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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Freight Route Management Application for the Port of Anchorage</title>
      <link>https://rip.trb.org/View/2512620</link>
      <description><![CDATA[The objective of this research is to develop and evaluate an intelligent transportation management application for improving the efficiency, safety, reliability, and cost-effectiveness of freight and fuel truck movement to/from the Port of Alaska located in Anchorage, Alaska. This is a partnership project between the City of Anchorage’s Port of Alaska and Alaska Department of Transportation and Public Facilities (DOT&PF).  Truck transportation network located at the port will be able to better route and stage cargo transport within the Port of Alaska footprint. The application could be used outside the port by truck drivers, Alaska 511, and traffic operations centers.]]></description>
      <pubDate>Fri, 21 Feb 2025 21:42:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2512620</guid>
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      <title>Anchorage Protected Bike Lane Pilot Study</title>
      <link>https://rip.trb.org/View/2512618</link>
      <description><![CDATA[This project documents and presents the results of a protected bike lane (PBL) pilot study conducted on two different Anchorage roads during the summers of 2023 and 2024. The pilots designed and installed one-way PBLs on both sides of Pine Street and McCarrey Street and a two-way PBL on the left side of 6th Avenue and A Street using temporary plastic flex-posts, rubber curb stops, traffic signs, striping, markings, and bike signals installed on a temporary basis. Public outreach measured cyclist and pedestrian comfort and public perception of different user groups, while traffic analysis quantified cycling usage, vehicle speed and travel time, and crash statistics. The study found that pilot protected bike lanes increased cyclist comfort and cyclist usage, resulted in minimal impact on traffic circulation, and identified common concerns from business owners and drivers for future projects. Moreover, the study demonstrated that pilot installations with lane conversions and temporary materials can be effectively implemented for use by the travelling public to investigate existing safety concerns, test out designs, and gather extensive public feedback.]]></description>
      <pubDate>Fri, 21 Feb 2025 21:32:01 GMT</pubDate>
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      <title>Assessment of Traffic Congestion in Anchorage Utilizing Vehicle-Tracking Devices and Intelligent Transportation System Technology</title>
      <link>https://rip.trb.org/View/1307046</link>
      <description><![CDATA[Traffic is increasing in most urban cities around the world, with Anchorage being no exception. According to the United States Census Bureau, the population of Anchorage has increased by over 9.0% (8000 people per year) since the year 2000 [18]. With an increase in population comes an increase in the number of vehicles on the road, which adds to traffic congestion. The exact impact of this increase is not known because the current means of determining congestion in Anchorage is through vehicle counters and sparsely-placed video cameras (that may or may not be monitored). In addition, the only ways drivers in Anchorage can be informed of current traffic conditions is through radio and television broadcasts and 511 information (which is not always updated in a timely manner). Vehicle-tracking devices utilizing a vehicle-to-infrastructure (V2I) architecture have already been installed in 15 vehicles in the city of Anchorage, and this project will install these devices in an additional 30 vehicles. Further, taxi fleets, emergency response vehicles, transit vehicles, shipping vehicles, navigation system companies, and any other organizations that may be tracking speed, location, and direction of vehicles will be contacted to attempt to leverage the data that is already being collected. From all of the data that is being collected and will be collected, a realistic measurement and understanding of congestion in Anchorage will be determined. As more vehicles are equipped with tracking devices, the data will become more accurate and will include a larger range of the city and state. In addition, the Department of Transportation (DOT) does not currently have much data on the origin and destination of individual vehicles, and this project will provide that information. The overall delay experienced by individual drivers and the extent of cut-through and spill-over traffic due to congestion will be determined. The data gathered in this project will all be anonymously exposed in a public web interface called FreeSim (http://www.freewaysimulator.com, Figure 1) that can be viewed by anyone. The current speeds of the tracked vehicles will be displayed using different colors on the roads, as well as showing exact speeds when hovering the mouse over a road or vehicle. Historical data will be maintained so that algorithms can be executed to determine daily, weekly, monthly, seasonal, and annual changes in traffic. In short, this project will provide the DOT, the population of Anchorage, and anyone in the world with data about the current state of traffic on the roads of Anchorage and Alaska. The novelty of this project is that the data will be gathered from a continuous flow of data rather than at discrete locations, as is the case with many traffic analysis tools currently in use. As additional resources becomes available, drivers will have the ability to view the current traffic conditions during their commutes, and further, the system will be able to send drivers the streets to take to minimize the amount of time spent in traffic.]]></description>
      <pubDate>Thu, 24 Apr 2014 01:01:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/1307046</guid>
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      <title>Integrating Climate Change Considerations Into MPO Long Range Transportation Forecasting</title>
      <link>https://rip.trb.org/View/1230278</link>
      <description><![CDATA[In the last few years, there is an increasing number of scientific evidences supporting the hypothesis that emissions of greenhouse gas (GHG) into the atmosphere is contributing to changes in the earth's climate with many detrimental effects that are already taking place. A consensus toward the need for actions for the reduction of GHG has grown among the public and elected officials. In the United States, a major source of GHG emissions is Carbon dioxide (CO2) emissions from personal vehicles and trucks. The amount of CO2 emissions from mobile sources is tied directly to the amount of fuel consumed, which is then tied to the total Vehicle Miles Traveled (VMT). In order to devise effective multimodal transportation policies and financial programs for VMT reduction (i.e., reduction in CO2 emissions) in a metropolitan area, a travel demand forecasting model with sufficient spatial and temporal resolution is needed to generate traffic forecasts to be used as inputs to air quality models such as EPA's MOBILE 6 or MOVES model. It is noted that MOVES, which will replace MOBILE 6 as the next generation mobile source emission model, require traffic volume forecasts with spatial and temporal details that exceed what the current travel demand models can produce. The two Metropolitan Planning Organizations (MPO) in Alaska are Anchorage Metropolitan Area Transportation Solutions (AMATS) and Fairbanks Metropolitan Area Transportation System (FMATS). Each of them maintains a travel demand forecasting model for the purpose of long-range transportation plan updates. This study proposes to use the two MPO models as the case studies to first examine the inefficiencies of the two models in terms of meeting data requirements for MOVES. The inadequacies for the two models to address the effectiveness of reflecting GHG reduction policies will also be examined. Effective methods to address the models' inefficiencies will then be researched and developed. The newly improved models will be validated and calibrated with most current observed data. The models will be tested with a forecasting scenario to demonstrate their capability to reflect the effectiveness of GHG emission reduction of proposed transportation measures.]]></description>
      <pubDate>Thu, 03 Jan 2013 13:57:24 GMT</pubDate>
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