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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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    <item>
      <title>Empirical distribution of probe vehicle penetration rates and implications for traffic state estimation</title>
      <link>https://trid.trb.org/View/2701453</link>
      <description><![CDATA[𝘗𝘳𝘰𝘣𝘦 𝘷𝘦𝘩𝘪𝘤𝘭𝘦𝘴 (PVs) provide a sampled view of urban traffic, with the PV 𝘱𝘦𝘯𝘦𝘵𝘳𝘢𝘵𝘪𝘰𝘯 𝘳𝘢𝘵𝘦 (PR) being a key parameter for estimating the network traffic state. To date, existing studies have not reached a consensus on the PR distribution across urban networks. While PRs are often assumed to be uniformly distributed in traffic state estimation, empirical evidence increasingly highlights their spatiotemporal heterogeneity. This study investigates the spatiotemporal distribution of PRs and the resulting estimation errors using matched loop detector and PV data from Madrid, Luzern, Zurich, and London. By examining the uniformity of PR distributions across varying spatiotemporal scales, we find that PRs exhibit high uniformity only within small spatiotemporal scales (e.g., spatial radius  < 0.4 km and temporal duration  < 0.5 h). As the spatiotemporal scale increases, the statistical validity of the uniform-PR assumption declines significantly. However, notably, we reveal that this statistical non-uniformity does not compromise the accuracy of PV-based estimation of the Macroscopic Fundamental Diagram (MFD). Instead, the estimation error decreases and stabilizes at a low level (typically below 6%) as the spatial scale expands, which is attributed to the weak correlation between traffic volumes and PR deviations, allowing local over- and underestimations to cancel out at the network level. Therefore, despite pronounced non-uniformity at large spatiotemporal scales, the uniform-PR assumption remains viable for PV-based MFD estimation, yielding only minor estimation errors when a reliable PR is applied. In other words, applying a reliable PR value for the uniform-PR assumption is more critical than the actual uniformity of the PRs. Overall, these results delineate clear boundary conditions for applying the uniform-PR assumption and offer practical insights for advancing PV-based traffic state estimation.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701453</guid>
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    <item>
      <title>Differentiated street greenery cooling of road surfaces: Spatially varying nonlinear effects from street view and geographically weighted machine learning</title>
      <link>https://trid.trb.org/View/2709805</link>
      <description><![CDATA[Urban vegetation is a critical nature-based solution for mitigating the urban heat island effect, yet its cooling efficacy on road surfaces varies spatially due to differences in vegetation type, structure, and local urban morphology. This study investigates the spatially varying nonlinear cooling effects of differentiated street greenery, including trees, shrubs, and grass, on land surface temperature (LST) using Google Street View (GSV) imagery in Zurich, Switzerland. A fine-tuned Mask2Former deep learning model was employed for semantic segmentation to accurately extract five street view factors (trees, shrubs, grass, buildings, and sky) from GSV images collected via three methods: multi-view collection, panoramic image collection, and fisheye. These factors were then compared across methods, revealing methodological biases but limited influence on subsequent modeling outcomes. A geographically weighted random forest model integrated with SHAP (SHapley Additive exPlanations) analysis was developed to capture spatially non-stationary and nonlinear relationships between the five view factors and LST derived from Landsat 8. Results show that trees exert the strongest nonlinear cooling effect, followed by shrubs, while grass provides moderate and context-dependent benefits; buildings consistently drive warming, and sky view has minimal impact. The three GSV collection methods yield comparable predictive performance and feature interpretations, suggesting practical flexibility in method selection. This framework advances street-level urban climate research by combining pedestrian-perspective data with local machine learning interpretability, offering evidence-based insights for targeted greening strategies in heat-vulnerable urban areas.]]></description>
      <pubDate>Wed, 26 Aug 2026 10:13:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709805</guid>
    </item>
    <item>
      <title>Exploring the impact of accessibility on leisure social diversity: A Schelling model approach to 15-minute cities</title>
      <link>https://trid.trb.org/View/2694790</link>
      <description><![CDATA[One proposal to improve accessibility is the 15-minute city; the goal is to create a city where all daily necessities are reachable within a 15-minute walk or bike ride from home. However, a potential consequence is the reduction of the role of city centers, which are essential places of interaction among diverse social groups. This study investigates the potential impact that a 15-minute city would have on the co-presence of individuals from different social groups. Using an Agent-Based model and Zurich as a case study, we analyze how a relocation of POIs, as proposed by the 15-minute city, impacts the co-presence of socioeconomic groups in leisure activities, with a focus on bars and restaurants. Two conclusions can be drawn. First, the reorganization of venues reduces the distance traveled by 1.2 kilometers for restaurants and bars. Second, the dissimilarity index of the city center increased by 0.03 (indicating less diversity), but the average dissimilarity of the city decreased by 0.11 (indicating more diversity), showing that a redistribution of POIs following a 15-minute ideal increases the overall co-presence of individuals during leisure activities while reducing the distance traveled. Such insights are valuable for urban and transport policy, as they help to understand the broader social implications of a spatial reorganization of services and activities.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694790</guid>
    </item>
    <item>
      <title>Rethinking cost–benefit analysis for transformative cycling policies: Integrating behavioral change and the logsum method</title>
      <link>https://trid.trb.org/View/2681487</link>
      <description><![CDATA[Transforming urban transport systems toward sustainability requires a critical re-evaluation of the appraisal methods used in policy analysis. While cycling infrastructure offers clear environmental, health, and livability benefits, its economic evaluation through cost–benefit analysis (CBA) remains underdeveloped. This paper presents a CBA of the “E-Bike City” concept in Zurich, which involves a radical reallocation of road space from cars to bicycles and e-bikes, implemented within a large-scale MATSim agent-based transport model. We address methodological challenges in applying CBA to cycling projects, specifically demand forecasting, valuing consumer surpluses, and treating subjective safety. Consumer surpluses are calculated using both the conventional value of travel time savings (VTTS) rule-of-half approach and the logsum method derived from discrete choice models. We compare results with and without assuming behavioral change in response to the new transport supply. Our findings demonstrate that the transport demand model as well as the consumer surplus methodology significantly affect appraisal outcomes. Without accounting for preference change, both methods yield negative net present values (NPVs). In contrast, when behavioral adaptation is included, the logsum method produces strongly positive NPVs. The analysis also reveals substantial reductions in external costs, crashes, and greenhouse gas emissions. However, long-term decarbonization goals remain out of reach without further systemic changes, given projected population growth. We conclude that CBAs focusing on transformative, sustainable mobility policies require methodologies that reflect long-term behavioral adaptation and utility beyond travel time savings, making the logsum method a more suitable tool for sustainable transport appraisal.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:56:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681487</guid>
    </item>
    <item>
      <title>Fast-lane for planning cycling infrastructure: On the effectiveness and efficiency of cycling infrastructure planning processes</title>
      <link>https://trid.trb.org/View/2643360</link>
      <description><![CDATA[Timely development of cycling infrastructure is essential to achieving societal goals such as decarbonisation and cyclist traffic safety. However, delays in infrastructure project completion persist across many planning contexts, partly due to the infrastructure planning processes. This paper addresses the lack of academic research on infrastructure planning process improvement, specifically for cycling infrastructure, by applying a structured, three-step methodology—process mapping, process analysis and improvement proposal—to the case of Canton Zürich, Switzerland. The paper includes mapping the existing cycling infrastructure planning process, identifying process-related challenges using three decision-making criteria (technical readiness, societal consensus, and political-financial prioritisation), and proposing targeted improvements. Key findings highlight the need for timely planning mandates, early-stage cost overviews, and systematic treatment of uncertainty to enhance planning process efficiency. It is argued that these process modifications can accelerate the realisation of cycling infrastructure projects and improve alignment with long-term strategic goals such as achieving net-zero carbon emissions by 2050. By bridging the gap between planning process design and infrastructure outcomes, this study contributes an approach for analysing and improving planning processes. The findings are relevant for infrastructure planners, policymakers, and researchers seeking to support more effective and efficient cycling infrastructure development.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643360</guid>
    </item>
    <item>
      <title>Flexible facility requirements for strategic planning of airport passenger terminal infrastructure</title>
      <link>https://trid.trb.org/View/2614551</link>
      <description><![CDATA[Facility requirements determine how and when the capacity of airport passenger terminal facilities is adjusted over time to meet expected demand. Given high levels of uncertainty inherent in long-term airport planning, under and over provision of capacity is a recurrent risk, as conventional strategic planning methods fail to adapt dynamically to changing circumstances. This paper introduces a novel flexible capacity expansion model for airport terminals that considers simultaneously real options ‘on’ and ‘in’ systems. The model is validated for the provision of check-in facilities at Zurich Airport. Results confirm suggestions in the literature that incorporating flexibility creates planning and financial advantages over conventional alternatives. Indeed, for the case of Zurich, the financial value of the flexible alternative is approximately 5% higher than the best conventional phased plan. This also suggests that phasing developments can be carefully devised to produce satisfactory outcomes that enable ex-post application of flexibility ‘on’ systems.]]></description>
      <pubDate>Wed, 28 Jan 2026 14:41:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614551</guid>
    </item>
    <item>
      <title>Meet me halfway – Disentangling the factors affecting leisure joint destination choice</title>
      <link>https://trid.trb.org/View/2625759</link>
      <description><![CDATA[This study investigates the factors that influence joint-leisure activities and travel between dyads (pairs of friends, family or acquaintances). We draw on a special dataset of self-reported frequently visited leisure destinations conducted in Zurich, and estimate two discrete choice models that consider relationship attributes, such as relationship time length, strength and gender homophily. The first model analyzes home-visits, as the probability of a person hosting a social activity at their place; while the second model is an out-of-home destination choice that quantifies the impact of relationship attributes on the distance traveled for social activities. The findings show that long relationships, (relationship time length > 7 years), have a higher probability of hosting social activities by 3.87 percentage points, and having a strong relationship (when survey respondent can draw on ≥3 expressive resources from the other person) results in higher probability of hosting by 10.5 percentage points. For social activities outside the home, strong ties (≥3 expressive resources from the other person) travel 0.88 km farther on average, and long ties (>7 years) 1.54 km farther, relative to weak (<3 expressive resources) and short (≤7 years) ties, respectively. However, dyads in a relationship that is both long and strong travel an average of 3.18 kilometers extra than those in relationships that are neither long nor strong, showing that these relationship attributes have an even higher impact when combined.]]></description>
      <pubDate>Fri, 12 Dec 2025 17:04:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625759</guid>
    </item>
    <item>
      <title>Mapping the Swiss Road and Rail Infrastructure Planning Process and Identifying Potential Areas for Improvement</title>
      <link>https://trid.trb.org/View/2594272</link>
      <description><![CDATA[The Swiss transportation infrastructure planning process, like all infrastructure planning processes, requires that the organizations involved determine how societal needs will change over time, how the current infrastructure may accommodate these needs and which interventions will best help modify the infrastructure if modification is required. This is challenging due to the divergence in needs of society, the many different organizations involved in the process, the long duration of the process and the iterative nature of the process. By addressing the efficiency of these processes, society’s changing needs will be accommodated more quickly. Efforts to improve infrastructure planning processes, however, lack an overarching view of the process and therefore cannot optimally improve the process. The work presented in this paper addresses this gap by modeling the Swiss road and rail infrastructure planning process for the first time, assessing its ability to meet societal needs, and making proposals for improvement. This is done by modeling the relevant portions of the planning process, analyzing how decisions are made within the process, and identifying challenges and opportunities in terms of efficiency and effectiveness. These challenges and opportunities are put in context using specific case studies in the canton of Zürich. The proposed opportunities for improvement are the adoption of an early planning network-benefit appraisal tool, the establishment of a coordinating body to align the many organizations involved, the systematic explicit consideration of the uncertainty related to planning decisions and the consideration for the time required for planning process tasks. It is suspected that these improvements would help policymakers shape planning processes to better enable planning objectives to be met efficiently and effectively.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:06:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594272</guid>
    </item>
    <item>
      <title>Walking environments for transit access: Travelers' perspectives</title>
      <link>https://trid.trb.org/View/2604274</link>
      <description><![CDATA[Efforts to address urban transport and sustainability challenges have increased interest in walking as a form of transport. Organizations such as the World Health Organization and the International Association of Public Transport emphasize integrating walking and transit in urban policy. To support this, the paper presents findings from 596 face-to-face interviews on walking trips to tram stops. Three regression analyses examine the effects of walking environments on (1) the pleasantness of the walk, (2) walking time, and (3) safety from traffic. Time pressure, crowding, traffic, and unattractive environments reduce pleasantness. Green environments double acceptable walking times, while elderly, younger, and first-time travelers tend to walk shorter distances. Safety decreases with street crossings but improves in green areas. Female travelers are more sensitive to compromised safety.A structural equation model further analyzes relationships between pleasantness, walking time, safety, and other factors. Longer walks correlate with reduced safety and pleasantness, while pleasantness increases with safety. Results from the structural equation model reveal a sensitivity to unpredictable delays in reaching stops. Streets with traffic and crowded areas increase delays and reduce pleasantness and safety, while green walking environments reduce delay risks. The findings identify specific environmental conditions that support walking to transit and highlight the potential for integrating walking and transit policies to create synergies that benefit both modes.]]></description>
      <pubDate>Fri, 14 Nov 2025 17:06:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604274</guid>
    </item>
    <item>
      <title>Dual-Powered Trolley Bus Inter-Stop Travel Energy Consumption Prediction Method: Temporal Feature Transformer Framework</title>
      <link>https://trid.trb.org/View/2611133</link>
      <description><![CDATA[Dual-powered trolley buses (DTBs), with their low vehicle weight, extended range, high passenger capacity, and low investment costs, play a significant role in reducing infrastructure construction expenses, decreasing energy consumption, and promoting lightweight construction of public transit vehicles. Reliable prediction of the inter-stop travel energy consumption of DTBs is a crucial prerequisite for flexibly adjusting routes in the case of operational anomalies and developing new routes without the need for overhead cables. Although many scholars have conducted extensive research on the operational energy consumption prediction of electric buses and achieved substantial theoretical and practical results, studies on inter-stop travel energy consumption prediction for DTBs, particularly those that integrate regression prediction and temporal dependency, remain relatively uncommon. To address this gap, this paper presents a DTB inter-stop travel energy consumption prediction method, the temporal feature transformer model architecture. This approach, based on the self-attention mechanism, models both temporal dependency and the correlation among various variables. Experiments conducted on real driving data from two DTBs operating within the Zurich public transport network from 2019 to 2022 demonstrate that the proposed model outperforms traditional baseline models. Specifically, the model achieves a mean absolute percentage error of 8.76%, reflecting a reduction of more than 26.4% compared to the best-performing baseline. The findings of this study contribute significantly to the dynamic scheduling and effective management of DTBs, enhancing public transportation service levels and delivering environmental benefits through energy savings and reduced emissions. These promising results underscore the practical utility of the model.]]></description>
      <pubDate>Sat, 18 Oct 2025 18:52:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611133</guid>
    </item>
    <item>
      <title>Cycling speed profiles from GPS data: Insights for conventional and electrified bicycles in Switzerland</title>
      <link>https://trid.trb.org/View/2577203</link>
      <description><![CDATA[Understanding cycling speed dynamics is crucial for effective transportation planning and infrastructure development. This study analyzes GPS-based cycling speed profiles in Zurich, Switzerland, focusing on conventional bicycles, e-bikes (25 km/h), and speed pedelecs (45 km/h). Using GPS data from 351 cyclists, the authors examine the influence of socio-demographic factors (age, gender, BMI), road infrastructure, gradients, and weather conditions on cycling speeds. The findings reveal that speed pedelecs achieve the highest speeds, frequently exceeding residential speed limits, raising questions about their classification and integration into urban mobility networks. Machine learning models identify road gradients, BMI, and age as key determinants of cycling speed. Additionally, results show that e-bikes and speed pedelecs experience longer intersection delays. These insights offer valuable contributions to urban transport policies, cycling infrastructure planning, and traffic modeling, ensuring safer and more efficient mobility solutions.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577203</guid>
    </item>
    <item>
      <title>Supervisory controller for minimizing fuel consumption and NOₓ emissions of plug-in hybrid electric vehicles operating in zero-emission zones</title>
      <link>https://trid.trb.org/View/2566030</link>
      <description><![CDATA[This paper introduces a supervisory controller designed for Plug-in Hybrid Electric Vehicles (PHEVs) to minimize total energy consumption and tailpipe NOₓ emissions while adhering to Zero Emission Zone (ZEZ) constraints. The case study exploits historical driving cycle data from an urban bus route in Zurich to analyze trip correlations, where a ZEZ restriction was added to assess the vehicle performance under such conditions. A PHEV bus model was built, integrating the powertrain and After-Treatment System (ATS), where an electric heater is included to mitigate NOₓ emissions. The supervisory controller is tasked with determining the optimal power split and the electric heater power to ensure adherence to feasible operating conditions along the route. Historical driving cycle data analysis demonstrates that the speed profiles along the selected route exhibit similarities. This observation is leveraged by a Dynamic Programming (DP) optimization, where an arbitrary bus trip is employed to generate a cost-to-go matrix. Then, the resulting cos-to-go matrix was indexed by the position in the route and is used on the real-time controller by applying the one-step look-ahead roll-out algorithm. Simulation results demonstrate the controller effectiveness in addressing ZEZ restrictions, presenting a trade-off between energy consumption and tailpipe NOₓ emissions. A benchmark was carried out, comparing the results obtained with the DP solution as the baseline, assuming perfect knowledge of driving cycle disturbances, revealing a 7% increase in tailpipe NOₓ emissions and a 1.3% increase in fuel consumption compared to the theoretical minimum and fulfilling the ZEZ restrictions in all the cases.]]></description>
      <pubDate>Mon, 25 Aug 2025 12:24:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2566030</guid>
    </item>
    <item>
      <title>Mobility On Demand: What About the Weekend?</title>
      <link>https://trid.trb.org/View/2589120</link>
      <description><![CDATA[Mobility on demand (MoD) services like ride-hailing, ride-sharing, and car-sharing are changing travel behavior by providing increased options and flexibility. These services can be best understood and planned for through the use of detailed computer simulations. However, existing simulations predominantly focus on modeling average working days, characterized by high and predictable travel demand. This approach overlooks the distinct travel patterns observed during weekends. Unlike weekdays, which feature pronounced peak hours, weekend travel is distributed more evenly throughout the day, particularly on Saturdays. This study compares the differences in travel demand patterns between weekends and weekdays and their possible impact on policies drawn from MoD simulations. Using an agent-based simulation framework MATSim, we simulate the introduction of an autonomous mobility on demand (aMoD) service to Zurich, Switzerland, and its environs. We then compare weekday and weekend travel patterns highlighting unique aspects of weekend travel and their implications for MoD service operations. The findings suggest that transport policies should account for the unique characteristics of weekend travel. The results provide insights into modal shifts, showing how more public transport and private vehicle trips could be replaced by MoD services during weekends, especially for long-distance travel. Furthermore, results show that optimal fleet sizes vary between weekdays and weekends owing to differences in demand. While weekends see higher MoD demand, wait times don’t necessarily increase. However, longer detours for pickups may extend travel times. Accounting for weekend travel in simulations helps ensure policy planning supports reliable service while balancing wait times, travel times, occupancy, and operational costs.]]></description>
      <pubDate>Mon, 18 Aug 2025 16:43:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589120</guid>
    </item>
    <item>
      <title>Clustering moves: Spatial network analysis in residential mobility</title>
      <link>https://trid.trb.org/View/2480384</link>
      <description><![CDATA[In the past sixty years, network analysis has become a valuable analytic tool in the study of interpersonal relationships and also in the construction of spatial relationships within urban geographies. Researchers have applied network analysis in a wide array of spatial applications; however, the power of network analysis has yet to be applied to understanding patterns in residential mobility. Residential movement patterns take place over longer time scales and potentially greater distances than daily journeys or commutes, but equally create networks connecting places and people. This research demonstrates how applying network analysis to residential moves may lend insight to open questions in the study of residential mobility, using fine-grained data on residential moves held by the city of Zurich, Switzerland. By creating a weighted directed network linking locations on a 400 meter coordinate grid with residents moving between them, the work demonstrates that network analysis can reveal geographies that overwrite the political boundaries commonly used to categorize residential spaces in the city. The new geographies produced by this method capture a wholistic picture of moving behaviour independent of reported residential choices. By tracking completed moves rather than stated aims, the uncovered mobility geographies sidestep the common categorisation of voluntary versus structurally determined residential choices, aggregating accumulated choices to demonstrate some of the geographic specificities of residential moving behaviour.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480384</guid>
    </item>
    <item>
      <title>Bike network planning in limited urban space</title>
      <link>https://trid.trb.org/View/2476524</link>
      <description><![CDATA[The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network planning is a tedious process, relying on heuristic computational methods that frequently overlook the broader implications of introducing new cycling infrastructure, in particular the necessity to repurpose car lanes. In this work, the authors call for optimizing the trade-off between bike and car networks, effectively pushing for Pareto optimality. This shift in perspective gives rise to a novel linear programming formulation towards optimal bike network allocation. The authors' experiments, conducted using both real-world and synthetic data, testify the effectiveness and superiority of this optimization approach compared to heuristic methods. In particular, the framework provides stakeholders with a range of lane reallocation scenarios, illustrating potential bike network enhancements and their implications for car infrastructure. Crucially, the authors' approach is adaptable to various bikeability and car accessibility evaluation criteria, making their tool a highly flexible and scalable resource for urban planning. This paper presents an advanced decision-support framework that can significantly aid urban planners in making informed decisions on cycling infrastructure development.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2476524</guid>
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