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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>The feasibility of suburban single-track laterally positioned light rail transit lines in the modern United States</title>
      <link>https://trid.trb.org/View/2692336</link>
      <description><![CDATA[In the first three decades of the twentieth century, single-track laterally positioned trolley infrastructure was responsible for most of the interurban and suburban transit traffic in the northeast United States. These designs are nowhere to be found in today’s light rail landscape in the United States, but they can be found in Europe. With the changing nature of suburbs in the Northeastern United States, where public rights-of-way are limited, light rail lines built to this design might again make sense. A feasibility study was conducted to determine if single tracking and/or lateral positioning of light rail lines are design choices that are practical under modern conditions. This article presents the outcomes of an expert panel discussion and modeling to determine that feasibility. This article also concludes with guidance on design implementation factors such as right-of-way width and design, safety devices and considerations, side roads and driveways, and applicable light rail service headways.]]></description>
      <pubDate>Wed, 20 May 2026 17:04:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692336</guid>
    </item>
    <item>
      <title>Regional passenger rail fare and related factors</title>
      <link>https://trid.trb.org/View/2692333</link>
      <description><![CDATA[The main purpose of this study is to clarify the actual fare level and factors affecting the fare level in Japan. In numerical calculations, Ramsey pricing is applied for different demand groups by using a database of 133 railways between 2015 and 2020. Ramsey pricing is intended to maximize economic welfare, under the constraint that fare revenues cover the costs of service provision. First, among three different demand groups, the fare levels are highest for non-commuter regular fares, followed by work commuter pass fares, and then student commuter pass fares. Another important finding is that the markup rate for student commuter pass fares is only about 50% of the marginal cost, while the non-commuter regular fares are about twice the marginal cost. This result might show the possibility of cross-subsidy between demand groups in fare setting. As for factors affecting the fare level, seemingly unrelated regression (SUR) analysis for fares of the three demand groups is applied by using 732 observations between 2015 and 2020. The SUR method is one regression method to estimate a system of several equations for these services. The results show that both, a longer network length and a higher share of rail transportation work increase fare levels. However, neither a higher profit level of railway companies nor public ownership has the effect of lowering fares, but in fact have the opposite effect of raising fares. Yardstick regulation works in the direction of reducing fares, as expected by the government, but the effects of the vertical structure organizational type are inconclusive.]]></description>
      <pubDate>Wed, 20 May 2026 17:04:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692333</guid>
    </item>
    <item>
      <title>Analysis of bus dwell times from automated passenger count data and the impact of dwell-time variability on the performance of transit signal priority</title>
      <link>https://trid.trb.org/View/2692331</link>
      <description><![CDATA[The design of transit signal priority (TSP) systems requires knowledge of dwell-time distributions at bus stops within the block. Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson’s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692331</guid>
    </item>
    <item>
      <title>Scheduling electric vehicles by simulated annealing with recombination through ILP</title>
      <link>https://trid.trb.org/View/2685623</link>
      <description><![CDATA[In this paper, we consider the electric vehicle scheduling problem (e-VSP): a set of trips corresponding to a given timetable have to be driven by a set of electric buses with limited capacity. This problem, like many other planning problems, boils down to assigning to each bus a subset of the trips with the obvious side constraint that the selected subset can be feasibly driven by this single bus; such a feasible subset is called a vehicle schedule. If we know all possible vehicle schedules, we can select the best set of these by solving an integer linear program (ILP). This idea has inspired many researchers to the heuristic of finding a decent subset of all vehicle schedules using the technique of column generation and then solve the ILP. For the e-VSP, this approach leads to good solutions, but there is still room for improvement. Instead of using column generation, we apply simulated annealing to find the subset of vehicle schedules that we use as input for the ILP. For the e-VSP, this leads to better solutions. Moreover, this approach, which we call simulated annealing with recombination through ILP, is generally applicable and has as a clear advantage that we do not have to solve the pricing problem, because this approach increases the application possibilities and takes far less time.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685623</guid>
    </item>
    <item>
      <title>The price of proximity: analysing distance-based fare structures in liberalised Greek coastal shipping</title>
      <link>https://trid.trb.org/View/2685408</link>
      <description><![CDATA[This article examines whether passenger fares in Greek coastal shipping remain distance-driven after market liberalisation. Using a harmonised 2025 cross-section of 52 non-PSO routes (i.e. routes not operated under Public Service Obligation—PSO—contracts), we measure the association between the lowest economy-class passenger fare and distance, compare 11 curve families, and derive a route-level fare benchmark. A no-intercept power function emerges as the empirically preferred specification. For high-speed services (S), distance explains 99.3% of fare variation; for conventional ships (CS), 99.6%. In both cases the estimated elasticity is below unity, so fares rise with distance but less than proportionally, implying a declining €/nm profile. Market concentration, measured via route-level Herfindahl–Hirschman indices (HHI) based on frequencies and capacities, places almost all lines in the “highly concentrated” range, with many effective monopolies. The average model-implied benchmark fare per nm is €0.72 for S and €0.33 for CS. The benchmark is a descriptive statistical construct that summarises the observed distance–fare pattern in a concentrated market. It is not a regulatory target and should be interpreted alongside information on costs, service quality and demand.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685408</guid>
    </item>
    <item>
      <title>Reducing the number of shunt movements by rescheduling trains</title>
      <link>https://trid.trb.org/View/2685406</link>
      <description><![CDATA[In the off-peak hours, rolling stock is put on shunting yards. Since the number of used trains increases and the size of the shunting yards remains the same, it becomes harder and harder to find a feasible schedule for the shunting yards. The Dutch Railways (NS) is developing a software package that is capable of finding feasible schedules, but these often contain a high number of shunt movements, which is undesirable. In this paper, we propose a method that can reduce the number of shunt movements in existing feasible schedules by repeatedly rescheduling trains. We repeatedly reschedule one train at a time using the observation that finding a new schedule is equivalent to finding where and when a train will park. We build a model that allows us to find feasible parking locations and time intervals while taking into account the length of the tracks. By carefully looking at the position of the trains on the track, we are also able to avoid a special type of movement, called move-up movements. We apply a variant of a shortest path with time windows algorithm on this model, resulting in a schedule with the smallest number of movements for the rescheduled train. We then extend this approach to reschedule two trains simultaneously. We conclude with experiments on real-world data and a description of other possible use cases for the developed algorithm.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685406</guid>
    </item>
    <item>
      <title>Public transport service accessibility index: a new approach incorporating residents’ location and transport capacity: a case study in NCSA (Algiers)</title>
      <link>https://trid.trb.org/View/2683128</link>
      <description><![CDATA[The aim of this paper is to develop an evaluation index for resident accessibility to public transport (PTAIR). This index expands over previous measures of public transport accessibility by incorporating additional parameters, specifically spatial resident distribution and transport capacity, with the objective of integrating service quality into accessibility assessment. Research that simultaneously accounts for population distribution and transport capacity as key indicators of accessibility remains limited. To address this gap, the proposed index was applied to the New City of Sidi-Abdallah (NCSA) in Algiers as a case study. Accessibility levels were evaluated and represented through maps generated using a geographic information system. The results reveal a significant mismatch between the transport system and the spatial distribution of residents, particularly in newly developed collective residential neighborhoods where access to public transport is either poor or very poor. These findings suggest that the PTAIR index can serve as a valuable tool for improving public transport planning by aligning accessibility more closely with resident distribution.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683128</guid>
    </item>
    <item>
      <title>Network-wide transfer synchronization strategies in a public bus system with real-time AVL and smart card data</title>
      <link>https://trid.trb.org/View/2683078</link>
      <description><![CDATA[This paper presents a scalable methodology for real-time transfer synchronization in urban bus networks, using online stochastic optimization (OSO). The approach integrates three key components. First, an offline arc-flow model captures all control tactics—hold, speedup, and skip-stop—for a main line and its feeder connections, using a graph-based representation over a fixed control horizon. Second, the Regret (R) algorithm operates in real time within an OSO framework, leveraging the offline model to evaluate multiple stochastic scenarios and to select robust control tactics. Third, a network-wide simulator (NWS) integrates the full OSO framework and re-optimizes decisions dynamically at each bus departure from any stop, allowing the coordination of multiple interconnected lines. The NWS is applied to the transit network of Laval, Canada, using historical vehicle positions and smart card validations to replicate real-time stochastic conditions. Results show significant improvements in both passenger travel and transfer times across a variety of network structures, highlighting the scalability and applicability of real-time transfer synchronization for urban multi-line transit networks.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683078</guid>
    </item>
    <item>
      <title>Transfer accessibility and convenience of major train stations in Chinese cities</title>
      <link>https://trid.trb.org/View/2692328</link>
      <description><![CDATA[The expansion of high-speed rail networks has significantly enhanced the convenience of rail travel for the Chinese population. However, when direct train routes between origin and destination are unavailable, passengers are required to transfer. These transfers can occur within the same station or between different stations across a city. This study initially applies kernel density analysis to examine the spatial distribution of train stations in China. Subsequently, it assesses the accessibility of transfers to major urban stations based on the shortest travel time. Finally, the study evaluates the convenience of transfers through network analysis. The key findings are as follows: (1) On average, there are approximately 98 stations per province in China, with the highest concentration in the four major urban clusters and the densest train distribution in the Yangtze River Delta and Pearl River Delta regions. (2) In China, 64.69% (218) of the cities at the prefecture level or above offer both conventional and high-speed rail services, with 108 cities facilitating same-station transfers averaging 41.28 min, and the longest transfer time being 1.48 h. (3) Beijing, Shanghai, and Guangzhou are the central hubs of China’s rail passenger transport, characterized by numerous stops and high transfer convenience, with most provincial capitals and directly administered municipalities also exhibiting high transfer convenience. These findings can provide data support and a theoretical basis for transportation planners in formulating urban transportation plans, and at the same time enrich the theoretical basis of transportation accessibility research.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692328</guid>
    </item>
    <item>
      <title>A Logistic Burr mixture autoregressive model for impact quantification of contributing factors and prediction on the bimodal behavior in bus section travel time</title>
      <link>https://trid.trb.org/View/2692327</link>
      <description><![CDATA[This paper introduces a novel Logistic Burr mixture autoregressive model (LBMAR) for two key bus scheduling and operation problems. First, the LBMAR model effectively analyzes and quantifies the individual and joint impacts of contributing factors on the time-varying bimodal distribution of bus section travel time. Unlike traditional approaches that examine factors between bus sections, our study investigates their impact on the validated bimodal variations in travel time for various operating environments of bus sections. Second, the LBMAR model jointly accounts for contributing factors in point and interval predictions of bus section travel time. Validated using six months of data and 169 bus section travel time observations with passenger information, we meticulously analyze four key contributing factors: day of the week, time of the day, bus occupancy changes, and neighboring bus section's travel time variability. The results show a remarkable correlation between bus occupancy changes and bimodal variations in bus section travel time. Moreover, the LBMAR model exhibits significant improvements in point and interval predictions, particularly for high to medium levels of variations in bus section travel time. These findings have profound implications for real-time bus operation management. By effectively identifying, quantifying, and managing the impact of these contributing factors, bus transportation operators can make informed decisions to optimize their operations, resulting in more efficient and reliable services.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692327</guid>
    </item>
    <item>
      <title>Spot-fare inspection in urban bus transportation systems: strategy and unpredictability under a Stackelberg game approach</title>
      <link>https://trid.trb.org/View/2692326</link>
      <description><![CDATA[This study addresses the operational implementation of a spot-fare inspection strategy on a proof-of-payment urban bus transportation system, where opportunistic passengers can evade fare payment by the most convenient path. The spot-fare inspection strategy defines the frequency at which the transit authority should control sites of the transportation network to inhibit the action of opportunistic passengers. The operational implementation is done using an unpredictable allocation schedule, where the transit authority selects an allocation schedule of n sites to be controlled (one for each inspection team) each day with some probability. The challenge is to determine the set of allocation schedules and their respective probabilities of being selected whose systematic day-to-day application matches the inspection frequencies defined by the spot strategy. The interaction between transit authority and opportunistic passengers is modeled as a Leader–Follower Stackelberg game, where the decision of opportunistic passengers to evade the fare payment and the path to take depends on the passengers’ observations on the inspection frequencies set by the transit authority. We consider that the transit authority implements a vehicle selective inspection policy and an on-board passenger mass inspection policy, with and without interruption of the bus schedule, representing two real approaches to fare inspection.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692326</guid>
    </item>
    <item>
      <title>Evaluating the accessibility impact of integrated micromobility and transit systems: a case study of Calgary, Canada</title>
      <link>https://trid.trb.org/View/2692325</link>
      <description><![CDATA[Electric shared micromobility services, such as scooters and bikes, have recently been introduced in many cities to address primarily first-and-last-mile connectivity needs. However, limited research examines these services’ impact within a multimodal accessibility framework. This study examines the accessibility impact of integrated electric scooter (e-scooter) and public transit services in Calgary, Canada. Using MuTraNG (Multimodal Transportation Network Generator), an online tool we developed based on our proposed multimodal transportation network construction framework, and made publicly available for other researchers, the research integrates e-scooters into the existing transportation network alongside walking and public transit. Two scenarios have been created: one featuring a multimodal service that includes walking and transit and another incorporating e-scooter and transit, designed to access key amenities, including educational institutions, financial services, and healthcare facilities. The findings reveal that e-scooters significantly reduce travel times across the city, particularly in areas distant from the city center and regions underserved by public transit, with travel times to essential services reduced by an average of 6 min. This enhancement in accessibility promotes social and economic inclusion by expanding the area within which residents can access essential services within designated timeframes of 5, 10, and 15 min. While the study highlights the benefits of micromobility in improving urban connectivity, it acknowledges limitations, including the focus on travel time as the sole measure of generalized cost and the assumption of uniform e-scooter availability. Future research should explore additional cost measures and broader impacts of micromobility on traffic congestion, safety, and environmental sustainability. This research provides valuable insights into the first-and-last-mile roles of micromobility and the potential of tools like MuTraNG in fostering more equitable and accessible urban environments.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692325</guid>
    </item>
    <item>
      <title>Improving public transport through machine learning influence flow analysis (MIFA): Southern England bus case study</title>
      <link>https://trid.trb.org/View/2692324</link>
      <description><![CDATA[Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics impact user decisions. Practical implications include deploying smart ticketing systems and contactless payments to streamline bus usage. Our results suggest that these strategies can enable bus operators to allocate resources more effectively, leading to increased ridership and enhanced user satisfaction.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692324</guid>
    </item>
    <item>
      <title>Spatio-temporal analysis of public transportation ridership: leveraging APC data for a comprehensive evaluation of usage rates</title>
      <link>https://trid.trb.org/View/2691800</link>
      <description><![CDATA[The emphasis on the efficient utilization of public transportation resources has become particularly relevant in recent years due to the post-pandemic fluctuation in public transportation usage and the rise in operational costs. The analysis of transportation usage rates provides valuable insights into the efficiency of the service, offering an indicator that integrates actual demand with the capacity. This study aims to develop a methodology for analyzing the occupancy rate from large-scale datasets to identify gaps between supply and demand in public transportation. Leveraging the spatio-temporal granularity of data from Automatic People Counting (APC) systems and relying on the Generalized Linear Mixed Effects Model and the Generalized Mixed-Effect Random Forest, in this study we propose a methodology for analyzing factors determining low occupancy rates. The model’s results are examined at both the segment and ride levels. Initially, the analysis focuses on identifying segments more likely associated with low occupancy rates, understanding factors influencing the probability of having low occupancy rates, and exploring their relationships. Subsequently, the analysis extends to the temporal distribution of low-occupancy-rate situations, encompassing its impact on the entire journey. The proposed methodology is applied to analyze APC data, provided by the company responsible for public transport management in Milan, on a radial route of the surface transportation network.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691800</guid>
    </item>
    <item>
      <title>Time-weighted ensemble long–short-term memory for delay prediction in railway systems</title>
      <link>https://trid.trb.org/View/2689397</link>
      <description><![CDATA[Railway systems are complex and consist of many subsystems working together to ensure smooth and timely operation. Delays that cause deviations from the scheduled operations can lead to cascading issues throughout the railway network. These delays can be a result of a large number of causes, from adverse weather to random equipment failure. Delay prediction is crucial in the mitigation of delay effects. This paper proposes a data-driven approach to the prediction of railway delays. Applying Recursive Neural Networks in this data-driven approach allows it to exploit the sequential nature of train operations without having to make intermediate predictions common in event-driven approaches. Raw scheduling data are processed to compute features relevant to the analysis of delay and its propagation. In the proposed method, each row of data is weighted according to its temporal distance to the prediction horizon, assigning increased importance to more recent events. These time-weighted data are analysed at different scales via the component long–short-term memory (LSTM) models in the proposed ensemble model. Data of a 1-year time period from the Historical Service Records of the British Railway were used to train and test the proposed time-weighted ensemble LSTM architecture. The proposed architecture showed an improved performance compared to other data-driven models implemented as benchmarks, outperforming them by achieving a root mean squared error of 0.27 min, a mean absolute error of 0.17 min as well as a coefficient of determination (R2) value of 0.9875.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689397</guid>
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