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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>Examining System-Wide Bike-Share Ridership in Response to Bikeway Expansion: A Trend-Based Analysis</title>
      <link>https://trid.trb.org/View/2628061</link>
      <description><![CDATA[A bike-share system, also referred to as a public bicycle system or bike-sharing scheme, offers bicycles for short-term use, either for free or at a fee, and has become an increasingly vital component of urban transportation networks. To investigate the impact of bike infrastructure, particularly bike lanes and bike paths, on bike-share demand, this study employed an autoregressive integrated moving average (ARIMA) model with exogenous variables in a longitudinal framework. The results revealed that each additional kilometer of bike lane led to an average increase of 24 daily riders per week and 51 daily riders on weekends. This corresponded to an increase of approximately 38 daily riders per week and 82 on weekends for each additional mile of bike lane. Likewise, adding one bike-share station was associated with an increase of 16 daily riders per week and 34 daily riders on weekends. Active stations had a stronger influence on daily ridership during the week, while the built environment as a whole exerted a greater impact on weekend ridership, highlighting the higher demand for bike-sharing on weekends. Among the weather variables analyzed, temperature and wind speed were not found to affect daily average trip counts significantly. However, precipitation exhibited a significant negative effect on ridership, particularly on weekends. These findings provide valuable insights into the temporal and infrastructure-related dynamics of bike-share usage, offering guidance for planning and evaluating future investments in active transportation systems.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628061</guid>
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    <item>
      <title>Not to travel or how to travel? Understanding weekly commute choices in metropolitan versus rural settings in Australia</title>
      <link>https://trid.trb.org/View/2618079</link>
      <description><![CDATA[This study investigates how individuals allocate their weekly work time across not working, working from home (WFH), and commuting via different transport modes, with a comparative lens on metropolitan and rural settings in Australia. Using survey data collected in late 2022 across New South Wales and Queensland, a multiple discrete continuous extreme value (MDCEV) model is estimated to capture the proportion of weekly time allocated to each work-travel alternative. The model incorporates latent variables representing public transport concern and perceived WFH benefits. Results reveal significant differences between rural and metropolitan behaviours: metropolitan participants are more likely to WFH, use a broader range of transport modes, and are more sensitive to public transport egress time, often penalising it significantly more than in-vehicle time. In contrast, rural participants rely heavily on private vehicles, face more limited access to alternative modes, and are not very sensitive to the perceived benefits of WFH. The findings underscore that WFH tends to substitute car use more than public transport, and that perceived benefits of flexibility and non-commuting significantly influence weekly WFH adoption in metropolitan areas - particularly among younger individuals, caregivers, and women. These results highlight the need for context-sensitive policy: in metropolitan areas, promoting flexible work arrangements and addressing public transport concerns are key to encouraging sustainable commuting, while in rural areas, investment in digital infrastructure, remote work opportunities, and alternative transport options is needed to reduce car dependency. By modelling work and travel as interdependent weekly decisions, this study provides novel insights to inform transport planning and flexible work policies in the post-pandemic context.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:53:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618079</guid>
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    <item>
      <title>Extracting Commuters from Automated Road Traffic Counters: A Gaussian Mixture Approach</title>
      <link>https://trid.trb.org/View/2553115</link>
      <description><![CDATA[Assessing traffic patterns is important for many applications such as rush hour traffic management, cross-border commuting statistics, transportation disruption assessment, and crisis management. The authors present a method for detecting commuting patterns from time-detailed traffic sensor data. The method uses Gaussian mixture models to identify morning peaks that also exhibit expected variation patterns over weekends and holidays as corresponding to commuting. The authors apply the method to detect the variation in commuting between countries in the Nordics during the disruptions caused by the COVID-19 pandemic. Results show that the commuting traffic experienced a smaller decrease (42–71%) than the total traffic (87–92%) during the pandemic. For Finland and Sweden, both types of traffic have in 2023 returned to approximately the same level as before the pandemic, while the traffic between Norway and Sweden has only recovered to about 73% of the pre-pandemic level. The methods can be applied in real-time to provide useful information for applications.]]></description>
      <pubDate>Fri, 17 Oct 2025 16:38:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553115</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>How to improve public environmental health by facilitating metro usage on weekend: Exploring the non-linear and threshold impacts of the built environment</title>
      <link>https://trid.trb.org/View/2470848</link>
      <description><![CDATA[The accelerated motorization has brought a series of environmental concerns and damaged public environmental health by causing severe air and noise pollution. The advocate of urban rail transit system such as metro is effective to reduce the private car dependence and alleviate associated environmental outcomes. Meanwhile, the increased metro usage can also benefit public and individual health by facilitating physical activities such as walking or cycling to the metro station. Therefore, promoting metro usage by discovering the nonlinear associations between the built environment and metro ridership is critical for the government to benefit public health, while most studies ignored the non-linear and threshold effects of built environment on weekend metro usage. Using multi-source datasets in Shanghai, this study applies Gradient Boosting Decision Trees (GBDT), a nonlinear machine learning approach to estimate the non-linear and threshold effects of the built environment on weekend metro ridership. Results show that land use mixture, distance to CBD, number of bus line, employment density and rooftop density are top five most important variables by both relative importance analysis and Shapley additive explanations (SHAP) values. Employment density and distance to city center are top five important variables by feature importance. According to the Partial Dependence Plots (PDPs), every built environment variable shows non-linear impacts on weekend metro ridership, while most of them have certain effective ranges to facilitate the metro usage. Maximum weekend ridership occurs when land use mixture entropy index is less than 0.7, number of bus lines reaches 35, rooftop density reaches 0.25, and number of bus stops reaches 10., Implication: Research findings can not only help government the non-linear and threshold effects of the built environment in planning practice, but also benefit public health by providing practical guidance for policymakers to increase weekend metro usage with station-level built environment optimization.]]></description>
      <pubDate>Tue, 18 Mar 2025 15:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2470848</guid>
    </item>
    <item>
      <title>Exploring spatiotemporal characteristics of ride-hailing ridership connecting with metro stations: A comparative analysis of holidays, weekdays, and weekends</title>
      <link>https://trid.trb.org/View/2485406</link>
      <description><![CDATA[Ride-hailing services offer practical solutions for addressing “first- and last-mile” connectivity challenges at metro stations. While previous research has explored the spatiotemporal patterns of metro station-based ride-hailing ridership (MBRR) on weekdays and weekends, it has largely overlooked the unique dynamics of holiday periods. Furthermore, the influence of the built environment on first-mile MBRR (FM-MBRR) and last-mile MBRR (LM-MBRR) has received insufficient attention. To address these gaps, this study investigates the characteristics of MBRR across regular weekdays, weekends, Valentine's Day, and the Spring Festival. The authors employed ordinary least squares (OLS) and spatial lag regression (SLR) models to analyze the impact of the built environment on MBRR at the station level. Using data from Shenzhen, their findings reveal that: 1) Metro station-based ride-hailing is predominantly used for accessing metro stations, with FM-MBRR consistently exceeding LM-MBRR. 2) The Spring Festival results in a decrease in MBRR, while Valentine's Day exhibits an increase in post-work activity and nighttime MBRR. 3) On Valentine's Day, travel distance positively influences FM-MBRR, reflecting longer ride-hailing trips for holiday-related activities. During the Spring Festival, tourist attractions significantly influence both FM-MBRR and LM-MBRR, highlighting the role of tourism in shaping holiday mobility patterns. These findings provide valuable insights for integrating ride-hailing services with metro systems, emphasizing the need to account for holiday-specific dynamics and local built environment characteristics in urban transportation planning.]]></description>
      <pubDate>Mon, 27 Jan 2025 11:34:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485406</guid>
    </item>
    <item>
      <title>Pedestrians injuries in the north east region of Jamaica: a cross sectional study</title>
      <link>https://trid.trb.org/View/2420246</link>
      <description><![CDATA[To describe the sociodemographic data of injured pedestrians, temporal patterns of injury, injury patterns, and the independent predictors of hospital admission. A two year cross-sectional study was conducted at the Saint Ann’s Bay Regional Hospital in pedestrians with injuries post collision with a motor vehicle. A census was performed in all patients who received either emergency room treatment, hospital admission, or surgical intervention. A 30-item interviewer questionnaire was administered to collect the data. A logical regression model was used to determine independent predictors for hospital admission. Ninety pedestrians were included. Age range: 6-86 years old (Mean=39.9). Males were 63.3%, 75.6% were employed, 31% had a chronic illness and 27% reported marijuana use. Most injuries occurred in April, lowest injury rates occurred in August and September. Twenty two percent of collisions occurred on Saturdays. Most injuries occurred at 5pm and 3pm. Many (54.4%) had a fracture, 73.5% were closed. Approximately 32% had contusions and 6.7% had lacerations. Independent predictors of admission were history of marijuana use and having a fracture. Those with history of marijuana use were 4.21 times more likely to be admitted. Those with fractures were 7.10 times more likely to be admitted. Injury patterns spanned a wide age range. They often involved a high energy mechanism of injury as evidenced by the frequency of fractures, hospital admission and surgery intervention rates. The data also suggests a need to implement marijuana testing programmes in our road users.]]></description>
      <pubDate>Mon, 23 Sep 2024 09:04:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2420246</guid>
    </item>
    <item>
      <title>Revealing representative day-types in transport networks using traffic data clustering</title>
      <link>https://trid.trb.org/View/2419659</link>
      <description><![CDATA[Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.]]></description>
      <pubDate>Fri, 13 Sep 2024 10:35:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2419659</guid>
    </item>
    <item>
      <title>What Lies behind Idle Connection Time in Fast-Charging Public Stations: Evidence from Changshu, China</title>
      <link>https://trid.trb.org/View/2382053</link>
      <description><![CDATA[Understanding charging vehicles, charging stations, and built environment concerning idle connection times significantly guided the management of charging infrastructure. However, the interplay between these factors had remained incompletely understood. This study addressed this gap by investigating public charging stations in Changshu, Suzhou, China, as a case study. The random forest regression and partial dependence plots were employed to explore the nonlinear relationships between idle connection times of vehicles at public fast-charging stations and the built environment, charging stations, and charging vehicles. The exploration encompassed two typical scenarios: workdays and weekends. The findings reveal the distinct influences of various factors in different scenarios. Notably, catering service points of interests in the proximity of charging stations, significantly impact the idle connection time on both workdays and weekends. Furthermore, government groups and residential areas have a notable influence on idle connection times during workdays. Shopping service and Leisure sport have a significant impact on idle connection time during the weekends. Variables such as the charging start time and charged energy also exhibit significant effects. Importantly, these influencing factors demonstrate heterogeneity and exhibit different threshold effects. This research can offer valuable insights to planning authorities and charging facility operators for formulating strategies to enhance charging infrastructure utilization.]]></description>
      <pubDate>Sun, 30 Jun 2024 16:02:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2382053</guid>
    </item>
    <item>
      <title>Guidelines for Activating Ramp Meters during Off-peak Hours and Weekends</title>
      <link>https://trid.trb.org/View/2389245</link>
      <description><![CDATA[Ramp metering is a Transportation Systems Management and Operations (TSM&O) strategy that utilizes signals installed at freeway on-ramps to dynamically manage traffic entering the freeway. Ramp metering signals (RMSs) are usually activated during peak hours to alleviate recurring congestion. However, recurrent congestion during peak hours constitutes less than half of all congestion. It is the non-recurrent congestion, resulting from traffic incidents, work zones, adverse weather conditions, special events, etc., that adversely impacts the performance of the freeway. The primary goal of this research was to develop specific guidelines to activate ramp meters during off-peak hours and on weekends in response to non-recurring congestion. The analysis was based on a 10-mile section of I95 between Ives Dairy Road and NW 62nd Street in Miami-Dade County, Florida. Real-time traffic data were used to develop the guidelines for activating and deactivating RMSs in response to incidents and adverse weather conditions (i.e., rain) during off-peak hours on weekdays. Since the RMSs are not operational on weekends, a microscopic simulation approach was used to develop the guidelines for activating and deactivating RMSs in response to incidents on weekends. The potential benefits of activating RMSs in response to non-recurring congestion during off-peak hours and on weekends were quantified based on the developed guidelines. Findings suggest that activating the first RMS upstream of the incident location could help improve traffic flow conditions during daytime off-peak periods. During nighttime off-peak periods, results indicated that activating the first RMS upstream and downstream of the incident location could help improve traffic conditions upstream. Findings also suggest that activating the RMSs during daytime and nighttime off-peak periods could help improve traffic flow conditions during rain. During weekends, the results indicated that activation of RMSs in response to incidents increased the average speed and also reduced the average delay of vehicles in the roadway network. The developed guidelines were incorporated into a spreadsheet application designed to automatically determine when to activate or deactivate RMSs during off-peak hours and weekends based on prevailing traffic conditions. Recommendations for the guidelines to be included in the the Florida Department of Transportation (FDOT) District Six Standard Operating Guidelines (SOGs) were also provided. The proposed guidelines will enable the FDOT District Six to use ramp metering to improve traffic operations and safety during off-peak hours and weekends.]]></description>
      <pubDate>Mon, 24 Jun 2024 09:22:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389245</guid>
    </item>
    <item>
      <title>Impact of Sunday Trade Ban on Traffic Volumes</title>
      <link>https://trid.trb.org/View/1972729</link>
      <description><![CDATA[The results of the analysis of the impact of the trade ban on Sundays into the daily traffic volumes on weekend days on the road network of the medium Polish city are presented in this paper. The analyzes were carried out on the basis of data collected by the local ITS system, on traffic volumes counted in a continuous mode. Comparison of the results of traffic volume counts for the whole 2018 year, in which every two Sundays per month were indicated as allowed for trade, enabled a large research sample to determine changes in these volumes. The presented data may provide guidance for future activities in the field of planning and organizing road transport on weekend days, as well as enable determination of the impact of trade restrictions on transport pollution and environment.]]></description>
      <pubDate>Wed, 29 May 2024 09:28:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1972729</guid>
    </item>
    <item>
      <title>Evolution and characteristics of shared e-scooters usage in Munich, Germany – Results of an over 8 million trips data analysis</title>
      <link>https://trid.trb.org/View/2348257</link>
      <description><![CDATA[Based on Mobility Data Specification (MDS) data supplied by almost all operating sharing e-scooter companies in Munich, Germany, this study investigates how shared e-scooters have been used in the city. This research examines how the aspects such as the frequency, duration, and distance of travel have changed. Indeed, variations over time, differences by weekday, and developments throughout the day are investigated more closely. Furthermore, the study addresses the effect of temperature and precipitation on the frequency of use across the entire period. The analysis of over 8 million trips during 27 months reveals that since shared e-scooters were introduced in Munich, the number of rides using them has gradually risen yearly. Furthermore, the utilization of the services increases noticeably throughout the summer. According to the investigations, the weather and temperature change significantly impact booking rates. The impact of temperature is directly correlated with the volume of rides. In the case of precipitation, it depends more on whether it is raining at the time or not, while the amount of rain plays a subordinate role. The study shows apparent differences in demand between weekdays and weekends or public holidays: During the week, the number of trips rises earlier, and there is a morning peak. Most journeys occur in the afternoon between 4 and 6 pm. The weekday bookings are generally consistent from Monday through Thursday, slightly higher on Fridays and Saturdays, and again decreasing on Sundays. Another study finding is that the typical journey time has stayed constant over time, ranging between seven and eight minutes.]]></description>
      <pubDate>Mon, 27 May 2024 16:02:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2348257</guid>
    </item>
    <item>
      <title>The Activity-based model ABIT: Modeling 24 hours, 7 days a week</title>
      <link>https://trid.trb.org/View/2348310</link>
      <description><![CDATA[The paper introduces the activity-based model ABIT as a novel approach to modeling travel demand. Traditional aggregate transport models are limited in their ability to assess certain transport policies, such as ride-pooling services and autonomous cars, due to their inability to accurately represent complex travel behaviors. ABIT generates weekly activity patterns for individuals, forming the basis for understanding habitual and incremental travel behavior. Unlike traditional models, ABIT can distinguish between day-to-day variations in travel behavior and more significant year-to-year changes. The model's base-year structure is described in detail, including steps for assigning habitual modes, mandatory activities, discretionary activities, subtours, duration, start times, destination choices, and vehicle allocation. The paper emphasizes the importance of habitual mode choice, especially in modeling commute modes. The results of ABIT show variations in activity frequency across different days of the week, with weekdays dominated by work and education activities, while weekends exhibit a higher proportion of discretionary activities. The paper acknowledges longer runtimes and random variations as potential limitations, suggesting caution in analyzing results at a fine- grained level.]]></description>
      <pubDate>Mon, 13 May 2024 15:45:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2348310</guid>
    </item>
    <item>
      <title>Exploring the impacts of built environment on bike-sharing trips on weekends: The case of Guangzhou</title>
      <link>https://trid.trb.org/View/2361964</link>
      <description><![CDATA[Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas ekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments’ and enterprises’ efforts to improve the cycling environment and ensure the efficient management of shared bikes.]]></description>
      <pubDate>Thu, 18 Apr 2024 17:07:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2361964</guid>
    </item>
    <item>
      <title>Disproportionate impact of weekday-to-weekend transit service cuts on access for disadvantaged populations</title>
      <link>https://trid.trb.org/View/2362384</link>
      <description><![CDATA[This study examines uncertainty in access and access loss by measuring the relative and absolute differences in the number of reachable opportunities when catching public transit with the shortest and the longest wait times. An evaluation of uncertainty in access and access loss on both weekdays and weekends in the Washington Metropolitan Area yields three findings. First, the weekday-to-weekend service reduction disproportionately impacts Black, Millennial, low-income, and carless households. Second, the U.S. capital bears less uncertainty in access but more access loss than its neighboring counties. It is noticed that a quarter of the population resides in areas with high access uncertainty and high access loss; Asians and carless households, respectively, comprise the highest and the lowest shares. Third, there is a negative correlation between uncertainty in access and transit ridership. The findings also echo that transport equity should be approached through an intersectional lens as vulnerable groups often overlap.]]></description>
      <pubDate>Fri, 05 Apr 2024 09:03:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2362384</guid>
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