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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Evaluating the Cumulative Impact of Environmental Conditions on Stress Levels in Micromobility Users: An AI-Driven Multimodal Approach</title>
      <link>https://rip.trb.org/View/2652172</link>
      <description><![CDATA[Micromobility solutions, such as e-scooters and bicycles, are increasingly utilized in urban transportation, providing flexible and sustainable mobility options. However, micromobility users face significant exposure to environmental stressors, including air pollutants emitted by motorized traffic. While prior studies have explored the physiological effects of transportation emissions, the psychological impacts, particularly stress, remain underexplored. This study aims to bridge this gap by developing an AI-driven predictive model that evaluates the cumulative impact of transportation-related air pollutants on stress levels in micromobility users. By integrating wearable sensor data (e.g., electrodermal activity, heart rate variability, and skin temperature), air pollutant concentration data (e.g., PM2.5, NOx, and CO), and spatial context data (e.g., GPS and accelerometer readings), this research will leverage Temporal Fusion Transformer (TFT) models to predict real-time stress levels and generate stress heatmaps. The results will inform policymakers, transportation planners, and public health officials, contributing to more sustainable and inclusive urban transportation systems. Additionally, the project will provide hands-on research opportunities for students, fostering workforce development in AI-driven transportation health studies. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:55:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652172</guid>
    </item>
    <item>
      <title>Pedestrian Route Choice and Traffic Exposures: A Case Study in Downtown Atlanta</title>
      <link>https://rip.trb.org/View/2652174</link>
      <description><![CDATA[Walkable neighborhoods and active mobility provide human health benefits through exercise, stress relief,  and local sustainability, but concerns over near-roadway traffic-related air pollution remain significant (Luo, 2018). Exposure to pollutant concentration exposure assessments downwind from roadway sources not only undermines the health advantages of walking, but also poses economic burdens, as indicated by the substantial costs associated with air pollution-related mortality (World Bank, 2020). Recent studies are increasingly focused on understanding how pedestrian route choice influences exposure to traffic related air pollutants (Khreis, 2020), and integrating these exposure metrics into pedestrian route planning may improve health outcomes. Shortest path, impedance-based routing tools, such as SidewalkSim, can be used to predict pedestrian paths through the sidewalk network. Route selection can account for pedestrian asset design and condition, and even route wheelchair users around crossings that are missing a curb ramp (which imposes a significant impedance on the crossing link). When second-by-second pedestrian route data are combined with spatiotemporal predictions of pollutant concentrations downwind from roadways, analysts can assess how exposure accumulates over the course of each walking trip. Because the routing tools are impedance-based, pollutant concentrations can be converted to link impedance and potentially used to route pedestrians on slightly longer routes that result in much lower pollutant exposure. This project will apply impedance methods in the context of pedestrian travel in downtown Atlanta, testing a variety of impedance functions from the assessments to evaluate the tradeoffs between route circuity and pollutant exposure across these functions. The project will develop a framework that supports healthier, lower-exposure pedestrian pathways (while maintaining reasonable routes and convenience for all users). ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:38:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652174</guid>
    </item>
    <item>
      <title>Pedestrian Level of Traffic Stress (PLTS) Validation for Pedestrians with a range of Ages and Abilities</title>
      <link>https://rip.trb.org/View/2625597</link>
      <description><![CDATA[Pedestrian Level of Traffic Stress (PLTS) is a safety tool that can be used to map the most and least pedestrian-friendly parts of an entire roadway network, recommend comfortable walking routes, help prioritize locations for infrastructure improvements, and evaluate project- and system-level changes in pedestrian accommodations over time. However, existing methods of evaluating pedestrian traffic stress (Landis et al. 2001, Chu & Baltes 2001, Raad & Burke 2018) are not standardized and often require many inputs that are impractical for agencies to apply. During the first two years of CPBS grants, the UW-Milwaukee research team attempted to address these issues by creating and testing a new Pedestrian Level of Traffic Stress (PLTS) method. The PLTS provides ratings from 1 to 4 (lowest to highest stress) to assess how pedestrians are likely to feel around vehicular traffic when crossing or traveling along specific roadway segments. The PLTS ratings are based on look-up tables with a relatively small number of inputs (e.g., number of lanes, traffic volume, speed limit, sidewalks and buffers, pedestrian crossing facilities, curb ramps), many of which are readily available in agency data. The PLTS builds on other recent table-based PLTS methods (Oregon Department of Transportation 2020, Washington State Department of Transportation 2020, Montgomery County 2020, Richardson 2023), but it is the first PLTS that the research team is aware of to undergo rigorous validation testing. During the Year 2 project, applied the PLTS method to different types of roadway corridors in three case study communities and compared our PLTS ratings with stress levels reported by online survey respondents. Still, online survey videos and pictures do not fully reproduce conditions that pedestrians experience on actual roadways. Further, the online format with videos and pictures was not accessible to people with visual disabilities, and our sample only captured a small number of older adults and people with other types of disabilities. Therefore, the Year 3 research will build on our current work by comparing the research team's PLTS ratings with PLTS ratings gathered from pedestrians who are older adults or who have sensory or mobility limitations at a series of real-time PLTS data collection events in three communities. This feedback will also inform whether (and if so, which) additional variables should be incorporated into PLTS ratings to better account for suitability among these groups.  The goal is to establish a validated, practical PLTS method that agencies across the country can use to estimate roadway segment and crossing suitability for pedestrians in various contexts, ultimately leading to safer and more enjoyable walking and rolling conditions. This Year 3 CPBS project will help improve understanding of PLTS for pedestrians with a wider range of ages and abilities.]]></description>
      <pubDate>Mon, 17 Nov 2025 14:33:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625597</guid>
    </item>
    <item>
      <title>Road User-Automated Vehicle Expectancies, Interactions, and Safety</title>
      <link>https://rip.trb.org/View/2425405</link>
      <description><![CDATA[Automated vehicles (AVs) have the potential to enable a safe, efficient, equitable, healthy, and sustainable transportation system and communities. However, broad public adoption of AVs is predicated on the AVs’ ability to engage in safe and efficient interactions with other road users: conventional human-driven vehicles (HVs), pedestrians, and bicyclists, in our current infrastructure and traffic systems. Human road users have certain expectancies for how other road users behave and interact accordingly. While humans can anticipate and handle a range of other road users’ behaviors, unexpected behaviors that fall outside or in tail end of the range, i.e., expectancy violations (EVs), can incite improper responses that could have ramifications for traffic safety and operations. Obviously, significant challenges for AV technology remain in coexisting in harmony with human road users, beyond its own proper functionality. The objective of this research is to do the foundational research to prepare to study interactions between human-driven vehicles, pedestrians, and automated vehicles to elucidate potential expectancy violations and consequent impact on safety. Eventually in a later phase of the project, systematic human-in-the-loop (HIL) experiments simulating AV-HV-Pedestrian and AV-HV interactions will be conducted to probe expectancies, expectancy violations, and safety impacts.
]]></description>
      <pubDate>Thu, 05 Sep 2024 17:04:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425405</guid>
    </item>
    <item>
      <title>Refining C3 Context-classification Criteria for Low-income and Minority Populations</title>
      <link>https://rip.trb.org/View/2404176</link>
      <description><![CDATA[This study seeks to refine the guidance for C3 corridors to account for the unique vulnerabilities of lower-income and minority populations. The three primary objectives include 1. The identification of contextual factors that may place lower-income pedestrians and bicyclists at increased risk along C3 corridors. 2. Estimation of the relative effects that specific features of C3 corridors have on crash incidence, thereby allowing for the prioritization of safety-related countermeasures. 3. The identification of threshold values that may serve to trigger a reconsideration of design treatments along C3 corridors.]]></description>
      <pubDate>Thu, 18 Jul 2024 11:12:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2404176</guid>
    </item>
    <item>
      <title>Assessing Pedestrian Sight Distance for Crossing Decisions

</title>
      <link>https://rip.trb.org/View/2381747</link>
      <description><![CDATA[A pedestrian can safely cross a street only if one of two conditions is met: either a vehicle comes to a full stop for the pedestrian or an adequate gap in traffic occurs that allows the pedestrian to cross without conflict. To assess such gaps, a pedestrian needs adequate visibility to make the decision.

Currently, evaluating sight distance for crosswalks is typically done from the perspective of a driver’s ability to stop for a pedestrian using stopping sight distance (SSD). However, limited guidance exists on how to evaluate pedestrian decision sight distance. Pedestrian sight distance can be assessed by modifying methodologies from the American Association of State Highway and Transportation Officials’ (AASHTO) A Policy on Geometric Design of Highways and Streets (hereafter the AASHTO Green Book) or Guide for the Development of Bicycle Facilities. However, criteria are not specified to determine the pedestrian crossing time in certain cases, such as Case B3 outlined in the AASHTO Green Book.

Research is needed to establish procedures and methodologies to support state departments of transportation in assessing and ensuring adequate pedestrian decision sight distance during the project development process.

The objective of this project is to develop a framework for assessing sight distance for pedestrian crossings across various roadway contexts and pedestrian types.
 ]]></description>
      <pubDate>Thu, 23 May 2024 10:06:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381747</guid>
    </item>
    <item>
      <title>BikePed Portal: Pedestrian Volume Estimation Based on Push Button Actuations from Signals Data</title>
      <link>https://rip.trb.org/View/2361978</link>
      <description><![CDATA[This project translates research from Oregon DOT's "Active transportation counts from existing on-street signal and detection infrastructure" (SPR 857), into a practical application on BikePed Portal. ]]></description>
      <pubDate>Tue, 02 Apr 2024 13:42:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2361978</guid>
    </item>
    <item>
      <title>Comprehensive Analysis of Factors Influencing Pedestrian Injury Severity at Intersection and Non-intersection Locations in Connecticut </title>
      <link>https://rip.trb.org/View/2321729</link>
      <description><![CDATA[Pedestrian safety is a growing concern in the United States, with 7,500 fatalities reported in 2022, marking the highest in three decades. Connecticut followed this trend, recording 69 fatalities in the same year. This study examined factors influencing pedestrian injury severity through a multi-level statistical analysis using Connecticut crash data, NHTSA’s VIN decoder, and Canadian Vehicle Specification data.


Crashes were classified into two categories: intersection and non-intersection, and pedestrian injuries were categorized into three: severe (Fatal/K, serious/A), non-severe (Evident/B, Possible/C), or no-injury/property-damage-only (O). Separate multinomial logistic regression models were developed to identify the factors influencing pedestrian injury severity, and binary logistic regression models were developed to compare fatal and serious injuries, providing a deeper analysis of severe injury outcomes.


At non-intersection locations, pedestrian impairment (OR=3.57), driver speeding (2.85), improper crossing (2.84), driver impairment (1.88), and unlighted roadways (1.55) significantly increased the odds of severe injury. At intersections, pedestrian impairment (4.53), speeding (7.40), roadway downgrade (2.04), and unlighted conditions (1.48) were key contributors.
Binary logistic models revealed, at non-intersections, pedestrian age (3% per year), pedestrian impairment (2.03), driver impairment (1.91), and roadway upgradient (3.18) significantly increased the risks of a fatal injury versus a serious injury. At intersections, speeding (7.39) was especially critical, while passive (0.20) and active (0.61) traffic control devices substantially reduced the risk of fatal injury.


The findings provide detailed, context-specific insights to guide pedestrian safety strategies. Reducing pedestrian impairment, enforcing speed control measures, improving roadway lighting, and implementing effective traffic control devices, particularly at intersections, can substantially reduce the likelihood of pedestrian injury severity.]]></description>
      <pubDate>Tue, 16 Jan 2024 12:31:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321729</guid>
    </item>
    <item>
      <title>Safety in Connected Automated Vehicles in the Presence of Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2292659</link>
      <description><![CDATA[Automated Vehicles (AV’s) can intermingle with pedestrians and cyclists when they are driving slowly in so-called “shared spaces”.  Smaller “people movers” traveling in pedestrian-dense urban areas fall in this category. However, at higher speeds on open roadways, AV’s need to be aware of the possibility of emergence of occluded pedestrians or cyclists. In such situations, the pedestrians and cyclists are referred to as Vulnerable Road Users (VRU’s).   In their previous work, the research team studied AVs' energy consumption and safety when occluded pedestrians appear suddenly in front of the AV. The research team also indicated that an entropy-based metric may be used to quantify the value of the information regarding the location of the emergent pedestrian or cyclist. In a separate work the research team investigated a risk-based control strategy when the probability distribution of emergent pedestrians is known.  In yet another study, the research team investigated the effect of adding a sensor to the roadside infrastructure to provide information to an oncoming vehicle, regarding a crossing pedestrian around the corner. The team labeled this the Extended Sensor.  They will continue investigating and developing their "value of information" based approach to evaluate additional sensors in the infrastructure. The research team shall consider regular intersections and will initiate a study on specific configurations.  The team will initiate a study on "indecisive pedestrians". These will be pedestrians who may stop or turn back while crossing the street, depending on their assesment of the approaching vehicle. The research team will assume that the vehicle will also make a decision on stopping, continuing and/or dodging the pedestrian.  The research team will study a pedestrian crossing two lanes of traffic, with different direction traffic flow. The research team shall model the pedestrian dynamics for a direct two-lane crossing vs a crossing attempt with a wait stage in between the lanes.]]></description>
      <pubDate>Tue, 21 Nov 2023 19:02:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292659</guid>
    </item>
    <item>
      <title>PedPal Lite: An ATSC-Independent Safe Intersection Crossing App</title>
      <link>https://rip.trb.org/View/2292660</link>
      <description><![CDATA[PedPal is a smartphone app designed to assist pedestrians with disabilities in safely crossing signalized intersections, developed originally as part of the Federal Highway Administration’s Accessible Transportation Technology Research Initiative (ATTRI) [1,2].   PedPal interacts directly with the surtrac adaptive traffic signal control (ATSC) system operating at the intersection using real-time traveler-to-infrastructure (T2I) communication and standard DSRC messaging to provide crossing support to its user.  Upon arrival at the intersection, PedPal receives and presents information to its user about the intersection’s geometry, crossing options, and current traffic signal state. When the user indicates her crossing intent, the app then communicates this information to the intersection (eliminating the need to locate and push a pedestrian call button), along with how much time is required by the user to safely cross the intersection. In response to receiving this information, the traffic signal system will set the pedestrian crossing time in the desired direction to ensure that upon getting the crossing signal, the user will receive crossing time that has been requested. More advanced PedPal capabilities include the ability to monitor user crossing progress in real-time, to recognize when the user is traveling slower than expected, and to trigger the traffic control system to dynamically extend the crossing time in such circumstances. The PedPal app is integrated with the smartphone's native accessible features and provides visual, auditory and haptic interaction modalities.   This project focuses on producing a cheaper and more broadly deployable version of PedPal. Whereas the ability exploit surtrac’s real-time ATSC capabilities enable advanced capabilities such as dynamic extension of the current phase duration that enhance safety, its deployment cost to municipalities presents a significant barrier to widespread deployment of the PedPal technology. Furthermore, a recent UTC funded project centered on technology support for the 'complete trip' has expanded the scope of PedPal's capabilities in several new safety-related directions, none of which depend on interaction with surtrac.  To foster more widespread deployment of the PedPal technology, this project will develop and pilot test a stand-alone version of PedPal (referred to as ‘PedPal-Lite’) that will interact directly with the hardware controller at the intersection via an ATSC-independent PedPal intersection manager. This manager will take over responsibility from the Surtrac ATSC system both for broadcasting information about the intersection and the current traffic control state to the smartphone app and for interacting with the traffic controller in response to messages received from the app, exploiting the same underlying T2I connectivity. The manager will run on a low-end processor residing in the cabinet at the intersection and will take advantage of the V2I-hub software module developed under sponsorship of FHWA to generate DSRC formatted messages for broadcast to PedPal users. To maximize deployment potential, the research team will focus integrating the PedPal intersection manager with controllers that support standard NTCIP interaction protocols.  The research team will demonstrate and pilot test the developed PedPal-Lite variant on a TBD intersection near the CMU campus that is running a conventional fixed signal timing plan on a hardware controller that supports the NTCIP standard.]]></description>
      <pubDate>Tue, 21 Nov 2023 18:56:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292660</guid>
    </item>
    <item>
      <title>Intersection Safety for the Vulnerable</title>
      <link>https://rip.trb.org/View/2292661</link>
      <description><![CDATA[Vulnerable road users are considered people that are not in a vehicle and are, consequently, at a higher risk for serious injury because they have less crash protection than a vehicle occupant. Pedestrians, bicyclists, motorcyclists, and road workers are common vulnerable road users. Vulnerable road users can be further categorized by their degree of mobility, perception, and cognition. Shown in Figure 1 are numerous examples for each of the vulnerability categories. Vulnerabilities also have spatial and temporal dependencies, which can be quantified by a vulnerability index that might range from very low risk to very high risk (Figure 2). For example, running on a sidewalk in the middle of the day has an associated very low index. However, jaywalking across the road during rush hour might have a high or very high vulnerability index.  The goal of the proposed work is to enhance the safety of the vulnerable at intersections because these are locations of planned conflict and thus have an inherent risk. To accomplish this goal, the research team proposes a cyber-physical system (Figure 3) that detects vulnerable road users, calculates a vulnerability index, then takes an appropriate action or actions to minimize the opportunity for injury. For example, if a person falls out of their wheelchair in the middle of a signalized intersection, all of the traffic signals would stay red, emergency medical vehicles would be dispatched, and audiovisual warnings would be broadcast. Warnings could also be sent via wireless communication to personal devices and even connected autonomous vehicles.  The core of the system is based on detecting the vulnerable in visual data captured from cameras. Accomplishing this task requires an annotated dataset of people with vulnerabilities, e.g., walking cane, bicycle, etc. There are some public datasets available with annotated wheelchair users for example, but not nearly enough vulnerable road users are available. To fill the dataset gap, the research team will deploy cameras in areas with expected high vulnerable activity. If the team is unable to capture enough image examples, they will augment the dataset with synthetic images (e.g., project a 3D model of a man using crutches into an image) and/or perform enactments. Then the research team will train models for detecting the vulnerable, develop a method for calculating a vulnerability index, and develop a warning system.   The research team have a longstanding deployment partnership with the City of Pittsburgh Department of Mobility and Infrastructure (DOMI). DOMI has already approved a camera deployment at the intersection of Forbes and Morewood. The research team also has a relationship with Easterseals Massachusetts in an advisory capacity for issues related to people with vulnerabilities.]]></description>
      <pubDate>Tue, 21 Nov 2023 18:52:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292661</guid>
    </item>
    <item>
      <title>Evaluating Autonomous Vehicles’ Safety Benefits in Mixed Autonomy Scenarios</title>
      <link>https://rip.trb.org/View/2292640</link>
      <description><![CDATA[Connected autonomous vehicles (CAVs) are gradually advancing towards widespread deployments. CAVs promise to improve transportation safety by operating more efficiently and avoiding incidents like crashes due to human driver error. However, they may cause incidents themselves, especially when interacting with humans. The goal of this project is to evaluate the potential safety benefits of CAVs in mixed-autonomy settings, in which CAVs and human vehicles share the road. This work has three parts: (i) estimating the effective incident rates of CAVs and how they are distributed across a city, leading to algorithms for prioritizing incident responses so as to reduce their overall impact on traffic flow and safety; (ii) incorporating CAVs’ and human drivers’ ability to react to human pedestrians, leading to algorithms for CAVs to reduce pedestrians’ impact; and (iii) evaluating models and analysis in a mixed-autonomy simulator.  Towards modeling CAVs’ effect on traffic incident rates, the research team will account for the fact that vehicle incident rates vary with the road congestion level and type, e.g., Pennsylvania data show that incidents are more common in heavy-traffic surface streets than sparsely populated highways. The team will build on their prior Mobility21 work studying mixed-autonomy traffic patterns to account for changes in congestion levels across the road network due to vehicle incidents, e.g., if CAVs overall reduce the incident rate on highways, this might lead to better overall traffic flow and fewer subsequent incidents. The results will enable prioritization of incident response so as to maximally reduce the resulting traffic congestion.  The team will then incorporate the effects of human pedestrians into their mixed-autonomy setting. Pedestrians can change safety dynamics as their actions may be more difficult to predict, especially for CAVs that may not be well-trained on pedestrian data. For example, CAVs can improve traffic flow by more closely following other vehicles; this is less feasible when human pedestrians are present. The team therefore plans to incorporate these pedestrian “shocks” into  their model of traffic flow and incident rates. The team will use these results to propose new techniques for CAVs to predict and plan for pedestrian behaviors.  The team will use their existing mixed-autonomy simulator, developed with Mobility21’s support, to numerically evaluate their models and how the above safety effects vary for different amounts of CAVs. The team will also leverage models and feedback from their deployment partner, the Southwestern Pennsylvania Commission (SPC), in their simulations. The team will further measure how CAVs’ effects are distributed around a city and implications for equity (see also “Outputs” below).  This project is synergistic with the concurrently submitted proposal entitled “Mitigating Cascading Failures for Safety in Transportation Networks in the era of Autonomous Vehicles,” where the goal is to evaluate the safety impact of AVs from the perspective of their impact on cascading road failures and congestion. In contrast, the current project focuses on CAVs’ safety impact in terms of the traffic incident rate in mixed-autonomy settings. As such, the two projects complement each other and can be combined at a total budget of $150,000 if preferred.]]></description>
      <pubDate>Mon, 20 Nov 2023 19:35:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292640</guid>
    </item>
    <item>
      <title>Measuring Pedestrian Psycho-Physiological Well-Being in the Built Environment</title>
      <link>https://rip.trb.org/View/2251037</link>
      <description><![CDATA[Current advancements in ubiquitous sensing technologies have the potential to increase transportation designers’ understanding of pedestrian behavior and experience. It is now possible to collect human state and behavior data naturalistically and longitudinally. Currently, over 900 million wearable devices are being used worldwide on a daily basis (Tankovska 2020). The application of these devices spans over a variety of fields such as mental health (Coughlin and Stewart 2016), health and physical activity (Kos and  Kramberger 2017, Hsu et al. 2018), and sleep monitoring and interventions (Jeon and  Kang 2019). Similarly, these technologies can be utilized to identify how pedestrian states, behavior, and well-being vary in different contextual settings. Using mobile sensing technology such as gaze, heart rate, and stress trackers, this research attempts to characterize whether quantitative physiological data (as collected by these sensors) can be used to predict qualitative perception data (captured by stated preference surveys), to ultimately capture changes in the pedestrian urban experience as environmental and infrastructure design variables shift. These user-centric data on road environments are essential for establishing actionable standards linking roadway design to pedestrian well-being. By emphasizing multimodal urban streetscapes that serve as public spaces for people, transportation planners, engineers, designers, and policy makers can reach goals for more livable, safe, and economically vibrant environments.]]></description>
      <pubDate>Thu, 21 Sep 2023 15:44:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2251037</guid>
    </item>
    <item>
      <title>SPR-4816:  Pedestrian and Bicyclist Trespassing Impacts on Rail Grade Crossings Safety and Mobility</title>
      <link>https://rip.trb.org/View/2232851</link>
      <description><![CDATA[This project will investigate pedestrian and bicyclist violations at highway-rail grade crossings. Data on pedestrian and bicyclist violations will be collected from existing INDOT and FRA records, and traffic video surveillance. A marginal analysis will be adopted to quantitatively explain the marginal effects of each independent variable on each injury level. Based on discussions with INDOT several grade crossings will be selected as a testbed to conduct this research. At the end of this project, our team will provide INDOT with some suggestions to improve the highway-rail grade crossing safety in Indiana.]]></description>
      <pubDate>Thu, 24 Aug 2023 15:15:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232851</guid>
    </item>
    <item>
      <title>Before and After Safety Evaluation of California’s and San Diego's Active Transportation Projects</title>
      <link>https://rip.trb.org/View/2232675</link>
      <description><![CDATA[California, and the San Diego region in particular, have invested in pedestrian and bike improvements, including sidewalks, protected cycletracks, bike lanes, protected intersections, and other infrastructure improvements to enhance the user experience and promote safety.

However, despite the efforts to improve infrastructure, collisions involving pedestrians and cyclists and persistently low active travel mode share remain significant concerns. The existing literature suggests that the presence and quality of bicycle infrastructure play a crucial role in improving perceived safety among riders and reducing collision rates (Ferenchak & Marshall; Buehler and Pucher, 2020; Fosgerau et al, 2023; Reynolds et al., 2009; Kaths, 2022; Steinacker et al., 2022; Wysling & Purves, 2022). Previous research that specifically examines the effects of pedestrian and bicycle-friendly infrastructure on crash and injury rates have shown mixed results. For example, longitudinal studies on the Safe Routes to School (SRTS) program, which funds both infrastructure and non-infrastructure improvements for pedestrians and cyclists, found that the program reduced collisions amongst active transportation users in California and New York City (Ragland, Pande, & Bigham, 2014; Dimaggio & Li, 2013). However, another SRTS study focused on New York State did not find significant declines in collisions resulting from the program (Kang et al, 2020).  In some cases, implementation of bicycle infrastructure caused vehicle-bicycle collisions to increase, but researchers hypothesized that this increase occurred because more cyclists were present on streets with improved bicycle infrastructure (Pedroso et al, 2016; Chen et al, 2012). 

California and the San Diego region lacks comprehensive research on both usage and collision rates associated with investments in pedestrian and bicycle infrastructure, which limits understanding of their effectiveness and impact. Quasi experimental studies using pre/post data surrounding new project implementation is particularly lacking. While the implementation of such projects has received widespread support, there is a need to critically examine their benefits, including bike and pedestrian trips, safety and the reduction of the collision rates throughout California and the San Diego region.]]></description>
      <pubDate>Wed, 23 Aug 2023 20:49:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232675</guid>
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