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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Evaluation of the Highway Construction Workforce Development Program</title>
      <link>https://trid.trb.org/View/2601528</link>
      <description><![CDATA[The Oregon Department of Transportation and Oregon Bureau of Labor and Industries have partnered in a statewide effort—the Highway Construction Workforce Development Program— to recruit, train, and employ a diverse workforce for highway construction jobs throughout the state. This program, which began in 2010, supports a variety of initiatives designed to improve the recruitment and retention of women and people of color in Oregon’s highway construction trades. The total budget for the Program was 1.9 million dollars for 2022-2023. The services evaluated in this report include: pre-apprenticeship programs, supportive services providing financial assistance (i.e., fuel assistance and support for overnight travel; child care; work clothes, tools, and protective equipment; hardship assistance funds), and supportive services providing non-financial assistance (e.g., counseling, budget class). This report provides findings based on data from the Oregon Apprenticeship System (OAS) from 2010-2023 and from administrative records collected from staff implementing the Highway Construction Workforce Development Program. Trades included in this analysis are carpenter, cement mason, electrician, ironworker, laborer, operating engineer, and painter.]]></description>
      <pubDate>Fri, 03 Oct 2025 11:54:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601528</guid>
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    <item>
      <title>Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement</title>
      <link>https://trid.trb.org/View/2596491</link>
      <description><![CDATA[This study investigates the intersection of socio-economic status (SES), race/ethnicity, and the likelihood of fatal bicycle and pedestrian collisions in California. Utilizing data from multiple sources, including the California Statewide Integrated Traffic Records System (SWITRS) and the American Community Survey, The authors analyze how neighborhood SES influences collision outcomes across different racial/ethnic groups across California. The findings reveal that higher SES neighborhoods generally have lower rates of fatal collisions, particularly benefiting White cyclists and pedestrians. However, Black and Hispanic individuals do not experience the same level of safety improvements, highlighting significant racial/ethnic disparities. The study identifies a lack of comprehensive infrastructure in low-income and non-White neighborhoods as a key factor contributing to higher collision rates. Additionally, dangerous driving behaviors and environmental conditions, such as driving under the influence and poor lighting, exacerbate risks in lower SES areas. The authors recommend targeted infrastructure investments, enhanced enforcement of traffic laws, and driver education campaigns to address these disparities. Further research is needed to explore the underlying causes of these differences and develop more effective interventions. This study aims to inform policies and practices that promote safer streets for all communities by understanding and addressing road safety's socio-economic and racial dynamics.]]></description>
      <pubDate>Tue, 23 Sep 2025 17:10:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596491</guid>
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    <item>
      <title>R Data for CPBS Report 23SDSU01 - Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement [supporting dataset]</title>
      <link>https://trid.trb.org/View/2445133</link>
      <description><![CDATA[This study investigates the intersection of socio-economic status (SES), race/ethnicity, and the likelihood of fatal bicycle and pedestrian collisions in California. Utilizing data from multiple sources, including the California Statewide Integrated Traffic Records System (SWITRS) and the American Community Survey, the authors analyze how neighborhood SES influences collision outcomes across different racial/ethnic groups across California. The findings reveal that higher SES neighborhoods generally have lower rates of fatal collisions, particularly benefiting White cyclists and pedestrians. However, Black and Hispanic individuals do not experience the same level of safety improvements, highlighting significant racial/ethnic disparities. The study identifies a lack of comprehensive infrastructure in low-income and non-White neighborhoods as a key factor contributing to higher collision rates. Additionally, dangerous driving behaviors and environmental conditions, such as driving under the influence and poor lighting, exacerbate risks in lower SES areas. The authors recommend targeted infrastructure investments, enhanced enforcement of traffic laws, and driver education campaigns to address these disparities. Further research is needed to explore the underlying causes of these differences and develop more effective interventions. This study aims to inform policies and practices that promote safer streets for all communities by understanding and addressing road safety's socio-economic and racial dynamics.]]></description>
      <pubDate>Thu, 21 Nov 2024 09:24:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2445133</guid>
    </item>
    <item>
      <title>Research on purchasing behavior of foreign city users: the Czech Republic experience</title>
      <link>https://trid.trb.org/View/2348292</link>
      <description><![CDATA[The global expansion of e-commerce has transformed consumer shopping patterns, blurring the lines between physical and online retail. With the influx of foreign residents in cities worldwide, understanding the purchasing behavior of foreign city users has become crucial. This research aimed to discern differences in online and in-store purchasing between these foreign city users and local residents. Through multinomial logistic regression, the study revealed three significant predictors for online purchasing: gender, foreign city user status, and age. Females were 42.6% less likely to purchase online compared to males. Foreign users, without an official foreign status, were also less inclined to make online purchases. Meanwhile, the 18-29-age group showed the highest online purchasing propensity. The study also highlighted the need for businesses to adapt their strategies by focusing on cultural sensitivity, language support, and optimizing last-mile logistics. Insights, understanding these dynamics, could be vital for (physical and online) retailers which need to adapt to the changing urban consumer landscape.]]></description>
      <pubDate>Sat, 18 May 2024 17:07:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2348292</guid>
    </item>
    <item>
      <title>Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price</title>
      <link>https://trid.trb.org/View/2195273</link>
      <description><![CDATA[Profound insights have been gained into which characteristics determine housing prices. These characteristics reflect two different aspects: those which are correlated with the dwelling itself and those which are correlated with the location and the surrounding area. However, few studies precisely looked at the disparities and heterogeneity in these effects across neighborhoods with varied conditions. Also, there lacks studies focusing on the moderate-density cases where housing markets have drawn concerns recently. This study aims to fill this research gap by analyzing these disparities across neighborhoods with different economic and racial/ethnic conditions. Through machine learning approaches, the authors compare the disparities in the impacts of housing status, public transit services, and surrounding environment factors under seven conditions. Results indicate that the heterogeneity in economic conditions could be more significant than racial/ethnical conditions. Through comparison analysis, the authors call policymakers to need to adopt differentiated perspectives on housing price analysis, and future studies should consider the disparities in the impacts across neighborhoods.]]></description>
      <pubDate>Thu, 22 Jun 2023 09:49:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2195273</guid>
    </item>
    <item>
      <title>Redlining and Neighborhood Walking in Older Adults: The 2017 National Household Travel Survey</title>
      <link>https://trid.trb.org/View/2008618</link>
      <description><![CDATA[In the 1930s, Black, working-class, and immigrant neighborhoods were color coded on maps (i.e., redlining) indicating investment risk, which negatively impacted mortgage attainment/homeownership for these groups and led to long-standing segregation by race/ethnicity and socioeconomic status. Limited studies have investigated the health impacts of redlining, particularly among older adults who tend to stay closer to their residences. This study examines whether older adults in historically redlined neighborhoods report less neighborhood walking and whether associations vary by race/ethnicity and income. The sample included 4,651 individuals aged ≥65 years from the 2017 U.S. National Household Travel Survey. U.S. Census tract‒based redlining scores were 1=best, 2=still desirable, 3=definitely declining, and 4=hazardous. Multivariable negative binomial regression tested the associations between redlining and neighborhood walking/day in the overall sample and with stratification by poverty status (analyzed in 2022). Participants were on average aged 73 years, and 11% were African/American Black, 75% were White, 8% were Hispanic/Latinx, and 6% were of other race/ethnicity. Participants reported a mean of 7.1 neighborhood walking minutes/day (SD=20.6), and 60% lived in definitely declining or hazardous neighborhoods. Individuals in hazardous neighborhoods (versus those in best neighborhoods) reported less neighborhood walking (prevalence ratio=0.64; 95% CI=0.43, 0.97). Among those living in poverty, living in definitely declining and hazardous neighborhoods was associated with less neighborhood walking (prevalence ratio=0.39 [95% CI=0.20, 0.79] and 0.39 [95% CI=0.18, 0.82], respectively). Less neighborhood walking was reported among individuals living in neighborhoods with a historic redlining score of definitely declining or hazardous. Future studies using larger, more diverse cohorts may elucidate whether associations differ by race/ethnicity and geographic location/city.]]></description>
      <pubDate>Mon, 26 Sep 2022 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2008618</guid>
    </item>
    <item>
      <title>Impacts of the COVID-19 Pandemic on Telecommuting and Travel</title>
      <link>https://trid.trb.org/View/2012405</link>
      <description><![CDATA[This chapter examines changes in telecommuting and the resulting activity-travel behavior during the COVID-19 pandemic, with a particular focus on California. A geographical approach was taken to “zoom in” to the county level and to major regions in California and to “zoom out” to comparable states (New York, Texas, Florida). Nearly one-third of the domestic workforce worked from home during the pandemic, a rate almost six times higher than the pre-pandemic level. At least one member from 35% of U.S. households replaced in-person work with telework; these individuals tended to belong to higher income, White, and Asian households. Workplace visits have continued to remain below pre-pandemic levels, but visits to non-work locations initially declined but gradually increased over the first nine months of the pandemic. During this period, the total number of trips in all distance categories except long-distance travel decreased considerably. Among the selected states, California experienced a higher reduction in both work and non-workplace visits and the State’s urban counties had higher reductions in workplace visits than rural counties. The findings of this study provide insights to improve the authors' understanding of the impact of telecommuting on travel behavior during the pandemic.]]></description>
      <pubDate>Wed, 07 Sep 2022 09:25:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2012405</guid>
    </item>
    <item>
      <title>Disparities in Activity and Traffic Fatalities by Race/Ethnicity</title>
      <link>https://trid.trb.org/View/1993458</link>
      <description><![CDATA[Traffic fatalities remain a major public health challenge despite progress made during recent decades. This study develops exposure-based estimates of fatalities per mile traveled for pedestrians, cyclists, and light-duty vehicle occupants and describes disparities by race/ethnicity, including a subanalysis of fatality rates during darkness and in urban areas. Estimates of person-miles traveled by mode and race/ethnicity group were derived from the 2017 National Household Travel Survey using replicate weights. Three-year average (2016‒2018) traffic fatalities were measured by mode and race/ethnicity group with the U.S. Fatality Analysis Reporting System. Fatality rates per mile traveled and CIs were calculated for each subgroup as well as separately for trips occurring during darkness and in urban areas. Analysis was conducted in 2021‒2022. Exposure to traffic fatality differs by race/ethnicity group and by mode, indicating that adjustment for differential exposure is needed when estimating disparities. The authors find that fatality rates per 100 million miles traveled are systematically higher for Black and Hispanic Americans for all modes and notably higher for vulnerable modes (e.g., Black Americans died at more than 4 times the rate for White Americans while cycling, 33.71 [95% CI: 21.84, 73.83] compared with 7.53 [95% CI: 6.64, 8.69], and more than 2 times the rate while walking, 40.92 [95% CI: 36.58, 46.44] compared with 18.77 [95% CI: 17.30, 20.51]). Previous estimates that do not adjust for differential exposure may underestimate disparities by race/ethnicity. Observed disparities remained when considering only urban areas and appear to be exacerbated during darkness. Traffic fatalities are a substantial and preventable public health challenge in America. Black and Hispanic Americans have higher traffic fatality rates per mile traveled than White Americans across the transportation system, requiring urgent attention.]]></description>
      <pubDate>Wed, 10 Aug 2022 16:38:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1993458</guid>
    </item>
    <item>
      <title>Facilitating research on racial and ethnic disparities and inequities in transportation: Application and evaluation of the Bayesian Improved Surname Geocoding (BISG) algorithm</title>
      <link>https://trid.trb.org/View/1905620</link>
      <description><![CDATA[ObjectiveRacial and ethnic disparities and/or inequities have been documented in traffic safety research. However, race/ethnicity data are often not captured in population-level traffic safety databases, limiting the field’s ability to comprehensively study racial/ethnic differences in transportation outcomes, as well as our ability to mitigate them. To overcome this limitation, we explored the utility of estimating race and ethnicity for drivers in the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse using the Bayesian Improved Surname Geocoding (BISG) algorithm. In addition, we summarize important recommendations established to guide researchers developing and implementing racial and ethnic disparity research.MethodsWe applied BISG to estimate population-level race/ethnicity for New Jersey drivers in 2017 and evaluated the concordance between reported values available in integrated administrative sources (e.g., hospital records) and BISG probability distributions using an area under the receiver operator curve (AUC) within each race/ethnicity category. Overall AUC was calculated by weighting each AUC value by the population count in each reported category. In an exemplar analysis using 2017 crash data, we conducted an analysis of average monthly police-reported crash rates in 2017 by race/ethnicity using the NJ-SHO and BISG sets of race/ethnicity values to compare their outputs.ResultsWe found excellent or outstanding concordance (AUC =0.86) between reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. We found poor concordance for American Indian/Alaskan Native drivers (AUC= 0.65), and concordance was no better than random assignment for Multiracial drivers (AUC = 0.52). Among White, Hispanic, Asian/Pacific Islander, and American Indian/Alaskan native drivers, monthly crash rates calculated using both NJ-SHO reported race/ethnicity values and BISG probabilities were similar. Monthly crash rates differed by 11% for Black drivers, and by more than 200% for Multiracial drivers.ConclusionFindings of excellent or outstanding concordance between and mostly similar crash rates derived from reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers (98.9% of all drivers in this sample) demonstrate the potential utility of BISG in enabling research on transportation disparities and inequities. Concordance between race/ethnicity values were not acceptable for American Indian/Alaskan Native and Multiracial drivers, which is similar to previous applications and evaluations of BISG. Future work is needed to determine the extent to which BISG may be applied to traffic safety contexts.]]></description>
      <pubDate>Tue, 22 Feb 2022 10:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1905620</guid>
    </item>
    <item>
      <title>An Analysis of Traffic Fatalities by Race and Ethnicity</title>
      <link>https://trid.trb.org/View/1861214</link>
      <description><![CDATA[The purpose of this report was to review the research on the impact of traffic fatalities on Black, Indigenous and People of Color (BIPOC) and to determine steps that states and communities can take to improve traffic safety equity. The key findings from previous research provided recent statistics on motor vehicle traffic-related pedestrian deaths; fatal injuries among children by race and ethnicity; ethnicity and alcohol-related fatalities; an analysis of pedestrian injury, built environment, travel activity, and social equity; an analysis of socioeconomic differences in road traffic injuries during childhood and youth; and an analysis of the City of Chicago's 2017 Vision Zero Action Plan. Additional analysis was conducted using Fatality Analysis Reporting System (FARS) and population data to further investigate whether BIPOC are disproportionately represented in fatal traffic crashes. It was found that BIPOC have faced and continue to face higher risk of traffic fatalities compared to white people. Suggested actions to address this inequality include: prioritizing safety countermeasures in underserved areas; treating traffic crash involvement as a healthy disparity issue; ensuring diverse representation in transportation agency leadership; developing new, research-based interventions; including input from BIPOC in safety education campaigns and outreach efforts; and engaging BIPOC in creating equitable traffic enforcement programs.]]></description>
      <pubDate>Mon, 27 Sep 2021 09:45:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1861214</guid>
    </item>
    <item>
      <title>Riding with an impaired driver and driving while impaired among adolescents: Longitudinal trajectories and their characteristics</title>
      <link>https://trid.trb.org/View/1857981</link>
      <description><![CDATA[The purpose of this study was to identify and characterize trajectory classes of adolescents who ride with an impaired driver (RWI) and drive while impaired (DWI).  The authors analyzed all 7 annual assessments (Waves W1–W7) of the NEXT Generation Health Study, a nationally representative longitudinal study starting with 10th grade (2009–2010 school year). Using all 7 waves, latent class analysis was used to identify trajectory classes with dichotomized RWI (last 12 months) and DWI (last 30 days; once or more = 1 vs. none = 0). Covariates were race/ethnicity, sex, parent education, urbanicity, and family affluence.  Four RWI trajectories and 4 DWI trajectories were identified: abstainer, escalator, decliner, and persister. For RWI and DWI trajectories respectively, 45.0% (n = 647) and 76.2% (n = 1,657) were abstainers, 15.6% (n = 226) and 14.2% (n = 337) were escalators, 25.0% (n = 352) and 5.4% (n = 99) were decliners, and 14.4% (n = 197) and 3.8% (n = 83) persisters. Race/ethnicity (χ2 = 23.93, P = .004) was significantly associated with the RWI trajectory classes. Race/ethnicity (χ2 = 20.55, P = .02), sex (χ2 = 13.89, P = .003), parent highest education (χ2 = 12.49, P = .05), urbanicity (χ2 = 9.66, P = .02), and family affluence (χ2 = 12.88, P = .05) were significantly associated with DWI trajectory classes.  Among adolescents transitioning into emerging adulthood, race/ethnicity is a common factor associated with RWI and DWI longitudinal trajectories. The results suggest that adolescent RWI and DWI are complex behaviors warranting further detailed investigation of the respective trajectory classes. The study findings can inform the tailoring of prevention and intervention efforts aimed at preventing illness/injury and preserving future opportunities for adolescents to thrive in emerging adulthood.]]></description>
      <pubDate>Fri, 23 Jul 2021 17:35:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1857981</guid>
    </item>
    <item>
      <title>The Philadelphia Story: Age, Race, Gender and Changing Travel Trends [supporting dataset]</title>
      <link>https://trid.trb.org/View/1862709</link>
      <description><![CDATA[Data sets associated with The Philadelphia story: Age, race, gender and changing travel trends. https://www.sciencedirect.com/science/article/pii/S0966692317307044 (2018). Abstract of the final report is stated below for reference: The authors examine changes in travel behavior in the Philadelphia region between 2000 and 2012. They use two household regional travel surveys over a decade apart, the 2000 and 2012 surveys, from the Delaware Valley Regional Planning Commission (DVRPC). Previous research, at the national scale, has highlighted changes among the Millennial cohort. They use these two regional datasets to examine the changing travel behavior among Millennials and to put these changes in context by comparing them with changes among other social groups of interest: women and minorities. The authors find that the travel behavior of young people did not change substantially between 2000 and 2012. Where there are changes, these pale in comparison to the changes among women and black residents. And finally, they find that the built environment factors appear to influence travel more in 2012 than in 2000. Taken together, the authors' findings fit an overarching narrative about urban gentrification and the suburbanization of poverty, rather than a story of Millennials’ changing travel behavior.]]></description>
      <pubDate>Thu, 08 Jul 2021 09:53:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1862709</guid>
    </item>
    <item>
      <title>Neighborhood Green Land Cover and Neighborhood-Based Walking in U.S. Older Adults</title>
      <link>https://trid.trb.org/View/1844551</link>
      <description><![CDATA[Greenspace exposure has been associated with physical activity, but few studies have investigated its association with physical activity in the residential neighborhood. This study investigates whether greater amounts of neighborhood open space and forest are associated with neighborhood-based walking in older adults. In 2020, cross-sectional analyses were conducted on those aged ≥65 years from the 2017 National Household Travel Survey. Minutes of neighborhood walking per day were derived from travel diaries. Green land cover measures from the 2011 National Land Cover Dataset were linked to respondent data by the U.S. census tract. Adjusted linear regression models, using weights accounting for survey sampling, tested the associations between the percentage of green land cover in the neighborhood (open space, forest) and minutes of neighborhood walking per day. Adjusted models were stratified to examine whether the associations varied by an individual- and neighborhood-level SES, sex, and race/ethnicity. Respondents (N=72,753) were aged 74 (SD=7) years on average. Greater percentage of open space was associated with more neighborhood walking in African Americans (estimate=0.069, 95% CI=0.005, 0.133). Greater percentage of forest was associated with more neighborhood walking in the overall sample (estimate=0.028, 95% CI=0.006, 0.050), women (estimate=0.025, 95% CI=0.005, 0.045), and Whites (estimate=0.034, 95% CI=0.004, 0.064). Type of neighborhood green land cover (open space versus forest) may be differentially associated with neighborhood walking depending on race/ethnicity. This study suggests a possible association between greater neighborhood open space and greater walking among African Americans that must be confirmed in future studies.]]></description>
      <pubDate>Wed, 26 May 2021 11:18:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1844551</guid>
    </item>
    <item>
      <title>Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships</title>
      <link>https://trid.trb.org/View/1763533</link>
      <description><![CDATA[Ridesharing is critical for promoting transportation sustainability. As a new form of ridesharing services, ridesplitting has rarely been studied. Based on the Chicago ridesourcing trip data, this study explores how ridesplitting adoption rate (i.e., the proportion of ridesourcing trips with ridesharing authorization) varies across space and what factors are associated with these variations. The authors find large variations in ridesplitting adoption rates across neighborhoods (Census Tracts) and across origin–destination (Census-Tract-to-Census-Tract) pairs. Particularly, the ridesplitting adoption rate is low for airport rides. The authors further apply a random forest model to explore which factors are key predictors of ridesplitting adoption rate across O-D pairs and to explore their nonlinear associations. The results suggest that the socioeconomic and demographic variables collectively contribute to 68.60% of the predictive power of the model, but travel-cost variables and built-environment-related factors are also important. The most important variables associated with ridesplitting adoption are ethnic composition, median household income, education level, trip distance, and neighborhood density. The authors further examine the nonlinear association between neighborhood ridesplitting adoption rate and several key variables such as the percentage of white population, median household income, and neighborhood Walk Score. The revealed nonlinear patterns can help transportation professionals identify neighborhoods with the greatest potential to promote ridesplitting.]]></description>
      <pubDate>Tue, 23 Mar 2021 11:13:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1763533</guid>
    </item>
    <item>
      <title>Discriminations in the market for “Lemons”: A multicriteria correspondence test in France</title>
      <link>https://trid.trb.org/View/1751408</link>
      <description><![CDATA[The existence of discrimination by the sellers of second-hand cars is a little studied subject, whereas the possibility of acquiring a car conditions multiple aspects of economic and social life. In this article, the authors apply the correspondence test method to the purchase of a used car in order to measure the extent of discrimination in this market according to the ethnic origin, gender, place of residence and age of the applicant. The authors constructed six profiles of fictitious individuals, perfectly similar except for their age, gender, origin indicated by the consonance of their surname and first name or place of residence. Between January and May 2015, the authors used these fictitious profiles to respond to 489 used car sales ads located in Ile-de- France. Statistical analysis of the responses to these tests reveals the existence of discrimination according to gender and place of residence. The analysis shows that information based discrimination prevails on the second-hand car market rather than taste based discriminations.]]></description>
      <pubDate>Mon, 07 Dec 2020 11:07:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1751408</guid>
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