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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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      <title>Optimising public transport investment from a cost-neutral carpooling policy: A case study in Wellington, New Zealand</title>
      <link>https://trid.trb.org/View/2681478</link>
      <description><![CDATA[Seamless first- and last-mile connectivity is critical for encouraging public transport usage, yet such solutions often require substantial government investments. However, there is limited research on addressing the connectivity challenge through a cost-neutral approach. We propose free carpooling and paid single-occupancy parking at train stations on working days, in the context of daily commuting. Additional revenue could be anticipated from two sources: paid parking spaces and increased train ridership, as new commuters may be attracted to the network by the free carpooling incentive. This study formulates an integer programming model to reinvest this revenue towards increasing rail service frequency during peak commuting hours. The model is constrained by operating costs, rail capacity, and timetable symmetry. Using rail-world data from the Wellington Metro Railway Network, we compute line-specific capacity limits and identify optimal revenue allocations. The results show that peak-hour service intervals can be significantly reduced within the available reinvestment budget. This model demonstrates a scalable, data-driven strategy for improving public transport operational efficiency through behavioural incentives and revenue reinvestment.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:06:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681478</guid>
    </item>
    <item>
      <title>Integrating Carpooling in Mobility-as-a-Service Platforms: Learnings from Co-design Activities</title>
      <link>https://trid.trb.org/View/2580136</link>
      <description><![CDATA[App-based Mobility as a Service (MaaS) platforms combining public transport, car- and micro-mobility-shared services with dynamic carpooling are emerging as viable alternatives to solo car use for sub-urban contexts. Insights from real-life implementation are however still limited. Which practical challenges affect MaaS platforms leveraging carpooling? We tackle this question from the perspective of potential users of the Swiss-based MixMyRide platform, engaging them in co-design workshops. We find four elements of practical interest, resonating with limitations already identified for carpooling. First, carpooling increases the number of inter-changes potentially affected by delays. This requires real-time traffic information data, re-scheduling tools, and features for quick interaction between users. Second, as social control is low, features to create trust between strangers are needed, which calls for trade-offs between strict identity checks and quick registration. Third, carpooling pick-up/drop-off may endanger safety if bus stops are used. This requires in-advance agreements, negatively affecting the MaaS’ dynamism. Fourth, car-pooling offer is not granted. To accept possible discomfort, decreased flexibility, and effort to enter ride offers, drivers need incentives, such as sharing of travel expenses, virtual or tangible rewards by public institutions, and feedback on saved emissions.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580136</guid>
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    <item>
      <title>On-Demand Transport Services as a Supplement to Public Transport in Suburban Areas</title>
      <link>https://trid.trb.org/View/2580133</link>
      <description><![CDATA[App-based ridepooling offers the chance to provide a high-quality public transportation service in areas that have been inadequately served so far. One of the main strengths of ridepooling services is their ability to offer direct access to the public transport system and thus provide an attractive service on the first and last mile. While traditional on-demand services have so far been quite inflexible, due to having a fixed timetable, needing to be ordered in advance of the trip via a call centre, app-based ridepooling promises a true on-demand service for the user. This study is based on a household survey carried out in June/July 2022 in suburban areas of Hamburg, Germany's second-largest city. We investigated one area with a modern app-based on-demand service, as well as one area with traditional dial-a-ride transport with telephone booking options. Similarities as well as differences in the perception of these different services were investigated. The results show that the modern on-demand services are significantly better known and more frequently used than the traditional dial-a-ride transport services. At the same time, the same features of demand responsive transport (DRT) service are shown to be important to people, regardless of the type of DRT service offered. In particular, flexible booking options should be highlighted, which modern demand responsive transport is better able to fulfil.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580133</guid>
    </item>
    <item>
      <title>Exploratory study of long-distance carpooling supply and public financial incentives: The case of France</title>
      <link>https://trid.trb.org/View/2677605</link>
      <description><![CDATA[To meet its CO₂ reduction targets in the mobility sector, the French government aims to support carpooling as a means to achieve more efficient car use. Recognizing the gap between these targets and current levels, the government introduced a financial incentive of 100€ per new driver in 2023. This paper seeks to provide an exploratory study of long-distance carpooling supply in France, with particular attention to this public policy, while accounting for a broad set of intermodal and intramodal factors. The study relies on an empirical analysis of original data covering the period 2020–2024. The primary findings from the Seasonal-Trend decomposition analysis confirm the impact of COVID-19 restrictions in 2020 and the subsequent rebound in 2022, with a similar trend continuing into 2023. To explore this further, a Multiple Linear Regression model incorporating a large panel of variables was developed. Results indicate that new driver uptake correlates more with other influencing factors than with the subsidy itself. They highlight a strong complementarity with train services, as new drivers are associated with train transport shortages and seasonality, particularly during peak demand periods.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2677605</guid>
    </item>
    <item>
      <title>Promoting carpooling in ride-hailing services with motivational messages in-app and defaults</title>
      <link>https://trid.trb.org/View/2679096</link>
      <description><![CDATA[Carpooling within taxi services presents a sustainable alternative to single-occupancy vehicles, given the rapidly growing urban population and reliance on private cars. Aiming to examine passenger choices for this type of carpooling and their underlying motivations, we experimentally tested mock-ups mimicking DiDi, China’s biggest ride-hailing provider. We conducted a 4 × 2 between-subjects experimental design (N = 1600) comparing the effects of three motivational messages – environmental benefits, carpool safety, and monetary savings (vs. no message control group). Furthermore, we tested the effect of placing the carpool option as the (default) first option shown to participants (vs. the second option), when choosing their preferred ride, both for an urgent trip, and a non-urgent trip. Results showed that carpool was preferred for non-urgent trips, and placing carpool specifically as the default choice in non-urgent trips further increased average demand by 8% (4% without any additional message). For urgent trips, single-occupancy rides were preferred. However, even in the case of urgency, adding a motivational message or placing carpooling as the default first option significantly increased carpool choice.]]></description>
      <pubDate>Thu, 18 Jun 2026 09:05:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679096</guid>
    </item>
    <item>
      <title>Enhanced H-Gasa Algorithm for Efficient Path Optimisation in Online Ride-Hailing Carpooling</title>
      <link>https://trid.trb.org/View/2666430</link>
      <description><![CDATA[Online ride-hailing carpooling services often need help with bottlenecks, such as delayed response times and low computing efficiency, which negatively impact user experience and platform operation. Current path optimisation algorithms also need help managing real-time dynamic requests and large-scale computing challenges. In this respect, this paper proposes a bi-directional path-based online taxi carpooling optimisation model that considers road network conditions and time window constraints. It minimises operating and passenger travel costs under multiple constraints. The fitness assessment and acceptance criteria are optimised based on a genetic algorithm, combined with the temperature regulation mechanism of simulated annealing, and a hybrid genetic-simulated annealing algorithm (H-GASA) is proposed. In addition, this paper brings the parallel repair mechanism and accelerates the solution repair process using modern multi-core processors and parallel computing framework, significantly improving the solution efficiency. The experimental results show that the H-GASA algorithm substantially reduces the passenger travelling time and vehicle operating cost under multiple time windows, which is better than the existing algorithms and effectively solves the common premature convergence problem of genetic algorithms. The study verifies the efficiency and reliability of the algorithm in practical applications and provides strong technical support for optimising online carpooling services.]]></description>
      <pubDate>Tue, 26 May 2026 09:41:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666430</guid>
    </item>
    <item>
      <title>(Un)Easy Riders: The real-world potential of integrating carpooling into digital travel planners</title>
      <link>https://trid.trb.org/View/2669972</link>
      <description><![CDATA[Digital travel planners offering inter-modal mobility solutions that combine public transport and carpooling services could help reduce car dependency affecting many Western countries, and may be particularly beneficial within Mobility-as-a-Service (MaaS) platforms. However, policy-makers lack evidence on their actual capability to support the transition from car dependency and recommendations to enhance their potential impact. We fill this gap, providing empirical evidence on the real-life use of an app-based travel planner that has public transport as its backbone and exploits carpooling ride offers by its users and shared active mobility services as opportunities to extend public transport network. The platform was made freely available in three Swiss regions for a one-year long trial between 2023 and 2024, and used by 624 volunteers. We analyse how it was used and by whom, by accessing automatic in-app data (N = 654), running a survey at the start of app use (n = 160), and performing semi-structured interviews with app users (n = 11). Relevant policy-findings indicate that the platform was unable to practically deliver novel inter-modal mobility services, due to insufficient carpooling ride offers by its users. Despite peer-to-peer feedback, in-app chatting features, notifications, and even monetary incentives, it did not raise the needed “critical mass” of carpooling offers. To produce tangible mobility impact and support the needed social change, we provide the following policy recommendations for future initiatives: (i) prioritise community building activities, rather than technology development aspects; (ii) leverage pre-existing, real-life communities; and (iii) rely on already existing apps already counting on a large community of users.]]></description>
      <pubDate>Fri, 15 May 2026 15:44:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669972</guid>
    </item>
    <item>
      <title>A study on the use of informal ridesharing in Scotland</title>
      <link>https://trid.trb.org/View/2682148</link>
      <description><![CDATA[The spread of shared mobility systems, for example ridesharing, can be a valid alternative to the use of private modes of transport. Indeed, a high number of private vehicles generate traffic congestion and air pollution. Ridesharing provides a cheaper way to travel, reducing traffic and environmental impact. It can be defined as the shared use of private cars between a group of people with similar origins and destinations. In order to support and improve this mode of transportation, it is important to identify the reasons and variables involved in their use. To this end, we took into consideration available data from the Scottish Household Survey in 2017. Using binary logistic regressions, it was possible to highlight the most relevant variables in the use of informal ridesharing. The results showed how the non-possession of cars was the most significant factor in the use of informal ridesharing systems.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682148</guid>
    </item>
    <item>
      <title>An enhanced approximate dynamic programming approach to on-demand ride-Pooling</title>
      <link>https://trid.trb.org/View/2643288</link>
      <description><![CDATA[Ride-pooling services have been growing in popularity, increasing the need for efficient and effective operations. The main goal of ride-pooling services is to maximise the number of passengers served while limiting wait and delay times. However, factors such as the timing and volume of passenger requests, pick-up and drop-off locations, available vehicle capacity, and the trajectory to fulfil multiple requests introduce high degrees of uncertainty, creating challenges for ride-pooling operators. This study aims to expand the current state-of-the-art Approximate Dynamic Programming (ADP) approach for ride-pooling services, introduce key extensions, and perform a comparative analysis with the Neural Approximate Dynamic Programming (NeurADP) approach to optimise the efficiency and effectiveness of these services. Specifically, we develop an ADP approach that incorporates three important problem specifications: (i) pick-up and drop-off deadlines, (ii) vehicle rebalancing, and (iii) allowing more than two passengers in a vehicle. We conduct a detailed numerical study with the New York City taxi-cab dataset and a dataset of taxi-cab requests collected in the city of Chicago. We also provide a sensitivity analysis on key model parameters such as wait and delay times, passenger group sizes, and vehicle capacity. Our comparative analysis highlights the strengths and limitations of both ADP and NeurADP methodologies. Network density and road directionality are found to significantly impact the performance. NeurADP is found to be more efficient in learning value function approximations for larger and more complex problem settings than the ADP approach. However, in less complex cases, ADP is shown to outperform NeurADP.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643288</guid>
    </item>
    <item>
      <title>Exploring peer-to-peer paid carpooling in Bogotá: A path to sustainable shared mobility</title>
      <link>https://trid.trb.org/View/2659405</link>
      <description><![CDATA[Carpooling is a sustainable transportation alternative that allows users with similar destinations to share private vehicles, contributing to reductions in energy consumption, pollutant emissions, and traffic congestion. While not a comprehensive solution, carpooling can lower private car use and increase vehicle occupancy rates. However, most carpooling initiatives have been limited in scope, often operating on a small scale or within corporate frameworks, restricting their potential for widespread adoption. In Latin America, where ride-hailing services are popular despite regulatory issues, carpooling remains uncommon. In Bogotá, an exception is the informal service called “Wheels,” which operates through WhatsApp groups to coordinate rides, focusing on university communities. This service quickly became a preferred mode of transport for students, faculty, and staff. This study aims to identify the factors driving the success of this informal initiative. A survey of 470 university community members was conducted, incorporating a discrete choice experiment to evaluate the attributes influencing their stated likelihood of use. Analytical methods included multiple correspondence analysis, logit models, and machine learning techniques. Our findings reveal that the adoption of carpooling is significantly influenced by price sensitivity, safety perceptions (particularly among women), reluctance to share rides with strangers, and demographic factors such as age and socioeconomic status. These insights offer valuable guidance for enhancing the appeal and scalability of carpooling as a door-to-door, reliable transportation alternative, particularly in similar sociocultural contexts as our case study.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659405</guid>
    </item>
    <item>
      <title>BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch</title>
      <link>https://trid.trb.org/View/2617726</link>
      <description><![CDATA[This paper introduces Localized Bipartite Match Graph Attention Q-Learning (BMG-Q), a novel Multi-Agent Reinforcement Learning (MARL) algorithm framework tailored for ride-pooling order dispatch. BMG-Q advances ride-pooling decision-making process with the localized bipartite match graph underlying the Markov Decision Process, enabling the development of novel Graph Attention Double Deep Q Network (GATDDQN) as the MARL backbone to capture the dynamic interactions among ride-pooling vehicles in fleet. Our approach enriches the state information for each agent with GATDDQN by leveraging a localized bipartite interdependence graph and enables a centralized global coordinator to optimize order matching and agent behavior using Integer Linear Programming (ILP). Enhanced by gradient clipping and localized graph sampling, our GATDDQN improves scalability and robustness. Furthermore, the inclusion of a posterior score function in the ILP captures the online exploration-exploitation trade-off and reduces the potential overestimation bias of agents, thereby elevating the quality of the derived solutions. Through extensive experiments and validation, BMG-Q has demonstrated superior performance in both training and operations for thousands of vehicle agents, outperforming benchmark reinforcement learning frameworks by around 10% in accumulative rewards and showing a significant reduction in overestimation bias by over 50%. Additionally, it maintains robustness amidst task variations and fleet size changes, establishing BMG-Q as an effective, scalable, and robust framework for advancing ride-pooling order dispatch operations.]]></description>
      <pubDate>Tue, 24 Mar 2026 16:23:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617726</guid>
    </item>
    <item>
      <title>Sustainable shared mobility as social common capital: conceptual framework and case analysis</title>
      <link>https://trid.trb.org/View/2643477</link>
      <description><![CDATA[While research on shared mobility has primarily focused on its environmental and economic benefits, theoretical development on equitable and sustainable governance remains underdeveloped. This study addresses this gap by proposing a governance framework based on the theory of social common capital to overcome challenges such as the tragedy of the commons and the free-rider problem. Critically examining the limitations of traditional management models, such as private and community-based, the study develops a conceptual framework in which an independent group of experts, trusted by citizens, manages shared mobility as social common capital. This approach, rather than relying solely on market principles or state control, ensures sustainable governance. Furthermore, it explores institutional challenges in designing a legal framework to guarantee equitable access and benefit sharing. To empirically explore this conceptual framework, the study also analyses shared mobility initiatives in 13 Japanese municipalities experiencing ageing and depopulation. The findings provide a benchmark for new governance models that support the sustainable development of shared mobility.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643477</guid>
    </item>
    <item>
      <title>Insights into Ridepooling efficiency: A case study analysis using a novel operational performance measure framework</title>
      <link>https://trid.trb.org/View/2636454</link>
      <description><![CDATA[The efficiency of demand responsive transport (DRT) services, such as ridepooling, has garnered significant attention across various (simulation) studies. However, a deeper look into the metrics employed to assess system performance exposes notable discrepancies in their utilization. Notably, certain indicators, such as the pooling rate, demonstrate a susceptibility to manipulation based on input parameters, potentially skewing results to give a more favorable impression of service performance. In this paper, we show that, under such circumstances, achieving a fair comparison between study outcomes and with traditional (public) transport modes becomes challenging.In light of these challenges, this study introduces a novel operational performance indicator: Operational System Efficiency (OSE). OSE is tailored to evaluate the operational efficiency of ridepooling systems in a holistic way. In particular, it combines important operational indicators such as detour factor and empty kilometers share, which are considered in numerous analyses. Hereby, OSE is fostering a more equitable assessment of service performance. We applied this methodology and calculated the indicators provided in other studies to two real-world trip datasets from ridepooling services in Berlin and Münster, Germany, and compared the results. Distinct disparities emerge in comparison with conventional efficiency indicators. Consequently, the proposed OSE holds promise for stakeholders, including service providers, public transport companies, and regulatory authorities, as a valuable tool for determining the suitability of a ridepooling service for a given locale relative to other transport modalities, while also providing a transparent assessment of its efficiency.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:49:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636454</guid>
    </item>
    <item>
      <title>Does subsidy increase the use of carpooling via platforms? The case of short-distance carpooling in France</title>
      <link>https://trid.trb.org/View/2627424</link>
      <description><![CDATA[Many initiatives have been introduced worldwide by governments and industry to promote the use of carpooling. In France, some local authorities have introduced carpooling subsidy policies since 2019 to encourage carpooling trips. We estimate the effect of local carpooling subsidies on the usage of platform-organized short-distance carpooling, using a difference-in-differences design that exploits variation across French “Communautés de Communes” (i.e. local authorities) in both the amount of subsidy and the timing of subsidy policy start. We find that, on average, the introduction of the subsidy increases the number of monthly short-distance carpool trips organized by platforms by approximately 5.2 trips per 1,000 inhabitants in the area covered by the local authority, and this effect increases over time. The study of the effect of the subsidy amount shows that a €1 increase in the carpooling subsidy improves the number of monthly carpool trips organized by platforms by 3.9 trips per 1,000 inhabitants. These average effects mask considerable heterogeneity, with subsidy increasing carpooling use more in larger and more densely populated local authority areas, and the effect being negligible in the smallest and least densely populated local authority areas. We also use survey results to investigate opportunity, windfall and environmental effects of the policy. It indicates our estimates of subsidy effects should be reduced by one-third or one-half to obtain the net new carpoolers effect, as between half and a third of the new carpoolers joining the platforms after the introduction of the subsidy are due to the opportunity effect. We find that carpooling subsidy amount needed to save one ton of CO2 is roughly between €1000 and €1300.]]></description>
      <pubDate>Thu, 26 Feb 2026 09:14:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627424</guid>
    </item>
    <item>
      <title>Efficient and stable ride-pooling through a multi-level coalition formation game</title>
      <link>https://trid.trb.org/View/2627387</link>
      <description><![CDATA[Ride-pooling has the potential to offer a sustainable solution for urban mobility by reducing vehicle use and emissions through shared trips. However, its adoption remains limited due to poor matching performance. Many requests fail to form feasible pools, and even successful matches often involve long detours or minimal cost savings. These inefficiencies largely arise from fragmented market structures: most operators act independently, restricting matching to their own request pools and limiting the formation of beneficial coalitions. Aggregation platforms improve efficiency by integrating regional operators through unified dispatch systems, but raise concerns over long-term stability. Differences in operator cost structures and market shares may incentivize deviation, at the same time, passengers may reject assigned payments if more attractive alternatives exist. To address these challenges, we propose a multi-level coalition formation game that jointly models operator and passenger collaboration. At the upper level, operators play a non-cooperative game to decide coalition partners. At the lower level, passengers are grouped into shared trips through a cooperative game that ensures individually rational payments. The two layers are coupled via constraint propagation, forming a unified decision-making process. We evaluate our framework using real-world data from three Chinese regions—Chengdu, Haikou, and the Ningxia Hui Autonomous Region—chosen to reflect diverse urban and regional contexts. Compared to independent operations, our approach increases vehicle occupancy by 14%–28%, reduces total costs by 10%–15%, and shortens average travel distances by 4%–5%. The system maintains stable coalition structures with operator deviation rates below 6.81% and near-zero passenger deviation rates.]]></description>
      <pubDate>Wed, 25 Feb 2026 09:11:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627387</guid>
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