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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <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>High-resolution traffic flow data from the urban traffic control system in Glasgow</title>
      <link>https://trid.trb.org/View/2521941</link>
      <description><![CDATA[Traffic flow data has been used in various disciplines, including geography, transportation, urban planning, and public health. However, existing datasets often have limitations such as low spatiotemporal resolution and inconsistent quality due to data collection methods and the need for an adequate data cleaning process. This paper introduces a long-term traffic flow dataset at an intra-city scale with high spatio-temporal granularity. The dataset covers the Glasgow City Council area for four consecutive years spanning the COVID-19 pandemic, from October 2019 to September 2023, providing comprehensive temporal and spatial coverage. Such detailed information facilitates diverse applications, including traffic dynamic analysis, traffic management, infrastructure planning, and urban environment improvement. Also, it provides a valuable dataset to understand traffic flow change during a once-in-a-lifetime pandemic event.]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521941</guid>
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    <item>
      <title>Outdoor lighting and active travel: A high-resolution analysis using satellite imagery and Strava data in Glasgow</title>
      <link>https://trid.trb.org/View/2636250</link>
      <description><![CDATA[The benefits of active travel are well-established. While previous research has explored how built environment factors (such as population density, accessibility, land use, and infrastructure) influence active travel, micro-scale features like outdoor lighting have received less attention. This study examines associations between outdoor lighting levels and active travel in Glasgow, accounting for broader contextual factors and distinguishing between daylight and dark conditions. The authors used Strava data, satellite-derived outdoor lighting imagery, and other spatial datasets aggregated to small-area zones in Glasgow. Bayesian spatial models (Besag–York–Mollié) were fitted to estimate associations between contextual variables and distances travelled on foot, by bike, and by both modes combined, separately for daylight and dark hours. Outdoor lighting levels derived from night-time satellite imagery were positively associated with walking, cycling, and overall active-travel distances during both light conditions (daylight and dark). These associations were stronger during dark hours, particularly for cycling. Several contextual relationships also varied by light condition: industrial density was positively associated with cycling only during daylight, while quietness and gradient showed stronger associations during daylight. Population and income deprivation were negatively associated across all modes under both light conditions. The findings underscore the potential relevance of lighting in shaping active travel patterns after dark, particularly for cycling. They also highlight the need for future research that considers light conditions and time of day in environmental studies of mobility, as well as across broader contexts, specific locations, and diverse population groups – to better inform equitable and effective active travel policy.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636250</guid>
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    <item>
      <title>Twelve years of evidence: Modelling the injury severity of single-vehicle collisions pre- and post-20mph (32 km/h) implementation in Edinburgh and Glasgow</title>
      <link>https://trid.trb.org/View/2587398</link>
      <description><![CDATA[This article presents a comprehensive evaluation framework for assessing the collision severity implications of two competing 20mph schemes in the cities of Edinburgh and Glasgow, UK. To achieve this, road traffic collision severity data are statistically analyzed to provide a comprehensive overview of road safety pre- and post-20mph implementation in each case city. Advanced discrete outcome models that account for unobserved heterogeneity, namely, Random Parameters Ordered Probit Models with allowances for Heterogeneity in the Means (RPOPHM) of Random Parameters were estimated to analyze the collision-, casualty- and vehicle-specific determinants of collision severity across different speed limit scenarios: Edinburgh pre- (1) and post-20mph (2) and Glasgow pre- (3) and post-20mph (4). The estimation of four separate models facilitated intracity (in other words, pre- versus post-20mph in each case city) and intercity comparisons of collision severity determinants. In terms of intracity findings, the results suggest that the citywide enforcement of 20mph speed limits, as in Edinburgh, has reduced the risk of vulnerable road users, and especially pedestrians, being involved in serious or fatal collisions, relative to other road users. Conversely, the Glasgow models suggest that the Glasgow 20mph scheme, which was less radical and more targeted, has not significantly altered the disproportionately high risk of pedestrians being involved in severe collisions. Policy recommendations are provided, specifically in terms of how varying 20mph approaches may affect existing road safety inequalities.]]></description>
      <pubDate>Tue, 19 Aug 2025 15:29:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587398</guid>
    </item>
    <item>
      <title>The potential of low traffic measures for healthy active ageing</title>
      <link>https://trid.trb.org/View/2458854</link>
      <description><![CDATA[Being and remaining active is reported to be positively associated with healthy ageing, though many older adults are not as active as they would like to be. Low Traffic Neighbourhoods have been identified as a possible solution to traffic-related activity barriers. This study aimed to explore this possibility, and the potential for these methods to support active ageing. 20 older adults aged between 60 and 91 (80 per cent women) took part in focus groups across low and high traffic areas. Discussions centred on experiences of staying active and perceptions of low traffic measures for facilitating activity. Six participants then took part in walking interviews across two areas, which explored some of the barriers and enablers to staying active. Staying active was found to be particularly important, though many participants suggested that a number of environmental barriers prevent them from being as active as they would like to be, in line with previous research. Many struggled to recognise the potential of low traffic measures, suggesting that there is a lack of consideration for older people within the planning process, particularly those with mobility issues. Future studies should focus on those with mobility issues in order to explore range of needs.]]></description>
      <pubDate>Mon, 16 Dec 2024 11:59:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2458854</guid>
    </item>
    <item>
      <title>Understanding urban traffic flows in response to COVID-19 pandemic with emerging urban big data in Glasgow</title>
      <link>https://trid.trb.org/View/2423455</link>
      <description><![CDATA[Urban traffic analysis has played an important role in urban development, providing insights for urban planning, traffic management, and resource allocation. Meanwhile, the global pandemic of COVID-19 has significantly changed people's travel behaviour in urban areas. This research uses the spatial Durbin model to understand the relationship between traffic flows, urban infrastructure, and socio-demographic indicators before, during, and after pandemic periods. The authors include factors such as road characteristics, socio-demographics, surrounding built environments (land use and nearby points of interest), and the emerging urban big data source of Google Street View images to understand their influences on time series traffic flows. Taking the city of Glasgow as the case study, they have found that areas with more young and white dwellers are associated with more traffic flows, while natural green spaces are associated with fewer traffic flows. Major roads between cities and towns also show heavier traffic flows. Besides, the application of Google Street View images in this research has revealed the heterogeneous effects of green space on urban traffic flows, as the magnitudes of their effects vary by distance. The authors also detect that the spatial dependence between adjacent neighbourhoods among the traffic flows and associated urban parameters is variable during the four COVID-19 periods. With the influence of COVID-19, there has been a significant decrease in long-distance travel. The noticeable change in travel behaviour presents a valuable opportunity to encourage active travel in the near future.]]></description>
      <pubDate>Wed, 16 Oct 2024 11:10:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2423455</guid>
    </item>
    <item>
      <title>Re-examining the role of street network configuration on bicycle commuting using crowdsourced data</title>
      <link>https://trid.trb.org/View/2423412</link>
      <description><![CDATA[Understanding the role of street network configuration on commuter cycling can aid city planners in assessing and evaluating interventions to promote regular cycling into people's routines. Studies examining this relationship generally build models on observed cycling counts. While this provides valuable information, it could still result in an incomplete picture because some routes with advantages (e.g., good accessibility, amenities etc) may not have been utilised as expected because of other confounding factors (e.g., safety issues, motorised traffic) or vice-versa, leading to incorrect conclusions especially in the absence of data on these confounding factors. Thus, in this study, the authors argue that observing higher cycling flows on a route should not be the sole criteria to examine the role of street network configuration on the cycling patterns, especially when certain confounding factors have not been controlled for. They utilise data from the activity tracking app Strava for the city of Glasgow and compare the observed cycling intensities with the modelled cycling intensities where all the cyclists take the shortest routes. They estimated three linear regression models for: Observed Strava Cycling Intensities, Modelled Strava Cycling Intensities, and the difference between these two measures. Street network configuration were incorporated using Space Syntax measures: Normalised Angular Choice and Normalised Angular Integration. The roles of these variables, along with route characteristics and natural environment factors, on commuter cyclists' trips are explored. Visual exploration and linear regression models indicate that cyclists deviated away from the well-integrated, straighter routes to aesthetically attractive routes with cycling infrastructure, and towards links within mixed land use and to the least deprived areas. These results are of interest to policy makers and assist in infrastructure planning.]]></description>
      <pubDate>Wed, 11 Sep 2024 10:53:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2423412</guid>
    </item>
    <item>
      <title>Situated, Yet Silent: Data Relations in Smart Street Furniture</title>
      <link>https://trid.trb.org/View/2005255</link>
      <description><![CDATA[This article provides new evidence of the ways that smart cities materialize within specific sites and contexts through smart street furniture (SSF). Drawing on empirical data generated through mixed-method field research, the article examines the situated data relations that emerge in the context of the adoption of InLinkUK smart kiosks in Glasgow and Strawberry Energy smart benches in London. The concept of “silences” is proposed to analyze insufficiently articulated data relations resulting from gaps or absences in the use, design, and governance of this new type of urban furniture. The argument made is that data silences lead to failures to account for decisions and the deferral of responsibilities regarding the data aspects of these objects. It is suggested that an approach that focuses on “listening” to and “speaking” about data relations can enable dialogical forms of accountability, and realize the potential of SSF for citizens in local contexts.]]></description>
      <pubDate>Fri, 30 Sep 2022 14:27:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2005255</guid>
    </item>
    <item>
      <title>Predicting cycling volumes using crowdsourced activity data</title>
      <link>https://trid.trb.org/View/1907957</link>
      <description><![CDATA[Planning for cycling is often made difficult by the lack of detailed information about when and where cycling takes place. Many have seen the arrival of new forms of data such as crowdsourced data as a potential saviour. One of the key challenges posed by these data forms is understanding how representative they are of the population. To address this challenge, a limited number of studies have compared crowdsourced cycling data to ground truth counts. In general, they have found a high correlation over the long run but with limited geographic coverage, and with counters placed on routes already known to be popular with cyclists. Little is known about the relationship between cyclists present in crowdsourced data and cyclists in manual counts over shorter periods of time and on non-arterial routes. The authors fill this gap by comparing multi-year crowdsourced data to manual cyclist counts from a cordon count in Scotland’s largest city, Glasgow. Using regression techniques, the authors estimate models that can be used to adjust the crowdsourced data to predict total cycling volumes. The authors find that the order of magnitude can be predicted but that the predictions lack the precision that may be required for some applications.]]></description>
      <pubDate>Thu, 24 Mar 2022 17:26:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1907957</guid>
    </item>
    <item>
      <title>Public participation geographic information system (PPGIS) as a method for active travel data acquisition</title>
      <link>https://trid.trb.org/View/1876347</link>
      <description><![CDATA[Promoting active travel (AT) requires informed transportation decisions relating to issues such as planning, safety, and infrastructure, tailored to user needs. To facilitate such a process, a new stream of research has emerged that utilizes social fitness networks (SFNs), such as Strava, to obtain user AT data. However, SFN datasets exhibit bias towards certain types of users (e.g., fitness-oriented cyclists, males, young users). In order to overcome this limitation, the authors tested a Public Participation Geographic Information System (PPGIS) as a primary data collection method and post-hoc tool to evaluate and complement SFNs by paying special attention to cycling data. In particular, PPGIS was administered in the City of Glasgow via 816 participants partitioned into Strava and non-Strava users. Spatial and contextual information was determined from the collected data. Statistical analyses demonstrate that PPGIS non-Strava users were distinct from PPGIS Strava users, as the latter tend to be associated with certain characteristics (i.e., younger, more fitness-oriented, proficient in smart devices), indicating the need for a complementary dataset. Spatially, a weak correlation (r = 0.22) between the route choices of PPGIS cyclist users and those of Strava indicates inconsistencies between the two; however, the origins and destinations were moderately positively correlated (r = 0.53 and r = 0.48, respectively). Further, statistically significant differences were observed between PPGIS Strava users and PPGIS non-Strava users, inferring the need for a supplementary tool. The authors' findings imply that PPGIS is a promising tool to fill these gaps.]]></description>
      <pubDate>Fri, 03 Dec 2021 11:22:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1876347</guid>
    </item>
    <item>
      <title>Examining the effects of a temporary subway closure on cycling in Glasgow using bike-sharing data</title>
      <link>https://trid.trb.org/View/1861683</link>
      <description><![CDATA[This study takes the 39-day Glasgow Subway closure in July 2016 as a natural experiment to evaluate the effect of subway closure on bike-sharing trips. The authors find that bike-sharing trips increased by 20.7% for incoming trips and 20.1% for outgoing trips on average for each bike station in the proximity of subway station during the subway suspension. Some of this change persisted, with 12.4% of the increased bike-sharing trips remaining after the resumption of the subway service. The findings suggest that first, subway and bike-sharing trips are substitutes; second, this temporary service disruption was not enough to break commuters’ long-term habits, and third, the diversion factors implied by the results are much lower than the recommended values for UK cities.]]></description>
      <pubDate>Fri, 23 Jul 2021 15:26:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1861683</guid>
    </item>
    <item>
      <title>Modelling cyclists’ route choice using Strava and OSMnx: A case study of the City of Glasgow</title>
      <link>https://trid.trb.org/View/1765165</link>
      <description><![CDATA[Previous research has demonstrated the influence of street layout on travel behaviour; however, little research has been undertaken to explore these connections using detailed and robust street network analysis or cycling data. In this study, the authors harness state-of-the-art datasets to model cyclists’ route choice based on a case study of the City of Glasgow, Scotland. First, the social fitness network Strava was used to obtain datasets containing the number of cycling trips on each street intersection for the years 2017 and 2018. Second, the authors employed a Python toolkit to acquire and analyse the street networks. OSMnx was subsequently employed to quantify several commonly used centrality indices (degree, eigenvector, betweenness and closeness) to measure street layout. Due to the presence of spatial dependence, a spatial error model was used to model route choices. Model results demonstrate that: (1) cyclists’ movement models were consistent for the years 2017 and 2018; (2) the presence of a spillover effect suggests that cyclists tend to cycle in proximity to each other; and (3) cyclists avoid streets with high degree centrality values and prefer streets with high eigenvector centrality, betweenness centrality and closeness centrality. These findings reveal cyclists’ desired street layouts and can be taken into consideration for future interventions.]]></description>
      <pubDate>Tue, 23 Mar 2021 11:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1765165</guid>
    </item>
    <item>
      <title>Dynamic accessibility: Incorporating day-to-day travel time reliability into accessibility measurement</title>
      <link>https://trid.trb.org/View/1749155</link>
      <description><![CDATA[Travel times and hence accessibility in urban areas are susceptible to traffic disruption caused by events such as congestion, roadworks and traffic accidents. Being highly valued by travellers, travel time reliability affects their participation in activities and thus, plays a decisive role in accessibility. The aim of the study was to develop an approach to integrate travel time reliability into the measurement of accessibility. To achieve this, the authors extended a commonly-used accessibility indicator to include day-to-day variability in travel times. In a case study of the accessibility to the newly-built Queen Elizabeth University Hospital (QEUH) in Glasgow, they used real-time travel times with high temporal resolution collected over a long period of time to demonstrate the applicability and the utility value of this approach compared to the standard accessibility measurement. The authors' results revealed that travel time reliability varied both temporally and spatially, and zones experienced relatively high levels of accessibility loss due to higher travel time variability. The proposed approach provides a more realistic representation of actual network performance, allows for assessing the effect of travel time reliability on accessibility throughout the day and will help transport planners to trace equity impacts on accessibility due to travel time unreliability.]]></description>
      <pubDate>Mon, 21 Dec 2020 13:48:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1749155</guid>
    </item>
    <item>
      <title>The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data</title>
      <link>https://trid.trb.org/View/1747046</link>
      <description><![CDATA[The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013–2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data.]]></description>
      <pubDate>Tue, 24 Nov 2020 15:57:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1747046</guid>
    </item>
    <item>
      <title>Keeping the car clean: on the electrification of private transport</title>
      <link>https://trid.trb.org/View/1736481</link>
      <description><![CDATA[The electrification of private road vehicles - and the provision of a low carbon generation mix that supplies the energy for their motion - is likely to be a key contributor to meeting net zero targets and limiting the disastrous effects of anthropogenic climate change. The work presented in this thesis surrounds two aspects of this transition. Firstly, as EVs are fundamentally different to internal combustion vehicles (ICVs), in that their energy storage capacity is far smaller and the rate at which it can be replenished is much slower, there has been consumer resistance to their adoption due to the perception that their charging carries inconvenience compared to ICV fuelling. Secondly, as the energy demand of private road vehicles is shifted from the petrochemical supply chain to the electricity grid, there are potential i) issues surrounding the resilience of the grid and ii) opportunities resulting from the flexibility and 'schedulability' of EV charging in enabling the further decarbonisation of the power sector. With regards to the first aspect, this thesis presents an investigation of the inconvenience of EV charging versus ICV fuelling and how this is likely to change depending on vehicle parameters - battery capacity and charger power - and the set of locations at which it can be charged. Secondly this thesis presents analysis into the likely impact of EV charging on residential distribution networks, taking into account the effect of i) the way in which drivers schedule charge events, ii) the social demographics (and hence travel habits) of the individuals served by a network and iii) the effect of the rapidly evolving EV sector and the resulting changes in technical parameters and level of charging infrastructure.]]></description>
      <pubDate>Tue, 01 Sep 2020 14:50:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1736481</guid>
    </item>
    <item>
      <title>Airport competition within the Scottish lowlands region</title>
      <link>https://trid.trb.org/View/1696927</link>
      <description><![CDATA[The aim of this paper is to undertake an assessment of airport competition within the Scottish Lowlands region, which has experienced significant variations in economic development, and to examine whether competitive forces have been strengthening or weakening in recent years. This region covers the airports of Edinburgh, Glasgow and Prestwick in the last twelve years they have all experienced changes in ownership. BAA which had, for many years, operated both Edinburgh and Glasgow airports, sold the former to GIP in 2012 whilst in 2013 the Scottish Government purchased the privately-owned Prestwick. During this period there were also significant changes in airline network strategies. In order to assess the competitive pressures facing these airports, three key areas are considered, namely: aeronautical charging policy, the service quality provided and traffic development. The analysis shows that since ownership separation, competition has intensified between Edinburgh and Glasgow, whilst Prestwick airport, which benefitted from Ryanair expansion in the 1990s, is now a significantly diminished competitive proposition in the Scottish Lowland market. This has implications not only for airport policy and economic regulation but also for broader economic well-being in this region.]]></description>
      <pubDate>Wed, 27 May 2020 17:24:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1696927</guid>
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