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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>Gender disparities in travel behaviour and leisure: A Tel-Aviv metropolis time-use study</title>
      <link>https://trid.trb.org/View/2704259</link>
      <description><![CDATA[Achieving gender equality in mobility and leisure is essential to enhancing quality of life and well-being, therefore intersectional gender discrepancies in daily live relating to travel and time use must be addressed. This study examines gender disparities in daily travel behaviour, time spent outside the home, and leisure activities among cultural groups in the Tel Aviv metropolis, using a time-use framework. Leveraging travel survey data (2014–2017) from 24,359 individuals across 12,930 households, the intersection of gender, cultural identity, and socio-economic factors is explored. Results indicate that women, particularly those in caregiving roles or from Arab and Orthodox Jewish communities, face greater mobility constraints and complexities than their male counterparts. Women perform more trip-chaining activities, such as chauffeuring and shopping, but spend on average 1.1 h less outside the home per day than men (6.98 vs. 8.09 h), and make fewer non-home based trips (3.36 vs. 4.33 per day). Notably, higher education and flexible employment correlate with increased mobility and leisure engagement for women, suggesting pathways to mitigate inequalities. The findings underscore the need for intersectional and gender-sensitive urban planning and transportation policies to enhance accessibility, reduce mobility gaps, and promote equitable participation in public life. By addressing these disparities, this research contributes to the broader discourse on gender equity and sustainable urban mobility.]]></description>
      <pubDate>Tue, 30 Jun 2026 08:55:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704259</guid>
    </item>
    <item>
      <title>Do boulevards play a role as pedestrian corridors? Unpacking an urban paradigm with pedestrian count data</title>
      <link>https://trid.trb.org/View/2673214</link>
      <description><![CDATA[Cities increasingly prioritize pedestrian-friendly environments to enhance sustainability and accessibility. Boulevards are often considered iconic pedestrian-friendly streets, balancing vehicular traffic with active transport. However, despite their reputation as key pedestrian corridors, there is limited scientific evidence supporting this notion with actual pedestrian count (PC) data. More broadly, big data technologies remain underutilized in pedestrian monitoring compared to motorized traffic research. This study utilizes longitudinal data from a mobile application in Tel-Aviv, Israel, to examine PC in boulevards compared to other streets across varying temporal contexts. The analysis spans multiple scales- from a city-wide perspective to neighbourhoods and detailed street sections- incorporating geospatial characteristics, such as network centrality, bus stations, trees, lighting, and proximity to destinations. City-scale findings show that boulevards attract more pedestrians on weekdays, with no statistically significant differences observed on weekends in most seasons. Furthermore, the role of boulevards within their neighbourhood varies over time - some function as key pedestrian routes during specific times, e.g., certain seasons or weekdays vs. weekends, while the majority showed no notable difference compared to nearby streets. A detailed examination of PC in selected boulevards and their parallel streets suggests that while boulevard-specific design features—such as medians or enhanced landscaping—may improve walking experience, it does not necessarily translate into increased walking volumes. Streets with basic pedestrian-supportive features can be just as attractive for walking, even without the formal design of a boulevard.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:17:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673214</guid>
    </item>
    <item>
      <title>Pardon me but your e-scooter is in my space: Evaluating the effectiveness of e-scooters parking policies through big data analytics</title>
      <link>https://trid.trb.org/View/2652400</link>
      <description><![CDATA[The growing popularity of shared e-scooters promises to enhance the mobility of urban dwellers in an environmentally friendly manner. On the other hand, e-scooters may potentially pose a risk to other road users, particularly pedestrians, and improperly parked vehicles may hinder accessibility. Therefore, to advance the goals of sustainability and mobility, cities should consider how street space should be managed for all travelers. One policy for shared e-scooter parking control is to limit parking to designated corrals. Corral-based parking policies have been adopted by many municipalities, with limited reports on effectiveness. This study provides an in-depth exploration of such policy deployment, governing the highly utilized services offered in the city of Tel-Aviv. By using our suggested framework and big-data spatio-temporal analytics of e-scooters data, we outline success measures, common pitfalls, and demonstrate city-wide patterns and location-specific results. Our discussion offers policy improvement suggestions, addressing design and policies, including corral-centered monitoring, redistribution policy, resilience estimation, and better space utilization. These could benefit municipalities, operators, and monitoring tools suppliers.]]></description>
      <pubDate>Thu, 26 Mar 2026 16:59:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652400</guid>
    </item>
    <item>
      <title>Pedestrian stress perception in the age of e-scooters: An epistemological and thematic analysis of Tel Aviv</title>
      <link>https://trid.trb.org/View/2589161</link>
      <description><![CDATA[As cities integrate e-scooters in line with the World Economic Forum’s vision for livable, sustainable urban environments, pedestrians are increasingly confronting new challenges in shared spaces. This study takes a multidisciplinary perspective and an epistemological approach to explore the cognitive and emotional aspects of pedestrian experiences with e-scooter traffic. By using stress perception as a key measure, the focus shifts from traditional safety concerns to wider considerations of emotional well-being in urban mobility. This research may help shape more inclusive, accessible, and pedestrian-friendly urban active mobility systems.Prompted by concerns from the Tel Aviv Municipality and the Israeli National Road Safety Authority, 192 survey responses were analyzed. Findings show that 70% of participants consider pedestrian separation and sidewalk width important, highlighting challenges in navigating mixed-use paths. About 60% cited micromobility paths and road traffic as stressors, often seen as beyond pedestrian control. Although less immediate than factors like shade or air pollution, these elements impact walking experiences. Older participants emphasized accessibility, and women reported increased stress in crowded areas. Preferred solutions include wider, shaded, and well-lit sidewalks, dedicated micromobility lanes, and policies aimed at improving safety and easing pedestrian stress.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589161</guid>
    </item>
    <item>
      <title>The gender travel gap: Exploring intersectional socio-demographic and gender differences in travel in Tel-Aviv through semiparametric mixed model analysis</title>
      <link>https://trid.trb.org/View/2531132</link>
      <description><![CDATA[After conditioning on salient socio-economic attributes such education, employment, income, and household structure, the consensus in the literature is that inherent gender differences persist as an underlying driver of mobility. Females continue to have a lower degree of mobility compared to males when drawing on evidence from studies predominately undertaken in developed countries in the global West. The aim of this study is to contribute to the relatively scarce literature on gender differences in travel in the global East. The authors perform semiparametric regression analyses to quantify the impact of gender differences in travel in the metropolitan region of Tel-Aviv, Israel using data from 2014–2017. The authors additionally investigate the intersection of gender and ethnic/religious identification as the data identifies Jewish and Arab populations. In line with the existing literature, the authors find that females travel less frequently and have shorter travel times and distances compared to males after accounting for 23 different socio-demographic, travel preference, and temporal variables. Moreover, the authors observe additional mobility penalties for females who are married and have children and for females who identify as Arab. To increase female mobility, the authors recommend policies to promote higher education which boosts female mobility and policies to reduce the home-based care-taking burden on females such as investment in childcare infrastructure and services. Specifically for Arab females, the authors additionally recommend policies to improve access to local job opportunities and as well as improving transport accessibility and connectivity in Arab sector zones in Israel.]]></description>
      <pubDate>Tue, 29 Apr 2025 16:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2531132</guid>
    </item>
    <item>
      <title>Optimization of mobility incentives in electric vehicle car sharing systems: A reinforcement learning framework</title>
      <link>https://trid.trb.org/View/2491775</link>
      <description><![CDATA[This work introduces a novel reinforcement learning (RL) framework for optimizing mobility incentives in free-floating electric vehicle carsharing systems. User participation in relocation and rebalancing activities is facilitated by a centralized control agent that assigns dynamic and space-heterogeneous tariff discounts and mobility credits. These mobility incentives can adapt to various operational conditions and are learned through direct interaction with a new simulation environment. This environment integrates a probabilistic demand model, historical reservation data, and a transportation mode choice model, and can simulate reservations, relocation, and charging activities affected by the incentives. The efficacy of the RL framework is demonstrated on a simulated fleet in Tel Aviv, and using real-world data from the AutoTel company. Four RL agents are trained via standard PPO, TD3, DDPG, and SAC algorithms designed to handle continuous action spaces. Efficacy is evaluated against baseline relocation strategies (crew-based and hybrid relocation) and the results demonstrate higher revenues and lower charging and relocation costs achieved by the RL agents. To test the scalability of the approach, two observation spaces of increasing dimensionality are investigated. Overall, this study highlights the potential of RL-based mobility incentivization strategies, offering benefits for transportation and energy grid operators. A powerful tool that can support sustainability and electrified carsharing operations.]]></description>
      <pubDate>Mon, 24 Feb 2025 12:01:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491775</guid>
    </item>
    <item>
      <title>Comparative analysis of pedestrian volume models: Agent-based models, machine learning methods and multiple regression analysis</title>
      <link>https://trid.trb.org/View/2487829</link>
      <description><![CDATA[Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families – multiple regression analyses, machine learning models, and agent-based models – in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods – especially in neighborhoods that did not develop in a self-organized process. Regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, the results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:56:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487829</guid>
    </item>
    <item>
      <title>Pedestrian Movement and the Built Environment – A Big Data-Based Analysis</title>
      <link>https://trid.trb.org/View/2407251</link>
      <description><![CDATA[Over the years, the urban planning literature has focused substantial attention on walkability research, aiming to enhance physical activity and healthy communities in the city through urban planning and design. However, while motorized traffic research seems to gain momentum in combining innovative technologies for traffic monitoring in recent years, pedestrian research seems to be left behind; the common tools used to collect pedestrian data are limited in time and scale. The increasing availability of quantitative data on the built environment holds great potential for a new generation of walkability studies, based on direct evidence of pedestrians’ flow around the city. This study aims at scrutinizing the added value of big data and crowdsourced big data to pedestrian and walkability research while experimenting with a new emerging technology of Bluetooth sensors. The authors used a dataset of over 53 million pedestrian records, monitored in 83 street-segments in Tel-Aviv, Israel, to analyze tempo-spatial dynamics of pedestrian movement at the street-level. The data was collected 24/7 for five months, including the time of COVID-19’s first lockdown. The results provide new insights on the relationship between attributes of the built environment and pedestrian movement, while identifying and evaluating attractive street segments across temporal changes. The authors discuss the role of street characteristics as determinants of walking, the impact of policy decisions on walking behavior and the possible implications of crowdsourced big data as tools for supporting planning decisions.]]></description>
      <pubDate>Thu, 26 Dec 2024 15:04:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407251</guid>
    </item>
    <item>
      <title>Understanding Group Social Ties and Their Impact on Travel Behaviour</title>
      <link>https://trid.trb.org/View/2407238</link>
      <description><![CDATA[Over the past decade, there has been a growing interest in understanding the impact of social ties on travel. Some of the questions raised are: What are the frequencies and scale of these rides? What are the means of transport taken? Furthermore, what social network parameters impact travel behaviour? While all recent studies are based on Wellman’s “Network Individualism” theory, this study aims to extend the theoretical framework by focusing on groups and their travel behaviour.The study introduced Scott Feld’s Foci theory and Hägerstrand’s time-space prism. According to Feld, group activities are based in relation to a physical or temporal focus; hence individuals are connected via events, locations, or activities. On the other hand, the time-space prism indicates the constraints under which activities are performed. Based on the two theories, the study focused on the fragment of group meetings: how they are formed and how their formation relates to socio-demographic parameters and the city’s structure. Data was gathered via online questionnaires, which traced the travel behaviour patterns of groups within the city of Tel Aviv. Meeting locations were mapped, and a measurement of the social tie strength amongst the meeting members was constructed. The study uses multivariate regression analysis to reveal the hidden variables affecting travel choices for a group meeting.]]></description>
      <pubDate>Thu, 26 Dec 2024 15:04:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407238</guid>
    </item>
    <item>
      <title>Ready, set, scoot! Investigating implicit attitudes toward risky e-scooter riding situations: A go/no-go association task study</title>
      <link>https://trid.trb.org/View/2463853</link>
      <description><![CDATA[E-scooters are a popular intercity mode of micro-mobility, with usage steadily rising in Tel-Aviv and other cities globally. Despite the increasing e-scooter use, there exists a gap in understanding the attitudes of these vulnerable road-users toward the associated risks. This study aimed to explore e-scooters’ implicit and explicit attitudes toward the risks associated with e-scooter riding at different city locations in Israel. Two experiments were conducted toward this goal. The preliminary study involved developing a customized Go/No-go Association Task (GNAT) tool, utilizing real-world scenarios. Forty-six participants briefly observed pictures of different e-scooter riding situations and rated their riskiness level on a Likert scale. Overall, riding on designated trails was perceived as safer than sidewalks or roads, except during phone conversations. Roads and sidewalks were perceived differently regarding riskiness level only during phone use with headphones or navigation. Neglecting to wear a helmet was perceived as risky. The GNAT tool proved valuable in assessing implicit attitudes. In the main study, sixty-four participants completed tasks assessing implicit and explicit attitudes toward risky riding. Self-described more cautious riders demonstrated more accurate responses to risky situations under negative than under positive priming, showing more favorable norms than less cautious riders. For positive priming and risky blocks, there was a positive effect of explicit attitudes on the probability of correctly identifying risky situations, suggesting that less cautious riders demonstrate more favorable norms only under positive priming. Understanding e-scooter riders’ attitudes may contribute to producing interventions that reduce risk-taking tendencies. Implications for licensing and policy recommendations are discussed.]]></description>
      <pubDate>Thu, 12 Dec 2024 16:58:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2463853</guid>
    </item>
    <item>
      <title>Towards a Sustainable City Policy for Managing Shared Dockless E-Scooters</title>
      <link>https://trid.trb.org/View/2421407</link>
      <description><![CDATA[Based on the anonymized dataset of one million shared scooter rides collected by the Tel Aviv municipality, the authors investigate the behavior of the shared e-scooter users in Tel Aviv and the resulting dynamics of the scooters’ spatio-temporal patterns. The users’ choice of the shared e-scooters as a transportation mode follows fast and frugal heuristics: the user who activated the shared scooter’s application continues with the ride when the closest available scooter is close enough, at up to 50-100 meters from her. Riders strongly prefer bike paths for riding and essentially deviate from the shortest path between the origin and destination and choose the routes with the higher percentage of bike paths. The longer the route, the higher the fraction of the bike paths in the rider’s route and the difference between the shortest and chosen paths. The authors also demonstrate that the operators fail to match the supply to the demand and the scooters are oversupplied in the central part of the city, where the demand is high, and undersupplied in the rest of the city, where the demand is essentially lower. Based on the analysis the authors propose a new policy of spatial adjustment between the demand and supply.]]></description>
      <pubDate>Mon, 09 Sep 2024 15:16:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2421407</guid>
    </item>
    <item>
      <title>Decomposing PM₂.₅ concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions</title>
      <link>https://trid.trb.org/View/2394257</link>
      <description><![CDATA[Vehicle-emitted fine particulate matter (PM₂.₅) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM₂.₅ using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOₓ) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012–2019) PM₂.₅ observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM₂.₅ species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM₂.₅ concentrations at an AQM station from the first location showed close agreement (R²=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM₂.₅ species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7–11 % of the observed PM₂.₅ in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60–78 % of the traffic-related PM₂.₅ concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM₂.₅ speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:40:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2394257</guid>
    </item>
    <item>
      <title>How far will you go? From empirical findings to formalization of walking route distances</title>
      <link>https://trid.trb.org/View/2378503</link>
      <description><![CDATA[Empirically based theorization of walking range patterns is rather limited, leading researchers and planners to rely on simplistic assumptions as to the typical distance and duration that pedestrians may walk. Using high-resolution GPS data collected from over 11,000 participants in the Tel-Aviv metropolitan area, the authors provide an empirical estimate for the distribution of walking route distance and duration, while examining potential factors that may affect it. In addition, the authors develop a general analytical framework that describes walking route patterns. The authors’ results show that the average route distance and duration in Tel-Aviv metropolitan is 630 m and 7.9 min. Factors associated with walking range include socio-demographic characteristics of walkers (age-group, socioeconomic status and number of cars in a household) and city characteristics (longer routes in cities with a larger population and in areas with high density of street intersections). The authors’ main finding is that walking route distance distribution can be best described using the theoretical log-normal distribution and can be characterized using its mean-log and SD-log parameters. The log-normal parameters make an analytical framework that enables the evaluation of differences in walking patterns between places and identification of where interventions are required to promote active travel. The authors explain why the log-normal distribution is likely to be suitable to other cases worldwide.]]></description>
      <pubDate>Thu, 30 May 2024 14:00:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2378503</guid>
    </item>
    <item>
      <title>Tempo-spatial analysis of pedestrian movement in the built environment based on crowdsourced big data</title>
      <link>https://trid.trb.org/View/2351051</link>
      <description><![CDATA[Over the years, the urban planning literature has focused substantial attention on walkability research, aiming to enhance physical activity and sustainable communities through urban planning and design. While motorized traffic research has gained momentum in combining innovative technologies for traffic monitoring, the common tools used to monitor pedestrian movement (PM) to this date are limited in time and scale. This study aims at analyzing tempo-spatial dynamics of walking behavior, while utilizing an emerging technology of Bluetooth sensors. The authors analyzed over 53 million pedestrian records, monitored in 83 street-segments in Tel-Aviv, Israel. The data was recorded for five months, including the time of COVID-19's first lockdown. They showcase tempo-spatial dynamics of PM through different times of the year, while discussing changes in walking traffic volume, walking patterns (frequency peaks), traffic at commercial vs. residential streets, popular street segments and land-uses, and possible applications to urban planning and design. Finally, the authors discuss the data limitations and challenges for PM monitoring and research. The study shows that BT sensor technology can provide the municipality and decisionmakers with insights on pedestrians' behavior and preferences in real-time and at street-level, enabling to locate infrastructure investments more efficiently and to support planning decisions. The preliminary results of this study suggest further use of this technology for pedestrian movement monitoring and research in urban networks.]]></description>
      <pubDate>Tue, 23 Apr 2024 10:49:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2351051</guid>
    </item>
    <item>
      <title>Shade maps for prioritizing municipal microclimatic action in hot climates: Learning from Tel Aviv-Yafo</title>
      <link>https://trid.trb.org/View/2292058</link>
      <description><![CDATA[This article presents a methodology for evaluating microclimatic summer conditions across an entire city, focusing on the provision of outdoor shade as a primary comfort indicator. Based on high-resolution 2.5D and 3D mapping of buildings, ground surfaces, and tree canopies in Tel Aviv-Yafo, a city of hot-summer Mediterranean climate, the authors employed a detailed calculation of solar exposure of streets and open spaces (public and private) using commonly available GIS algorithms. The raw results of these calculations were used for calculating summer Shade Index values for every street segment and neighbourhood of the city, which were then plotted to a comprehensive 'shade maps' reflecting the city's spatial hierarchy of shade. The shade maps, combined with analysis of tree canopy cover on similar scales, enabled to relate building and tree morphologies to outdoor shade conditions. For prioritizing intervention of local planning authorities in improving poor shade conditions or conserving highly-shaded locations, the authors related the climatic analysis to space-syntax classification of streets according to their potential to attract pedestrian movement, and produced a city-wide map that highlighted streets that have high pedestrian movement potential while requiring high levels of shade intensification or conservation.]]></description>
      <pubDate>Thu, 30 Nov 2023 10:47:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2292058</guid>
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