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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>Behavioural and attitudinal responses to pandemic: A study focusing on young adults with transport disadvantages in low-density built environment</title>
      <link>https://trid.trb.org/View/2714544</link>
      <description><![CDATA[Studies of young adults’ travel behavioral adaptation during Covid-19 are scarce. This study aimed to enhance the understanding of how young adults with transport disadvantages in Canberra, Australia adapted their travel both during and after Covid-19 restrictions. Two quantitative datasets (Daily Public Transport Passenger Boardings by Passenger Type and by Service Type) were used to understand the overall trend in ridership from March 2020 to December 2021. In-depth qualitative interviews of 20 participants were conducted to further understand behavioral responses and challenges. Participants were recruited from both inner and distant suburbs of the city, specifically regions where car journeys represent a high proportion of journey types. Persons without access to cars were actively recruited for this study. The six-step thematic analysis approach was applied to analyze the interview data. Quantitative data shows a 25% reduction in usage compared to its pre-pandemic peak in 2019; with a 5% reduction persisting even in August 2023. Bus usage showed a similar pattern (16.1% reduction during lockdowns and 8.6% lower usage in February 2024 compared to pre-pandemic peaks in 2019). From interview data, the authors identified five themes: 1) challenges of working/studying online, 2) inconvenient public transport service, specifically its unreliability, 3) fear of contracting the virus, 4) the car as a provider of independent mobility and 5) mental health issues due to isolation. Four types of remoding were suggested to discuss policy interventions: Type 1 (private motorized), Type 2 (shifted from public transport to private motorized), Type 3(public transport), and Type 4 (active transport). This study fills the gap in literature by identifying travel behavior adaptation of young adults during and after pandemic disruptions. Enhancing public transport service and reliability is most effective for remoding Types 2 and 3, whilst investing in transport infrastructure is critical for Type 4.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714544</guid>
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    <item>
      <title>Where should the last passenger be dropped off? Anticipatory walking in ridepooling</title>
      <link>https://trid.trb.org/View/2686702</link>
      <description><![CDATA[Ridepooling can gain significant efficiency by going beyond the door-to-door paradigm, i.e., including some short passenger walks. Current methods typically rely on Static Walking, where the drop-off point is fixed at the moment of assignment. This frequently results in walking only at pick-up, since drop-offs may occur at the end of the current partial schedule of the vehicle, when the next stop of the vehicle is still unknown. In contrast, Dynamic Walking, where the drop-off point can be updated while the passenger travels, is generally considered unacceptable to users due to the uncertainty of both ETA and actual alighting location caused by it. Therefore, we propose an Anticipatory Walking method that captures most of the improvements yielded by Dynamic Walking, while remaining as practical as Static Walking. First, we generate an Artificial Stop sampled from two different sources of demand, which is placed in the schedule immediately after the drop-off to be decided. We then combine this a method that computes nodes’ hierarchies, to optimize the exact drop-off location. To prevent cases in which the prediction would result harmful once the actual next stop is revealed, we maintain an alternative schedule where the passenger is dropped at their exact destination, and select optimally between the two options.Large-scale simulations in three real-world instances (Manhattan, Utrecht, and Canberra) show consistent improvements over the Static Walking baseline and performance close to a Dynamic Walking benchmark. Our method achieves 29% of the improvements of Dynamic Walking in terms of rejection rate in Manhattan, 70% of it in Utrecht, and 73% of it in Canberra, without introducing unacceptable uncertainty and unreliability for users. These patterns align with network structure and level of sharing: gains are greater in cities with more hierarchical networks and less-shared demand-supply patterns, such as Utrecht and Canberra. In all, our Anticipatory Walking captures much of the benefits of Dynamic Walking, without imposing additional uncertainty on the users, and without the need for extensive forecasting or continual re-optimization.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686702</guid>
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    <item>
      <title>The divisibility index as a theoretical tool to support public transport design</title>
      <link>https://trid.trb.org/View/2696885</link>
      <description><![CDATA[Along a corridor, a service may operate as a single line or be divided into segments. We investigate theoretically under which conditions a divided line is better than a single line. We identify three conditions that favor the line split at a particular stop, namely (i) few induced transfers, (ii) a large difference in the maximum flows between the two segments, and (iii) the segment with the lower maximum flow being long. These elements are combined into a single metric, the Divisibility Index (DI). Using the DI, we propose two algorithms that quickly determine whether and where to divide a line, and that can be integrated into existing network design tools. The approach is numerically tested in a simulated linear city and with Canberra light-rail data, producing results that reproduce the optimal division cost with an average error of 0.1% and demonstrate the correlation between DI and optimal division, respectively.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696885</guid>
    </item>
    <item>
      <title>Detecting home location and time-varying home location from transit smartcard data: A longitudinal spatio-temporal framework</title>
      <link>https://trid.trb.org/View/2683323</link>
      <description><![CDATA[Identifying travel habits is a crucial input to predicting multi-modal travel demand and under-standing how individuals adapt to changing urban environments. Habitual trips are formed through repeated daily behaviours, and home location is central to these patterns, as most people consistently begin and end their day at home. Key locations, such as homes, provide valuable contexts for detecting and analysing habitual travel. When the home location changes, it often triggers a broader shift in travel behaviour, with potential impacts on spatio-temporal demand across transport networks. However, cross-sectional data sources such as surveys and interviews typically fail to capture longitudinal home location dynamics, while micro-level data sources such as transit smartcards contain no explicit personal information about travellers.This study uses 8.5 years of transit smartcard data from Canberra, Australia, to infer home locations and detect residential relocations at scale. We apply and compare three inference approaches—Mean Frequent Stop (MFS), k-means clustering, and DBSCAN—and validate the results against self-reported survey addresses. To address noisy fluctuations in travel behaviour, we introduce a novel persistency metric and a three-stage relocation detection algorithm that accounts for spatial distance, temporal consistency, and behavioural stability.Our results show that home locations can be reliably inferred for 90% of cardholders. Of these, 59% changed their most frequent stops at least once, and persistent relocations were detected for around 25%. These findings demonstrate the capacity of smartcard data to capture both stability and change in travel habits over long periods. By framing relocation as a personalised life event with system-wide consequences, this research highlights opportunities for developing more adaptive, user-centred transport systems and for improving travel demand forecasting under both steady-state and disrupted conditions.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683323</guid>
    </item>
    <item>
      <title>Performance analysis of multi-objective demand-responsive transport as a replacement for local bus lines</title>
      <link>https://trid.trb.org/View/2681163</link>
      <description><![CDATA[This study proposes a comprehensive and systematic comparison between Demand-Responsive Transport (DRT) and Public Transport (PT) in a real-world context, Canberra, Australia, by replacing a local bus line with DRT. The conducted comparison considers key indicators such as the number of vehicles, travel distance, operational costs, fuel consumption, and passenger travel time. To enhance the decision-making process, a multi-objective approach is used to simultaneously optimise operational costs, environmental impacts, and passenger inconvenience. Various weight combinations are used to explore the trade-offs amongst these objectives. The proposed model is simulated on a real-world case study in Canberra, using the public transport smart card data from 2019 (January) to 2021 (July). To analyse the impact of daily variations in demand, three operation shifts are simulated. The findings reveal that DRT is more flexible, efficient, and environmentally sustainable in low-demand scenarios, while PT excels in cost-efficiency as demand increases. It is observed that changing the weights associated with the objectives can influence operational costs by 25%, fuel consumption by 11%, and passenger travel time by 10%. These insights provide valuable information about DRT’s operational features when integrated into public transport systems.]]></description>
      <pubDate>Thu, 26 Mar 2026 09:06:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681163</guid>
    </item>
    <item>
      <title>Operationalizing social justice theory in transport resource allocation</title>
      <link>https://trid.trb.org/View/2652510</link>
      <description><![CDATA[Operationalizing social justice is challenged by the absence of a universal equity definition and inherent policy trade-offs. This study bridges theoretical social justice principles with operational decision-making by integrating four commonly discussed distributive justice theories—Utilitarianism, Rawls’ Egalitarianism, Prioritarianism, and Capabilitarian Sufficiency—into a bilevel bus frequency optimization model. It introduces linearized equity-oriented formulations to reflect the distinct distributive principles of these theories and proposes justice theory-driven equity metrics to evaluate distributive impacts using different ethical criteria. Using cumulative opportunity as a unit of distribution, the proposed frameworks and metrics are applied to Canberra’s southern suburbs. The results demonstrate that the choice of justice framework is not a neutral modelling decision but a normative one, with each framework producing distinct redistributive patterns and community-level trade-offs. This highlights the importance of aligning equity frameworks with policy objectives and governance contexts, as these choices ultimately shape public perception, political feasibility, and the long-term societal goals of transport investment.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652510</guid>
    </item>
    <item>
      <title>Consumer insights on EV policy towards accelerated adoption – Canberra case study</title>
      <link>https://trid.trb.org/View/2572002</link>
      <description><![CDATA[This study explores consumer perspectives on electric vehicle (EV) adoption in Canberra, Australia—a city in the early stages of EV promotion. Drawing on 524 valid survey responses and a mixed-methods approach, we examine key barriers, policy needs, and the influence of consumer knowledge on adoption intent. Results indicate that EV knowledge and prior driving experience significantly enhance purchase interest. While respondents exhibit relatively strong awareness of environmental benefits and local incentives, they are less informed about operational cost savings and lifecycle emissions. A supplementary dealer survey reveals a supply-side information lag, highlighting the need for improved communication channels between policymakers, dealers, and consumers. Consumers’ primary concerns relate to EV performance and cost-effectiveness, suggesting that recent technological progress has not yet translated into widespread consumer confidence. Qualitative responses further reveal diverse policy expectations spanning social, technical, and economic domains. The findings emphasize the necessity of a comprehensive local policy framework to facilitate EV uptake in emerging markets. Such a framework should address evolving challenges, including battery recycling, integration with home energy systems, and the development of supporting services. While local governments have limited influence over vehicle pricing and national technological standards, they can play a critical role in closing information gaps through targeted educational programs and alleviating infrastructure-related barriers. This research contributes to the understanding of localized EV policy design and provides insights for other cities seeking to initiate or strengthen their EV transition strategies.]]></description>
      <pubDate>Thu, 31 Jul 2025 16:43:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572002</guid>
    </item>
    <item>
      <title>Benefits from a new transit line: Exploring the impact of rare users and spatially heterogeneous variations in intensity of use</title>
      <link>https://trid.trb.org/View/2534374</link>
      <description><![CDATA[What are the city-wide benefits of introducing a new transit mode? Justification of a new transit mode is often based on claims of transformational change regarding mode share, ridership, or passenger-kilometers travelled (PKT). Detractors argue that these dramatic benefits are not experienced uniformly across populations or places. This paper quantifies the extent and intensity of the change in travel behaviour caused by the addition of a new mode and builds on measures of increased ridership and PKT to explore who is travelling on the new mode and in what ways.Recent advances in smartcard data analysis provide detailed insights into transit ridership but are often temporally limited, restricting the ability to analyse long-term behavioural trends. While research on transit ridership patterns exists, few studies compare users of the old and new modes in terms of spatial distribution and intensity of use. This study relies on a long baseline of data to estimate the home locations of travellers and their intensity of use.The authors' findings challenge assumptions about infrequent users and reveal that rare users significantly influence ridership patterns. Increased reach of ridership is substantiated based on the appearance of new cards 48% higher than usual after the light rail introduction. However, only about 4% of new card IDs make trips at a rate equivalent to a daily commute. The results show strong spatial patterns, with 50% of frequent light rail riders having inferred home locations at the new stations and 63% of daily riders not using the bus to access the light rail.This approach suggests that (1) rare users, though they ride less often, have a notable impact on ridership trends; (2) transit-access distances are shorter than conventional assumptions of approximately half-a-mile; and (3) ridership benefits show strong spatial patterns.]]></description>
      <pubDate>Mon, 12 May 2025 09:45:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534374</guid>
    </item>
    <item>
      <title>Understanding nonuse of mandatory e-scooter helmets</title>
      <link>https://trid.trb.org/View/2387195</link>
      <description><![CDATA[Head injuries resulting from e-scooter use have led to calls for helmet use to be promoted or mandatory. Helmet use is mandatory for e-scooters in Australia but observational studies have reported significant levels of nonuse, particularly by riders of shared e-scooters. The aim of this study is to understand whether nonuse in the mandatory context is a consistent behavior for an individual or is situationally-influenced, and what are the factors associated with nonuse. An online survey was completed between 2022 and 2023 by 360 adult e-scooter riders in Canberra, Australia. Riders were asked whether they had worn a helmet on their last ride and how often they had not worn a helmet when riding in the last 30 days. The survey also asked about rider characteristics (demographics, frequency of e-scooter and bicycle use, perceived risk of e-scooter use, e-scooter ownership, and risky behaviors while riding), trip duration and perceptions of the helmet requirement (knowledge of and support for the law). Respondents were mostly male, young, highly educated, and full-time workers. Of the 29.1% of riders who reported riding without a helmet in the last 30 days, 24.4% had worn a helmet at least once during that period and 4.8% had consistently not worn a helmet. Younger age, shared e-scooter use and more frequent riding frequency (shared e-scooters only) were associated with helmet nonuse in the bivariate analyses but not in the logistic regression. Logistic regression showed that the independent predictors of helmet nonuse were the number of risky riding behaviors, lack of knowledge, and lack of support for the law. Most nonuse of helmets in a mandatory context seems to be situational, rather than consistent. Many of the factors associated with nonuse of helmets for e-scooters are similar to those reported for bicycles. Nonuse of helmets appears to be one of a number of risky behaviors performed by riders, rather than being primarily an outcome that is specific to factors associated with helmets (e.g., concerns about hygiene, discomfort or availability).]]></description>
      <pubDate>Wed, 26 Jun 2024 14:16:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2387195</guid>
    </item>
    <item>
      <title>Analysing preferences for integrated micromobility and public transport systems: A hierarchical latent class approach considering taste heterogeneity and attribute non-attendance</title>
      <link>https://trid.trb.org/View/2338921</link>
      <description><![CDATA[Shared Micromobility systems in urban regions hold the potential to reduce private vehicle usage and boost public transport patronage. To effectively achieve these goals, a comprehensive approach to integrating micromobility and public transport is essential. This study introduces a novel modelling framework to elicit travellers’ preferences towards the features of integrated shared micromoiblity and public transport systems. The data is obtained from a stated preference survey involving 250 residents in Canberra, Australia. Respondents’ mode choice behaviour and their propensity to switch from their current mode of transport to an integrated system are collected and modelled using a hierarchical latent class approach to account for taste heterogeneity and attribute non-attendance. The results show higher propensity of mode shift is associated with young age, high educational attainment, high scooter ownership and low car ownership. On average, respondents in this study express a willingness to pay of $0.55 for an integrated payment option. These results provide valuable insight into the integrated urban transport systems.]]></description>
      <pubDate>Mon, 26 Feb 2024 15:42:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2338921</guid>
    </item>
    <item>
      <title>Integrated Demand Responsive transport in Low-Demand Areas: A case study of Canberra, Australia</title>
      <link>https://trid.trb.org/View/2320008</link>
      <description><![CDATA[This paper evaluates Integrated-Demand Responsive Transport (I-DRT) as a solution to the challenges faced by traditional public transport (PT) systems in low-demand urban areas. The study investigates the implications of replacing local PT with I-DRT in low-demand urban areas. A multi-objective model, incorporating operational cost, environmental impact, passengers' travel time, and inequity is used to simulate the I-DRT performance. The analysis compares the performance of I-DRT and existing local bus lines in Belconnen, Canberra, Australia, based on number of utilised vehicles, operational cost, fuel consumption, average travel time, individual passenger travel time, delay, and inequity in delay distribution.]]></description>
      <pubDate>Thu, 18 Jan 2024 11:37:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2320008</guid>
    </item>
    <item>
      <title>Inability of the Mini-Mental State Exam (MMSE) and high-contrast visual acuity to identify unsafe drivers</title>
      <link>https://trid.trb.org/View/1922797</link>
      <description><![CDATA[To examine the validity of high-contrast visual acuity and the Mini-Mental State Exam (MMSE) as tools for identifying at-risk older drivers. Prospective multi-site observational cohort study. Community sample drawn from cities of Brisbane and Canberra, Australia. 560 licensed drivers aged 65–96 years recruited between 2013 and 2016, from the community, an optometry clinic and driver referral service. 50-minute standardized on-road driving test conducted on a standard urban route in a dual-brake vehicle with a driver trained Occupational Therapist assessor masked to participants’ cognitive, visual and medical status. Of 560 participants who completed the on-road test, 68 (12%) were classified as unsafe. Binary logistic regression models adjusted for age, gender, site, comorbidity and driving exposure indicated that a 1-point decrease in MMSE score was associated with a 1.35 (95%CI: 1.12–1.63) increase in odds of unsafe driving, and for each line reduction in binocular visual acuity (increase of 0.1 logMAR) was associated with 1.39 (95%CI: 1.07–1.81) increased odds of unsafe driving. However, Receiver Operating Characteristic (ROC) analysis showed low discriminative power for both measures (MMSE: AUC = 0.65 (95%CI: 0.58–0.73), visual acuity: AUC = 0.65 (95%CI: 0.59–0.72)) and typical cut-offs were associated with very low sensitivity for identifying unsafe drivers (MMSE <24/30: 2%; visual acuity worse than 6/12 Snellen (logMAR >0.30): 3%). The MMSE and high-contrast visual acuity tests do not reliably identify at-risk older drivers. They have extremely low sensitivity for detecting unsafe drivers, even when used together, and poor prognostic properties relative to validated screening instruments that measure cognitive, vision and sensorimotor functions relevant to driving. Clinicians should select alternate validated driver screening tools where possible.]]></description>
      <pubDate>Tue, 29 Mar 2022 09:58:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1922797</guid>
    </item>
    <item>
      <title>Validation of brief screening tools to identify impaired driving among older adults in Australia</title>
      <link>https://trid.trb.org/View/1888989</link>
      <description><![CDATA[There is an urgent need to develop evidence-based assessments to identify older individuals who may be unsafe drivers. To validate 8 off-road brief screening tests to predict on-road driving ability and to identify which combination of these provides the best prediction of older adults who will not pass an on-road driving test. This prognostic study was conducted between October 31, 2013, and May 10, 2017, using the criterion standard for screening tests, an on-road driving test, with analysis conducted from August 1, 2019, to April 2, 2020. A volunteer sample of older drivers was recruited from community advertisements, rehabilitation and driver assessment clinics, and an optometry clinic in Canberra and Brisbane, Australia. Off-road driver screening measures, including the Useful Field of View, DriveSafe/DriveAware, Multi-D battery, Trails B, Maze test, Hazard Perception Test, DriveSafe Intersection test, and 14-item Road Law test. Classification as unsafe on a standardized 50-minute on-road driving assessment administered by a driving instructor and an occupational therapist masked to the participant's clinical diagnosis and off-road test performance.   A total of 560 drivers aged 63 to 94 years (mean [SD] age, 74.7 [6.2] years]; 350 [62.5%] men) were assessed. Logistic regression and receiver operating characteristic analyses indicated the area under the curve was largest for a multivariate model comprising the Multi-D, Useful Field of View, and Hazard Perception Test, with an area under the curve of 0.89 (95% CI, 0.85-0.94), sensitivity of 80.4%, and specificity of 84.1% for predicting unsafe drivers. The Multi-D battery was the most accurate individual assessment and had an area under the curve of 0.85 (95% CI, 0.79-0.90), sensitivity of 77.1%, and specificity of 82.1%. The multivariate model had sensitivity of 83.3% and specificity of 91.8% in the cognitively impaired group and sensitivity of 87.5% and specificity of 70.8% in the visually impaired group. These findings suggest that off-road screening tests can reliably identify older drivers with a strong probability of failing an on-road driving test. Implementation of these measures could enable better targeting of resources for managing older driver licensing and support injury prevention strategies in this group.]]></description>
      <pubDate>Mon, 22 Nov 2021 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1888989</guid>
    </item>
    <item>
      <title>Preparing Canberra light rail</title>
      <link>https://trid.trb.org/View/1644501</link>
      <description><![CDATA[Shany Shaked and Robert Wagner of DB Engineering & Consulting describe the process of testing and commissioning for Australia's newest light rail system.]]></description>
      <pubDate>Wed, 07 Aug 2019 11:23:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1644501</guid>
    </item>
    <item>
      <title>Benders Decomposition for the Design of a Hub and Shuttle Public Transit System</title>
      <link>https://trid.trb.org/View/1583755</link>
      <description><![CDATA[The BusPlus project aims at improving the off-peak hours public transit service in Canberra, Australia. To address the difficulty of covering a large geographic area, proposes a hub and shuttle model consisting of a combination of a few high-frequency bus routes between key hubs and a large number of shuttles that bring passengers from their origin to the closest hub and take them from their last bus stop to their destination. This paper focuses on the design of the bus network and proposes an efficient solving method to this multimodal network design problem based on the Benders decomposition method. Starting from a mixed-integer programming (MIP) formulation of the problem, the paper presents a Benders decomposition approach using dedicated solution techniques for solving independent subproblems, Pareto-optimal cuts, cut bundling, and core point update. Computational results on real-world data from Canberra’s public transit system justify the design choices and show that the approach outperforms the MIP formulation by two orders of magnitude. Moreover, the results show that the hub and shuttle model may decrease transit time by a factor of two, while staying within the costs of the existing transit system.]]></description>
      <pubDate>Thu, 21 Feb 2019 09:49:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1583755</guid>
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