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Both crash frequency analysis (CFA) and real-time crash prediction models (RTCPMs) divide a highway into small segments with a constant length [typically 0.161 km (0.10 mi)] for data aggregation. Many previous studies refer to this constant length as the segment length for data aggregation, but this paper adopts fragment size to avoid confusion with aggregation based on highway geometric features. Several studies have shown that segmentation length impacts the studies' results and recommend not using a length smaller than 0.161 km (0.10 mi) or greater than 0.402 km (0.25 mi) to segment and aggregate traffic data for urban/suburban highways and freeways. Despite the significant impact of the segmentation length on traffic crash aggregation, no specific recommendation for selecting or determining the segmentation length for crash data aggregation exists. This research investigates the impact of segmentation length on traffic crash data aggregation. It establishes a methodology for determining a recommended fragment size (RFS) using hidden heterogeneity in traffic crash data. The study defines featured traffic crash rates using three major traffic crash characteristics: number of vehicles in crash, manner of collision, and crash severity. The analysis uses the Laplacian score with distance-based entropy measure and K-means to cluster highway segments based on the featured crash rates (FCRs) and total crash rates (TCRs) for fragment sizes ranging from 0.161 to 0.402 km (0.10 to 0.25 mi) with an increment of 0.016 km (0.01 mi). The clustering results are compared using their silhouette coefficients. The sample results shows that FCR-based clustering outperforms TCR-based clustering by providing important traffic crash groups within a highway and the RFS to segment and aggregate traffic crash data. The proposed method provides a data-driven comparison of different fragment sizes, revealing the pattern of traffic crashes and a standardized approach for RFS, which reduces the likelihood of fragment misclassification and benefits traffic studies depending on segmentation length. [ABSTRACT FROM AUTHOR]

The quality of the bicycle network determines ridership, safety, connectivity, equity, and livability. Very few former research studies investigated network connectivity for individual user types and identify network needs and barriers based on these rider types. This study measures the network connectivity for different rider types using level of traffic stress (LTS) and graph theory concepts. As a symbolic representation of a road network and its connectivity, a graph represents the structural properties of networks and compares one measure over another by taking into account spatial features. In addition, this study defines a bicycle network for different types of riders using LTS metrics based on traffic speed, road geometry, and traffic volume. This study evaluates the OpenStreetMap (OSM) bicycle network for Portland, Oregon, as a case study. Three transit stations in the downtown, riverside, and residential area were considered to assess the connectivity and barriers with a home at block level for last and first-mile coverage. The analysis shows that 29% of links in Portland need to be improved with more bicycle facilities to provide access to basic adult riders, and 33% of links require improvement for children. The networks are well connected for “strong and fearless” and “confident and enthused” users but not well connected for basic adults and children in many neighborhoods with low alpha and grid tree pattern (GTP) indices. The results indicate that planners and designers need to improve their network connectivity for all types of users to ensure equal active transportation opportunities beyond a particular portion of the network. [ABSTRACT FROM AUTHOR]

The demand for freight transportation is on the rise as a result of population growth, increase in the need for energy, and the expansion of global trade. Existing infrastructure for freight transportation seems inadequate for future growth, and new facilities are required to cover the deficiencies in cargo movement. Underground Freight Transportation (UFT) is a new mode of transportation that uses the space below the ground to carry cargo within autonomous vehicles in tunnels and pipelines. Different propulsion systems have been used for freight pipeline systems. The goal of this research is to find the proper propulsion system for the UFT system. In addition, this research aims at finding the minimum power of the propulsion system and energy consumption for the UFT system operation. Two short-haul and long-haul UFT systems with different load sizes are used to test and apply the results of this research. The findings of this research suggest that because of its light weight and short thickness, a Linear Induction Motor (LIM) is an appropriate propulsion system for UFT. Besides, this research estimates that the energy consumption and power requirement of a LIM system is low. The energy consumption of LIM system is calculated as a function of UFT system operating speed. Therefore, the results of this research can be applied in the design, operation and cost estimating of other UFT systems. [ABSTRACT FROM AUTHOR]

• Development of a cost effective conflict data collection approach. • Need for conflict data involving vulnerable transportation modes. • Provide a tool for rating conflict severity. • Test the effectiveness and practicality of crowd sourced data collection. Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-motor vehicle and other similar conflicts types may define a better performance measure for safety assessment. In the field of transportation safety, an absolute conflict occurs when two parties' paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver is required may also impact public perceptions of safety especially for vulnerable modes. Most of the existing literature focuses on vehicle conflicts. While in the past several years, more research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. A comprehensive analysis of conflicts appears critical. The major objective of this study is two fold: 1) Development of an innovative and cost effective conflict data collection technique to better understand the conflicts (and their severity) involving vulnerable road users (e.g. bicycle/pedestrian, bicycle/motor vehicle, and pedestrian/motor vehicle) and their severity. 2) Test the effectiveness and practicality of the approach taken and its associated crowd sourced data collection. In an endeavor to undertake these objectives, the researchers developed an android-based crowd-sourced data collection app. The crowd-source data collected using the app is compared with traditional fatality data for hot spot analysis. At the end, the app users provide feedback about the overall competency of the app interface and the performance of its features to the app developers. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, encouragement, enforcement, programs, policies, and infrastructure design and planning. [ABSTRACT FROM AUTHOR]

When a traffic incident occurs, congestion starts to disseminate around the incident location. Considering a suitable area to assess the impact of incidents and develop traffic network prediction models for evaluating traffic management schemes remains a challenging question. This study aims at developing a modeling framework to identify an affected area around the incident. For this purpose, linear regression models are presented to predict the maximum distance from a closed link to a link with a specified expected increase in travel time. Nine different models are presented to investigate the effects of the network topology and demand on the size of the affected area around the disruption. The models demonstrate that traffic volume on the closed link, a link's area type, and the travel time on the first and second alternate paths with lowest travel times predict the radius of the affected area. This study will help traffic network managers reduce the complexity of their models by allowing them to use a subnetwork instead of the entire network. [ABSTRACT FROM AUTHOR]

No previous research provided a comprehensive review of the bicycle volume estimation techniques assessing the current research gaps in data and modeling makes it challenging to understand the most effective and accurate strategies to estimate bicycle volumes. This article provides a detailed review of 58 studies published from 1996 to 2021. The review results indicate that conventional modeling approaches such as Linear regression, Negative Binomial, Poisson regressions, and a factor-up method represent the most popular econometric statistical models for bicycle volume estimation, while a decision tree is popular among machine-learning-based techniques due to its simplicity and ease of application, interpretation, and estimation with small data sets. In addition, Strava data, Socio-demographic variables, and bicycle facilities significantly contribute to the predictions. The study documents the current research gaps and recommends future research directions to improve data source evaluations, variable creations, modeling, and scalability/transferability advancements. [ABSTRACT FROM AUTHOR]

This article provides a technical evaluation of the traffic control element of the Anaheim Advanced Traffic Control System Field Operations Test (FOT), sponsored by the US Department of Transportation. The primary objective for this test was the evaluation of adaptive traffic signal control technologies, including the Split Cycle and Offset Optimization Technique (SCOOT) for intersection signal control. The SCOOT evaluation was defined relative to existing, first generation Urban Traffic Control System (UTCS)-based control using standard US field detectorization. This US geometry is not the detector configuration normally used with SCOOT. SCOOT was implemented with some degree of success, though technical problems limited its performance. Anaheim's existing communication and controller systems contributed major deployment limitations since they were less adequate than anticipated. SCOOT remains in use in selected areas, with plans for system expansion. [ABSTRACT FROM AUTHOR]

Objectives Companions (i.e. friends who spend time together) are important for the well-being of older adults. Senior centers in the United States are places for older adults to participate in group activities and form and maintain companionships. However, differences in mobility and transportation may affect the ability of older adults to leverage senior center activities into actual companionships. Methods This social network analysis was conducted to characterize the companionship network among members of a senior center in relation to their life-space mobility and transportation resources. An exponential random graph model was estimated to identify mobility- and transportation-related correlates of the likelihood of a companionship tie among senior center members (N  = 42). Results Members had an average of 2 companionships with one another (M  = 2.2, SD  = 2.7). Companionships were more likely for members with greater life-space mobility (p  = .009), who attended the senior center more frequently (p  = .004), with automobile ownership in their households (p  = .034), and who were not transportation cost-burdened (i.e. spent less than 15% of their income on transportation, p  = .005). Demographic characteristics, limitations on instrumental activities of daily living, and being at risk for depression were not significantly associated with the likelihood of companionships. Discussion These findings extend previous knowledge of the role of life-space mobility and transportation in supporting general social participation for older adults to include the importance of transportation and mobility for having companions within a senior center. [ABSTRACT FROM AUTHOR]

Widely used data-collection methods fail to fully capture the lived experiences and un-met travel demands of marginalized populations and those at disproportionate risk of experiencing transportation disadvantage (TD). This study describes an interdisciplinary collaboration between social work, civil -engineering, and computer science on the development of a transportation data collection tool, MyAmble. The theoretical framework of social exclusion was used as part of the study design in order to better understand the process of marginalization as a result of inability to participate in wider society, in part through lack of transportation. Thus, MyAmble augments the best practices in transportation daily diary research using features designed to characterize and quantify latent travel demand among individuals experiencing TD, as well as the subjective impact of lack of transportation on quality of life and well-being. MyAmble includes several innovations – a daily digital trip planning that captures latent demand, a text-messaging based qualitative interview tool, a self-guided travel history survey, and a challenge logger. We describe the design process and implementation that occurred during a five-day pilot test and present a case study. Finally, findings from the case study suggest that MyAmble was able to successfully capture planned, unplanned, and unserved trips as well as the lived experiences of TD. [ABSTRACT FROM AUTHOR]

Emerging sources of mobile location data such as Strava and other phone-based apps may provide useful information for assessing bicycle activity on each link of a network. Despite their potential to complement traditional bike count programs, the representativeness and suitability of these emerging sources for producing bicycle volume estimates remain unclear. This study investigates the challenges and opportunities by fusing Strava data with short-term and permanent conventional count program data to produce bicycle volume estimations using clustering and nonparametric modeling. Analysis indicates that the concentration of permanent counters at high bicycle volume locations presents a significant challenge to produce network-wide daily volume estimations even though Strava data demonstrate potential in mitigating the estimation bias at lower-volume sites. Despite the contribution of Strava to develop reliable and spatially and temporally transferable bicycle volume estimations, significant challenges remain to rely on Strava counts alone to characterize network-level activities due to sampling bias and spatial representations. This study will help planners discern and assess the challenges and opportunities of using emerging data in bicycle planning. [ABSTRACT FROM AUTHOR]

During major highway construction, when lanes or entire highway sections must be temporarily closed, traffic managers would like to inform motorists of alternative routes around the construction site well in advance of the project location. This study develops a traffic diversion model to propose an optimum alternate route to drivers during a construction activity. The models and algorithms developed in this study assess a potential diversion route to optimize network performance while considering the drivers' behaviors in following the proposed alternate route during a closure. A bilevel optimization model is proposed to minimize the total travel time of the affected network considering the link closure and a proposed alternate route for the travelers. A travelers' route choice decision is modeled based on the user equilibrium traffic assignment, whereas a certain percentage of drivers are assumed to divert to the recommended alternate route. A sufficiently large subnetwork is selected, and a path selection method is proposed to reduce the computational effort required to optimize the model. A set of simulation experiments is conducted using the Tarrant County network in north Texas. The results show the ability of the model to improve the overall network performance during hypothetical closure scenarios. [ABSTRACT FROM AUTHOR]

As agencies consider strategies for meeting the livability goals (e.g., public health, mobility, and access) of communities, they often repurpose the existing right-of-way of arterials to meet traffic operation or safety goals. Agencies need more definitive guidance on when this repurposing of existing right-of-way can be done without negatively affecting safety. Researchers around the world have considered the safety efficacy of lane width, but none defines lane width design recommendations of urban arterials. This study uses treed regression analysis on crash data collected from four cities of Nebraska to develop predictive models for identifying the crash modification factors of different lane widths. The analysis from five treed regression models indicates that narrower lanes increase the likelihood and the frequency of injury and noninjury crashes on high-volume segments; however more specifically, the results also show that narrower lane widths can be adopted for segments with lower volumes, a sufficient number of through lanes, and lower speed limits without degrading safety. Based on these crash modification factors, the research team creates design recommendations for arterial lane widths using combinations of speed limit, average annual daily traffic per lane, and number of through lanes. [ABSTRACT FROM AUTHOR]

Cell phone usage may impair a driver's decision-making at a dilemma zone. This research seeks to identify the impact of cell phone usage on different dilemma zone driver behaviors. Participants were exposed to different driving situations in a simulator where they had a phone call while driving through signalized intersections. A combination of variables was collected; therefore, this research estimates Classification and Regression Tree (CART) and stepwise logistic regression models to describe the factors influencing dilemma zone driver behavior. While the logistic regression models focus on the overall impact of the variables, the CART model develops subpopulations based on the variables' impact. Cell phone usage, especially incoming calls on a handheld device, and the overall experiment appear to encourage conservative behavior where the drivers opt to stop even when they are "expected" to go. Unfortunately, when the drivers decide to go, they tend to make the wrong choice and run the red light. Using hands-free devices on outgoing calls appears to reduce the likelihood of performing an illegal maneuver. This represents a potential opportunity for future policy and technological advancements to improve intersection safety by only permitting outgoing hands-free calls on arterials. [ABSTRACT FROM AUTHOR]