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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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      <title>Biometrics in roadway design: incorporating the subliminal human experience</title>
      <link>https://trid.trb.org/View/2554014</link>
      <description><![CDATA[Numerous tools are available to transportation planners and designers to understand how people use and navigate roadways while driving. Innovations in biometric technology present new methods to tap collective wisdom and measure visual attention and emotion elicited by urban elements, like streets. Photos and videos were collected during Memorial Drive’s seasonal road closure in Cambridge, Massachusetts to measure these responses. The authors found a discrepancy between car-centered urban design and human-centered urban design, offering new methodological approaches to advance urban research, policy, and planning.]]></description>
      <pubDate>Mon, 21 Jul 2025 14:42:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554014</guid>
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    <item>
      <title>Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery</title>
      <link>https://trid.trb.org/View/2526468</link>
      <description><![CDATA[Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This work is the first to utilize roadside urban imagery to aid transit agencies and practitioners in improving travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the General Transit Feed Specification real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, automated vehicle location data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results of this exploratory study illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80 and 85%. We assess the interpretability of the ViT model’s predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the added value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving transit operations and passenger real-time information.]]></description>
      <pubDate>Tue, 01 Apr 2025 09:51:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526468</guid>
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    <item>
      <title>Air quality monitoring using mobile low-cost sensors mounted on trash-trucks: Methods development and lessons learned</title>
      <link>https://trid.trb.org/View/2471388</link>
      <description><![CDATA[Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, the authors propose a technique to identify aerosol hotspots. Second, they employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, the authors obtain more robust results. Third, in order to evaluate source signatures in cities, they propose another technique, in which they cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. The authors test these methods using a dataset they collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.]]></description>
      <pubDate>Thu, 26 Dec 2024 15:03:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2471388</guid>
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    <item>
      <title>Cambridge Underground: Challenges of Sewer Separation and Stormwater Management</title>
      <link>https://trid.trb.org/View/2218577</link>
      <description><![CDATA[Cambridge is an old city that is seriously in need of the infrastructure improvements that are now being implemented. Sewer separation and stormwater management projects will alleviate the problem of flooding and threats to public health caused by untreated wastewater discharged to low-lying neighborhoods and the Charles River during heavy rainstorms. Since 1998, S E A Consultants Inc. and Montgomery Watson Harza (S E A/MWH) have been working with the Cambridge Department of Public Works to pinpoint problems within the existing system; develop ways to achieve the program objectives; and plan, design, and oversee construction of over $325 million in city-wide infrastructure improvements over the next 15 years. Unconventional construction methods in heavily developed urbanized areas such as use of large underground storage tanks, maximizing use of surface storage potential, use of best management practices to reduce floatables and sediment discharges to the Charles River, and applications of trenchless technologies to minimize construction disruptions while improving the infrastructure have been highlights of the program. The paper describes various aspects of the Cambridge sewer separation and stormwater management program, the condition of the existing infrastructure, improvements needed to support future growth, and innovative techniques used to address water quality and quantity considerations. Planning, design, construction, utilities, traffic and pedestrian management, and community participation will be explained as they relate to this program.]]></description>
      <pubDate>Wed, 18 Dec 2024 13:29:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2218577</guid>
    </item>
    <item>
      <title>Developing Crash Modification Factors for Separated Bicycle Lanes</title>
      <link>https://trid.trb.org/View/2260095</link>
      <description><![CDATA[This project investigated how conversions of a traditional bicycle lane to a separated bicycle lane (SBL) influence the safety performance of the roadway. The study further assessed the role of the type of vertical element used in the SBL and its influence on safety performance based on a reduction in bicycle-related crashes. The study models included data from Cambridge, Massachusetts; San Francisco, California; and Seattle, Washington. Data from Austin, Texas, and Denver, Colorado, were then used for model validation. The study produced exposure models and crash modification factors (CMFs) for Cambridge, San Francisco, and Seattle. The research summarizes the development of individual city models and a three-city composite model for CMFs. Assuming a baseline condition that uses a traditional bicycle lane and a countermeasure that upgrades the bicycle lane to an SBL, the resulting CMF values varied based on the type of vertical element. The resulting CMFs ranged from 0.28 to 0.60; however, the condition of converting a traditional bicycle lane to an SBL that uses flexible posts (known as flexible posts) had a rounded CMF value of 0.50. This outcome suggests that this SBL treatment can reduce crashes by 50 percent. The report provides additional CMFs for variations of the bicycle lane and the vertical elements. These CMFs had similar results. The findings from this study can be useful to an agency interested in reducing bicycle-related crashes.]]></description>
      <pubDate>Mon, 16 Oct 2023 09:05:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2260095</guid>
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    <item>
      <title>A comparative analysis of pedestrian network connectivity and accessibility using network approximation</title>
      <link>https://trid.trb.org/View/2205566</link>
      <description><![CDATA[Since the introduction of the Americans with Disabilities Act (ADA), transportation stakeholders have endeavored to include compliant and accessible pedestrian facilities as a part of their roadway improvement and maintenance projects. These projects, often completed in sections, have resulted in piecemeal pedestrian networks with limited holistic ADA compliance in many areas, the full extents of which are often unknown. This research introduces a methodology for approximating pedestrian networks and then employs the resulting data in a comparison of the connectivity of optimal and accessible pedestrian networks. The pedestrian network approximation method used street centerline and curb ramp data in combination with GIS tools to generate pedestrian optimal networks across four metropolitan areas: Cambridge and Boston in Massachusetts, and Seattle and Tacoma in Washington. These optimal network extents were then analyzed for ADA compliance to create accessible network models where only ADA compliant routes remained. Traffic Analysis Zones served as analytical units, for which alpha (α), beta (β), and gamma (γ) structural connectivity indices were calculated to compare the two pedestrian network conditions for each city. On average, the four cities exhibited a 93.47% reduction in alpha connectivity, and a 48.48% reduction in both beta and gamma connectivity indices, confirming that pedestrians depending upon ADA compliance for travel experience severely reduced mobility.]]></description>
      <pubDate>Wed, 26 Jul 2023 15:59:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2205566</guid>
    </item>
    <item>
      <title>Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery</title>
      <link>https://trid.trb.org/View/2132416</link>
      <description><![CDATA[While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods.]]></description>
      <pubDate>Tue, 28 Mar 2023 09:56:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2132416</guid>
    </item>
    <item>
      <title>Cambridge Clean Fleet Initiative: 2030 GHG Reduction Scenarios and Proposed Target</title>
      <link>https://trid.trb.org/View/1900423</link>
      <description><![CDATA[In 2016 the Department of Public  Works (DPW) began a Clean Fleet initiative that supports the City of Cambridge’s 2050 carbon neutrality goals under the Metro Mayors Climate Commitment and complements the City’s Net Zero Action Plan. To address fleet GHG emissions, the City is working with the U.S. Dept. of Transportation’s Volpe National Transportation Systems Center (Volpe) to (1) develop  strategies to decrease greenhouse gas emissions (GHG) from the municipal fleet; (2) establish a 2030  fleet GHG emissions reduction target from the 2016 baseline; and (3) create a fleet implementation plan  to reach that target. This report addresses the first two priorities; an implementation plan will be  developed subsequently]]></description>
      <pubDate>Thu, 13 Jan 2022 13:52:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1900423</guid>
    </item>
    <item>
      <title>Cambridge Clean Fleet Initiative: Technical Assistance for Selected Special Applications of Clean Fleet Technologies</title>
      <link>https://trid.trb.org/View/1900425</link>
      <description><![CDATA[Advanced technology solutions underpin the 2030 Target for the Clean Fleet Initiative, based on expectations that these technologies will be implementable in a wide range of City vehicles. Within the  target vehicle population, a number of special applications have been identified that need additional research to anticipate and mitigate potential  implementation  challenges. In consultation with  Cambridge, Volpe has solicited input from fleets and technical organizations with implementation experience related to three special application areas. These areas are important to address in the  course of attaining the 2030 Target or Stretch Target reduction levels: (1) Plow, water, and other pick-up  truck requirements in terms of power and payload, and whether or not lower power and torque can fulfill  those requirements. (2) Idling activity: What idle reduction device can provide air conditioning, heat, and power radio and possibly other power needs? What idle reduction technologies can address various departments’ specific operational needs? (3) Power Take-Off – what technologies can address PTO  loads and how significant are the loads?]]></description>
      <pubDate>Thu, 13 Jan 2022 13:52:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1900425</guid>
    </item>
    <item>
      <title>Estimating Pedestrian Flows on Street Networks</title>
      <link>https://trid.trb.org/View/1881151</link>
      <description><![CDATA[Problem, research strategy, and findingsCity governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts.Takeaway for practiceThe proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic.]]></description>
      <pubDate>Tue, 12 Oct 2021 16:53:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1881151</guid>
    </item>
    <item>
      <title>A Spatial Comparison of Roadway Lighting and Nonmotorist Crashes in Cambridge, MA</title>
      <link>https://trid.trb.org/View/1867616</link>
      <description><![CDATA[Dark lighting conditions, including those occurring at dawn and dusk, are correlated with increased nonmotorist crash frequency owing to reduced visibility, but little research has been done that investigates the spatial relationship between roadway lights and nonmotorist crashes on a community scale. This research used kernel density estimation methods to calculate the commonalities between geolocated streetlight data and non-motorist-vehicle crashes from 2010 to 2018 in Cambridge, Massachusetts. It was observed that dawn, dusk, and darkness showed a significant correlation between nonmotorist crashes and the absence of roadway lighting, all exceeding the control analysis undertaken with crashes occurring in daylight. The Getis-Ord Gi* hot spot cluster analysis indicated that areas with the greatest density of streetlights were associated with fewer nonmotorist crash hot spots. Future research seeks to corroborate these findings with data from other cities and to assess roadway lighting as a facet of pedestrian network connectivity.]]></description>
      <pubDate>Thu, 05 Aug 2021 09:18:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1867616</guid>
    </item>
    <item>
      <title>Direct demand modelling approach to forecast cycling activity for a proposed bike facility</title>
      <link>https://trid.trb.org/View/1763279</link>
      <description><![CDATA[In the United States, planning and design efforts to generate bike-friendly environments through the greater provision of safe, low-stress bike infrastructure in our cities continue to advance. In Cambridge, Massachusetts, construction of the Grand Junction Pathway – an envisioned shared-use pathway – is at the heart of a citywide effort to enhance its active transportation system. However, a challenge – shared by many public agencies given that data on cycling activity are rarely frequently systematically gathered – is the creation of a baseline estimate of cycling demand for this planned network link. Using short-duration manual data supplemented with long-duration count data, this study employs a state-of-the-practice method for generating annual average daily bicycle trips for current bike network facilities. A statistical modelling strategy is then undertaken to forecast the volume of daily cyclists that the proposed off-street, shared-use path could expect to attract given its physical context and the socioeconomic attributes of nearby residents.]]></description>
      <pubDate>Fri, 12 Mar 2021 10:06:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1763279</guid>
    </item>
    <item>
      <title>Sidewalk Static Obstructions and Their Impact on Clear Width</title>
      <link>https://trid.trb.org/View/1768798</link>
      <description><![CDATA[Data on sidewalks have long been deficient. But advances in remote sensing are beginning to increase data prevalence and accuracy. These sidewalk datasets rarely, if ever, account for static obstructions in the sidewalk such as signs, street furniture, or trees. This paper seeks to determine how much of a difference accounting for static obstructions will make when measuring the clear width of sidewalks. We extracted the minimum width of sidewalk surfaces—both with and without accounting for static obstructions—for the entirety of Cambridge, MA, using new GIS methods described in this paper. We then compared these results against Americans with Disabilities Act (ADA) standards for clear width as well as national and federal sidewalk guidelines. The results suggest a significant decrease in the average clear width of sidewalks when accounting for static obstructions. More specifically, the clear width of the average sidewalk drops from 4.5?ft (1.4?m) to 3.5?ft (1.1?m). The percentage of sidewalk segments meeting the 3-ft ADA standard drops from 78% to 51% when accounting for static obstructions. For the proposed 4-ft (1.2-m) ADA standard, it plunges from 59% of sidewalk segments meeting the width threshold to 31%. These results demonstrate that not accounting for static obstructions could lead to a gross overestimation of seemingly adequate sidewalks and an unrealistic assessment of sidewalk infrastructure and pedestrian accessibility.]]></description>
      <pubDate>Wed, 17 Feb 2021 10:44:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1768798</guid>
    </item>
    <item>
      <title>Home-work carpooling for social mixing</title>
      <link>https://trid.trb.org/View/1740307</link>
      <description><![CDATA[Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social benefits should be traded off with transportation efficiency. By incorporating traveler’s social characteristics into a recently introduced network-based approach to model ride-sharing opportunities, we define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally efficient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.]]></description>
      <pubDate>Tue, 27 Oct 2020 12:27:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1740307</guid>
    </item>
    <item>
      <title>A cycling-focused accessibility tool to support regional bike network connectivity</title>
      <link>https://trid.trb.org/View/1714235</link>
      <description><![CDATA[Many cities in the United States are working to become more “bike-friendly” through the provision of new bike infrastructure that is safe and attractive for all types of cyclists, from the timid to assured. These efforts are supported by evidence associating low level of traffic stress facilities with increased cycling activity rates and co-benefits related to the economy, environment, and public health. However, not every mile of bike infrastructure provides the same utility, prompting planning agencies with finite financial resources to search for empirical methods to help evaluate what projects will provide the greatest network connectivity benefit and how disparate projects can complement one another to produce a complete bike network. In this study, the authors introduce the Cyclist Routing Algorithm for Network Connectivity (CRANC), an accessibility-oriented decision-support tool designed to quantify the benefits of new bike facilities for various populations and neighborhoods. Unlike prior tools, this method simulates the route preferences of different cyclist types and trade-offs in travel time and level of traffic stress to model potential changes in destination accessibility that may result from multiple scenarios of citywide and regional bike network expansion. Here, CRANC is applied to the Boston region’s bike network to determine how a proposed shared-use path in Cambridge, Massachusetts will improve accessibility to regional job opportunities and to labor force for employment sites in Cambridge. The authors' introduced decision-support tool produces unique, meaningful results relevant to a variety of stakeholders, and holds promise as a new resource for transportation researchers and practitioners.]]></description>
      <pubDate>Mon, 13 Jul 2020 10:33:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1714235</guid>
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