link>
      <description><![CDATA[In the next several years, the NCDOT Rail Division will add locomotives to the fleet it provides for the Amtrak-operated Piedmont passenger rail service between Raleigh and Charlotte. NCDOT is taking a leadership role in demonstrating new retrofit Blended After-Treatment System (BATS) emission controls for both existing and newly added locomotives. These emission controls are based on selective
catalytic reduction (SCR) for control of nitrogen oxides (NOx) emissions and diesel particle filters for control of particulate matter emissions. NOx and PM are harmful to public health and are regulated with respect to emissions and air quality. The effectiveness of these controls will depend on actual passenger rail service for actual duty cycles on the Piedmont route. Furthermore, SCR effectiveness may depend on exhaust temperature, which varies depending on engine load, and the durability of both SCR and DPF under retrofit conditions for a diesel locomotive has not been quantified. The research will include the following tasks: (1) development of a detailed measurement plan for rail yard and over-the-road measurements, using portable emission measurement systems, to quantify the activity, energy use, and emissions of each locomotive with the retrofitted BATS; (2) rail yard measurements to assess performance of the retrofitted BATS, especially with respect to emissions
reduction and impacts on energy operation that affect energy use; (3) over-the-road measurements for the same purposes of assessing the performance of the retrofitted BATS, especially with respect to emissions reduction and impacts on operation that affect energy use; and (4) update of a planning-level model for
estimating the energy use and emissions of a typical Piedmont train. The model will be based on secondby- second (1 Hz) speed, acceleration, rail grade, and rail curvature. The model will enable calculation of differences in energy use and emissions based on the use of BATS-retrofitted locomotives, biofuels,
operational strategies, and changes in the corridor (e.g., replacing a grade crossing with grade separation). These tasks will be done based on fuels specified by NCDOT, which are expected to include ultra low sulfur diesel (ULSD) as a baseline and a biodiesel blend such as B20. This project will support increased operational efficiency by providing an empirical data-driven basis for fuel choice and qualification of the Blended After-Treatment Systems on multiple locomotives,
by quantifying the consistency of the BATS performance when comparing locomotives, and by updating a planning tool to help evaluate and prioritize adoption of emission controls, fuels, and capital improvements to the rail corridor and to identify opportunities for improved operational practices (such as modifying train duty cycles to reduce energy use and emissions). Reductions in energy use lead to
operational cost savings. Sustained durable long-term reductions in emissions will help NCDOT apply for additional Federal grants to further reduce emissions by extending BATS retrofits and other emission reduction measures to additional locomotives in the fleet. The updated planning level model for train operations will help evaluate the operational implications of broader deployment of BATS. The field
measurements of the environmental performance of NCDOT equipment will be helpful for environmental planning, to inform regulatory issues, and for communication of the effectiveness of NCDOT emission reductions to the public and other stakeholders. NCDOT will use the results of this research to make decisions regarding selection of fuels, qualification of emission controls, priorities for capital improvements, and opportunities for improved energy efficiency. The research results will quantify the real-world effect of fuels and emission control technologies on locomotive energy use and emissions from which a decision will be made regarding fuel selection and retrofits for additional locomotives for ongoing train operations. The research results will enable NCDOT to independently assess the real-world performance of exhaust after-treatment systems to determine vendor compliance with contract requirements and to quantify the real world benefits of the
new technology. The results will enable NCDOT to evaluate the effect of emissions controls, fuels, operational improvements, and corridor improvements on energy and emission operational impacts.]]></description>
      <pubDate>Mon, 16 Sep 2019 14:46:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/1651044</guid>
    </item>
    <item>
      <title>Leveraging High-Resolution LiDAR and Stream Geomorphic Assessment Datasets to Expand Regional Hydraulic Geometry Curves for Vermont: A Blueprint for New England States (C5.2018)</title>
      <link>https://rip.trb.org/View/1591306</link>
      <description><![CDATA[Regional hydraulic geometry curves for Vermont and surrounding portions of New England states will be updated through consideration of additional observations, and through application of advanced statistical techniques (e.g., clustering, multiple linear regression, Bayesian inference) that leverage newly-available high-resolution laser radar (LiDAR) and stream geomorphic assessment data. This will result in updated regional curves (or sets of curves) and will be made publicly-available for use by transportation departments of the New England states, as well as engineers and scientists working at private firms, non-governmental organizations and state and federal agencies. Updated curves will support sizing of stream crossing structures as well as embankment design for roads and rails that share narrow valleys with rivers. Geomorphically-compatible structures will have greater resilience to extreme flood events and will support aquatic organism passage objectives.]]></description>
      <pubDate>Sun, 10 Mar 2019 10:20:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1591306</guid>
    </item>
    <item>
      <title>Future-Proof Transportation Infrastructure through Proactive, Intelligent, and Public-involved Planning and Management (4.2)</title>
      <link>https://rip.trb.org/View/1590601</link>
      <description><![CDATA[The objective of this study is to future-proof transportation infrastructure (e.g., highways, railways, and bridges) through proactive, intelligent, and public-involved planning and management so that they can prepare appropriately, minimize impact, and capitalize opportunities in the face of future events, changes, and needs. Examples of future events, changes, and needs include climate change, sea level rise, and extreme weather in the northeast region. The ultimate goal of this study is to generate products that facilitate informed decision making in new and existing transportation infrastructure planning and management by incorporating future risks and opportunities into consideration. Therefore, new and existing transportation infrastructure can not only meet the needs for today, but also for the future.]]></description>
      <pubDate>Tue, 05 Mar 2019 09:32:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1590601</guid>
    </item>
    <item>
      <title>Mobile 3D Printing of Rail Track Surface for Rapid Repairment</title>
      <link>https://rip.trb.org/View/1475696</link>
      <description><![CDATA[The primary objective of this study is to develop 3D metal/composite printing technology for fixing railway surface damage on site, which includes:
(1) Mobile 3D printer system design; (2) Rapid surface cleaning of rail track; (3) 3D printing rail track surface on site; and (4) Trueing surface to achieve precise dimension and surface finish.]]></description>
      <pubDate>Sun, 23 Jul 2017 20:30:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1475696</guid>
    </item>
    <item>
      <title>Developing Acoustic Technology to Detect Transverse Defects in Rail at High Speed (220 mph)</title>
      <link>https://rip.trb.org/View/1475694</link>
      <description><![CDATA[Inspection of rail for defects has historically been performed using ultrasonic technology. While the ultrasonic method has proven to be reliable, limitations exist in regard to the speed of the inspection. The proposed study aims to increase the speed of rail inspection while maintaining a high level of reliability through the use of acoustic emission.

An acoustic emission sensor will be developed capable of detecting rail defects at high speeds. Acoustic detection has been widely applied to detect changes in medium generating distinct sounds. For example, the technology has been successfully utilized to detect automobile crashes at intersections, cable wire breaks in bridges, and defects in wheel-set bearings. The acoustic flaw
detection system will also be paired with an on-board GPS to provide defect location information.

To evaluate the sensor performance field testing will be conducted in three phases. Initial testing will be performed at low speeds with limited defect types to demonstrate the feasibility of the system. The second phase of testing will be completed at medium speeds with a wider range of defect types. Finally, the third phase of field testing will be conducted in China at speeds up to 180 mph. Throughout the field testing program the flaw detection system will be continually
improved based on the research findings.

The expected outcome of the research will be a high speed flaw detection system using acoustic technology. Ultimately, the goal of the research is improve rail inspection speed and reliability thereby enhancing rail safety.]]></description>
      <pubDate>Sun, 23 Jul 2017 20:03:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1475694</guid>
    </item>
    <item>
      <title>Rail Fatigue Life Forecasting Using Big Data Analysis Techniques</title>
      <link>https://rip.trb.org/View/1475692</link>
      <description><![CDATA[Railroad rail represents one of the largest infrastructure costs for railway systems, and is often the largest single maintenance of way expense item. Rail fatigue is one of the primary causes of rail failure, occurring in all modes of rail transit from heavy axle load freight to rail transit.  The current approach to forecasting the fatigue life of rail is a cumulative defect analysis using Weibull equations, which allows for the determination of the rate of defect growth and prediction of defect rates based on cumulative traffic levels (defined in terms of Millions of Gross Tons of Traffic or MGT).   

While the Weibull equations have been effective in projecting the growth rate of rail defects, they are insensitive to such key parameters as axle load, speed, curvature, and rail maintenance activities such as  rail grinding. As such, traditional Weibull forecasts are based on the assumption of homogeneous conditions throughout the entire analysis period and specified location. This has been a very limiting assumption.

With the availability of large volumes of data,  it is now possible to extend the Weibull analysis, using new “Big Data” analysis techniques, to allow for more accurate and effective forecasting of rail life. This research activity makes use of a large volume of rail defect data, representing over 30,000 miles of railroad track over a period of almost 10 years,  to extend ( and possibly replace) the existing Weibull forecasting models to overcome many of the deficiencies of the current model and to allow for more accurate rail life forecasting. These analyses are expected to bring out underlying relationships in the role of infrastructure on the development of rail defects, without requiring an assumption on the nature of the relationship beforehand.

The updated rail forecasting equation(s) then can be used to better predict the rate of rail defect development, allowing more accurate management of the expensive rail replacement process.  It should also become possible to gauge the change in the rate of defects due to infrastructure changes, allowing better control over future maintenance costs.
]]></description>
      <pubDate>Sun, 23 Jul 2017 19:41:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1475692</guid>
    </item>
    <item>
      <title>Development and Validation of a New Generation Rail Wear Model
Using Emerging Big-Data Analytic Techniques</title>
      <link>https://rip.trb.org/View/1475691</link>
      <description><![CDATA[The proposed research is to develop a more comprehensive rail wear degradation model utilizing emerging big-data techniques that are relevant to the types of data readily available to the railway industry.  This data represents a large set of data captured through automated inspection.  A major US Class 1 railroad with over 20,000 miles of track has already agreed to provide data from their rail profile measurement systems (ORIAN) onboard three of their track geometry cars.  This would represent over 60,000 miles of rail wear data taken per year, corresponding to three or more rail wear measurements per year for a multi-year period. 
The goal is to replace the generally “simplistic” wear models in use today with a higher end-wear forecasting model that accounts for the key influencing factors for rail wear: (1) traffic information, (2) rail type, (3) level of lubrication, (3) curvature, (4) grade, and (5) other influencing factors.  Big data analysis techniques such as Data Mining, Data Fusion, and Sensor Fusion will be used.
It is expected that the resulting model will be used in maintenance planning and management of the rail infrastructure to allow for modeling large sets of rail wear (and profile) data, along with other railway physical asset and inspection data to predict effective replacement points based on rail wear standards.  As such, it is a strong fit to the theme of the University Transportation Centers (UTC) activity.
]]></description>
      <pubDate>Sun, 23 Jul 2017 19:33:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1475691</guid>
    </item>
    <item>
      <title>Paving the Way for Autonomous and Connected Vehicle Technologies in the Motor Carrier and Rail Industries</title>
      <link>https://rip.trb.org/View/1436307</link>
      <description><![CDATA[This research sought to evaluate the broad impacts that automated and connected vehicle technologies can have on both the motor carrier and rail industries. The studies look at potential safety considerations and infrastructure needs that will be required to support the mass adoption of these emerging technologies, as well as the potential costs and benefits as they come into the market. 

Using large truck crash data from 2013 through 2015 obtained from the Missouri State Highway Patrol, chi-square automatic interaction detection (CHAID) decision trees were estimated to examine the effect of autonomous vehicle (AV) and connected vehicle (CV) technologies on motor carrier crash severity. Results suggest that the greatest contributory predictors of crash severity outcomes are driving too fast for conditions, distracted/inattentive driving, overcorrecting, and driving under the influence of alcohol. If these circumstances are altered by AV and CV technologies, it is suggested that between 117 and 193 severe crashes involving large trucks could be prevented annually in Missouri alone. To render such safety benefits, key vehicle needs include autonomously controlling acceleration and steering, monitoring of the environment, and responding to dynamic driving environments without the need for human intervention. Importantly, the safe operations of a system that can perform such AV and CV tasks require readable lane markings, traffic signals and signs, managed or dedicated lane usage, and dedicated refueling and/or recharging facilities.

Further, since the development and adoption of these technologies are likely to be gradual, three phases of adoption were posited and analyzed. Depending on the degree of autonomy that is available, the motor carrier industry could achieve up to a 42.1% reduction in average cost per mile. And if fully autonomous technology was made available for use in the motor carrier industry, it is estimated that the American rail freight industry could see a 19% to 45% drop in demand.]]></description>
      <pubDate>Thu, 01 Dec 2016 12:47:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1436307</guid>
    </item>
    <item>
      <title>St. Louis Freight Development Plan</title>
      <link>https://rip.trb.org/View/1402936</link>
      <description><![CDATA[The purpose of the St. Louis Freight Development Plan project is to investigate the economic and environmental effects of various freight-related projects around the St. Louis area. This is in collaboration with Bi-State Development, which seeks to prioritize and quantify the impacts of potential freight development projects in the region. All findings are intended to supplement a grant proposal by Bi-State Development asking for federal funds to invest in these projects. If awarded, the grant will allow organizations around St. Louis to improve and construct infrastructure that will enhance the capacity for St. Louis to deal with the flow of freight moving through the city.
As demand for freight increases nationwide, these projects also poise St. Louis to become a crucial transportation center that could absorb the overflow of freight traffic from competing cities such as Chicago and Kansas City. Enhancing freight lines has the potential to not only benefit producers and consumers across the country, it would also generate economic benefits for the St. Louis region in the form of job creation and attracting more businesses to the metropolitan area.
Economic effects will largely stem from the increased capacity to deal with freight and the potential jobs that will be created to handle the influx, as well as the businesses that might enter the St. Louis area as a result. Moreover, environmental effects are quantified largely through emissions reductions and habitat loss. Considering the fuel efficiency of barge and rail freight compared to that of trucks, any expansion of the former translates into fewer emissions per ton-mile as freight transport substitutes toward the cheaper, more fuel-efficient modes. Similarly, the projects also seek to improve road conditions, which will increase truck speed and reduce overall delays that also equate into higher environmental costs. Overall, the projects are expected to generate a net positive benefit for the St. Louis region.]]></description>
      <pubDate>Mon, 04 Apr 2016 10:03:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1402936</guid>
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