International Conference on Transportation and Development 2024: Transportation Planning, Operations, and Transit

This paper details challenges, opportunities, and lessons learned while seeking to count the number of people walking and bicycling along US 180 in southwest New Mexico. Findings show people are walking and bicycling along this corridor, with at least 2 to 6 people bicycling, one-way, on an average day, and at least 9 to 11 people walking, one-way, on an average day. Those using an existing sidewalk and multi-use pathway appear to be doing so for utilitarian purposes, particularly during weekdays. Bicyclists on the roadway and those walking and bicycling on the weekends appear to be for more recreational purposes. The researchers used the best available tools for the count. However, from this effort, there was a demonstrated need to provide methods, guidance, and tools regarding best practices for counting those walking and bicycling in the rural context.

With a growing interest in promoting sustainable and healthy modes of transportation within academic settings, this study aimed to identify factors that influence the campus community’s preferences for walking and bicycling. To assess the perceptions of the campus walking and bicycling environments, two subjective indices were developed based on the input from university campus members. The analysis involved studying campus members’ walking and bicycling habits, and further utilized structural equation modeling (SEM) to examine the relationships between different variables. The results indicated notable gender disparities in the frequency of active transportation on campus, with male students showing a higher inclination to ride bicycles compared to female students. This discrepancy was primarily attributed to the safety and security concerns encountered by female students. The present study also focused on the influence of built environment characteristics on walkability and bikeability scores. Clear and unobstructed pathways, well-marked bike lanes, appropriate lighting conditions, and the presence of visible traffic control devices emerged as significant factors impacting the perceived walking and bicycling conditions on campus. The SEM analysis also highlighted a positive correlation between walkability and bikeability scores, indicating that improvements in one domain have a beneficial impact on the other.

Intermodal facilities, including port terminals, play a significant role in the economic framework of the United States by making substantial contributions to the country’s GDP, but face challenges managing increased freight volumes. However, increased transportation time within port facilities leads to higher costs, emissions, and impacts on efficiency and sustainability. This research aims to develop a concept of operations (ConOps) for improving the efficiency of heavy truck movement outside port facilities, with goals of reducing congestion, considering greenhouse gas (GHG) emissions, and addressing truck drivers’ satisfaction. The study proposes integrating technological solutions to streamline heavy truck traffic at intermodal port facilities, including scheduled truck arrivals and departures, truck stop and rest areas, real-time traffic information, implementation of dedicated truck lanes, and autonomous truck platooning. The focus is improving communication, efficiency, and safety for trucking companies, operations managers, and truck drivers. Using microsimulation modeling a traffic impact study is also conducted, focusing on a case study near the port of Miami. Different traffic scenarios are implemented to evaluate different strategies, such as dedicated and exclusive truck lanes, freeway lane restrictions, and autonomous truck platooning. Simulation findings emphasize the positive impact of these strategies on travel times and delays, and forecast scenarios account for increased truck volumes. Dedicated truck lanes and truck platooning demonstrate promising results in improving overall traffic flow. This research supports decision-making for government officials and logistics service providers in sustainable and efficient intermodal freight planning.

The boom of e-commerce and the increasing demand for fast and reliable delivery services have led to the thriving of on-demand delivery (ODD), which provides delivery services to food takeout, grocery, pharmacy, and other light-weighted goods. The operational efficiency of ODD is subject to many factors—access to curbside, delays at the pick-up and drop-off locations, order dispatching mode, vehicle routing schedule, and vehicle refueling needs. The fast-growing delivery orders coupled with operational inefficiencies of ODD may lead to higher vehicle miles traveled (VMT) and pollutant emissions. Policymakers as well as practitioners need to evaluate the VMT and emissions impact of ODD, given the consumer behavior, operational paradigm, and business models. This paper conducted a systematic review of the existing literature to synthesize and summarize the impacts of ODD with a specific focus on VMT and emissions. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline was employed to systematically search for related studies in multiple databases and to crystallize the review scope. The impact evaluation was delved into three aspects: customer shopping behavior (online shopping vs. in-store shopping), ODD operational strategy (truck/van vs. green vehicles, professional delivery vs. crowdsourcing), and business models (home delivery vs. depot/collection point). Overall, this study found that online shopping with coordinated ODD can achieve significant VMT and emissions reduction compared with in-store shopping. The reduction extent depends on the customer trip chaining, travel mode choice, residential area type, and the ratio of product return. The use of zero-emissions vehicles in ODD, such as electric van/truck/vehicle, cargo-bike, UAV, provides relatively higher emissions reductions, but also brings new issues such as charging needs or capacity limits. Collection points (e.g., parcel locker, retailer store, postal service point) can reduce the VMT and emissions if they are optimally distributed, and customers visit them in zero-emissions modes or through trip chaining.

Inadequate truck parking along major US highways, a national crisis, worsens road safety and leads to hours of service (HOS) violations due to trucks parking in unauthorized areas. This study, focusing on challenges during pandemics and similar disruptions, used a survey revealing 85% of truck drivers struggled to find parking during the pandemic. Employing a random-parameters bivariate ordered probit model (RPBOPM), the study identified factors affecting parking availability and HOS compliance both before and during pandemic events. These findings highlight the severity of the truck parking issue and provide a basis for developing targeted programs and policies to alleviate these challenges, particularly during critical times like pandemics, thus enhancing overall highway safety.

The rapid growth of e-commerce has significantly increased last-mile deliveries in cities, leading to heightened greenhouse gas emissions. Crowdshipping (CS) offers a sustainable alternative by leveraging shared deliveries. However, due to its novelty and limited data, understanding CS characteristics remains a challenge. This study, based on the CS trips in Atlanta from 2015 to 2018, revealed the trip patterns and factors influencing trip production. Machine learning techniques highlighted those areas with concentrated food and home appliance stores, limited access to transit, and higher proportions of low-income, educated, or younger populations tend to experience more CS activities. The findings shed light on how to promote CS services in disadvantaged areas and encourage partnerships with businesses for last-mile delivery.

This research aims to enhance paratransit services for vulnerable road users amid Medicaid privatization trends. Addressing challenges in determining dwell time (time spent at a stop without progressing), we present adaptive reinforcement learning for more accurate predictions. Unlike traditional methods, we model the interaction between key factors influencing dwell time uncertainty, creating a reliable service tool. This framework optimizes paratransit routing by accurately estimating service times, contributing to more efficient healthcare access in underserved areas. The framework may lessen dwell time uncertainty and in-vehicle travel times, which could enhance the general quality of paratransit services for the elderly and disabled.

Historically, transportation planning emphasized rapid, often more expensive modes like driving, frequently sidelining community preservation and connectivity. Such trends predominantly affected mixed-use urban locales, advancing vehicle-centric designs over the accessibility, quality of life, and economic interests of residents. However, a recent pivot toward transportation equity, as evidenced by the Bipartisan Infrastructure Law (BIL), is notable. The BIL champions, the Reconnecting Communities Program (RCP), an initiative committed to repairing historically induced community separations and promoting accessible, cohesive, and flourishing neighborhoods. This research evaluates the concept of transportation burdening facilities as defined in various studies and synthesizes the existing literature on the methods for assessing barrier effects. Employing data analytics, the study scrutinizes the 2022 RCP awarded applications to discern common patterns in the outcomes of transportation infrastructure that either segregate or impose burdens on communities. This analysis aims to categorize these patterns, which could highlight community severance by pinpointing and addressing divisions caused by previous transportation decisions. Our findings aim to empower decision-makers with actionable knowledge to understand, address, and potentially eliminate such community disjunctions, fostering more integrated and holistic urban planning.

Seniors residing in rural areas often encounter limited accessibility to opportunities, resources, and services. This paper introduces a model proposing that both aging and rural residency are factors contributing to the restricted accessibility faced by rural seniors. Leveraging data from the 2017 National Household Travel Survey, the study examined three hypotheses pertaining to this causal model. Multiple causal pathways emerge in the data analysis, with mobility identified as a mediator in one of them. The study further identified specific challenges faced by rural seniors, such as the reduced accessibility in reaching medical services and assisting others. These challenges stem primarily from aging and geographic obstacles that not only diminish their willingness to travel but also restrict the group from choosing transportation modes with higher mobility. Insights gained from this study serve as a foundation for devising effective methods to enhance transportation accessibility for seniors in rural areas.

Unequal mobility and accessibility have been a key constraint in accessing jobs, education and healthcare, and other opportunities across the nation. This is aggravated by differences in income, transport infrastructure, transit, and indeed all modes, vehicle availability, class of workers, and other variables which individually or collectively contribute to the commute inequity. Evaluating these can be challenging because there are types of equity and impacts to consider including horizontal and vertical commute equities and various ways to measure them. Horizontal equity assumes that people with similar needs and abilities should be treated equally; vertical equity assumes that disadvantaged groups should receive a greater share of resources. The present research process involves examining and measuring commute equity as a dependent variable, which is determined by a function of independent variables such as vehicle availability, worker participation rates, travel time/time arriving, class/type of worker, mode choices, and other crucial components. Different variations of the regression model will be employed to analyze the relationship between the independent variables and commute equity. These regression models will serve as tools to quantify the impact of each independent variable on commute equity and gain insights into the factors contributing to transportation inequities. The accuracy of each regression model will be thoroughly examined to assess their predictive performance in estimating commute equity. The results obtained from each model will be described and analyzed in detail to provide a comprehensive understanding of the relationships between the independent variables and commute equity outcomes. Based on the derived results, the research project will formulate informed recommendations aimed at guiding policymakers in improving inclusivity and accessibility within transportation systems. These recommendations will take into account the identified influential variables and their impacts on transportation equity, providing practical strategies to address inequality and enhance equity within the studied neighborhoods and beyond.

This study examines the relationship between household income and cycling activities in the US, using 2017 National Household Travel Survey (NHTS) data. Contrary to expectations, lower-income households engage in more bike trips and bike-sharing than higher-income ones. This pattern holds for both utilitarian and recreational cycling, suggesting it serves as an essential mode of transportation for low-income individuals. Bikesharing usage is also higher among this group, particularly in the lowest income bracket, highlighting its potential as an affordable and convenient option. However, racial disparities exist in bikesharing, with white individuals participating more than black or African American individuals, indicating potential inequities in access to resources and infrastructure. While sociodemographic factors like age, gender, and education show no significant difference in bike-sharing usage, suggesting its potential for diverse users, further research is needed to address observed disparities and promote equitable access to cycling and bike-sharing for all.

This study addresses a gap in the literature by investigating bikeshare program usage across different racial groups, specifically focusing on sociodemographic variables. Using the latest and most comprehensive national transportation data in the US, the National Household Travel Survey (NHTS), the research explores newly introduced bike-related variables and prioritizes analysis for white, African-American, and Asian populations by weighting the NHTS data. Results reveal that the African-American population has the lowest average number of bike trips compared to white and Asian populations. Among African-American and Asian populations, females show the highest 30-day average bikeshare program participation compared to males. Additionally, individuals with a household income exceeding $100,000 have the highest 30-day average bikeshare program participation in these groups. The study’s primary contribution lies in offering a deeper understanding of bike-travel behavior among different races in the US, aiding authorities and transportation planners in equitable investment prioritization for bike infrastructure.

This paper addresses the urgent need to incorporate equity considerations into benefit-cost analysis (BCA) in US transportation and infrastructure projects, potentially leading to an equity-based BCA. Utilizing a comprehensive meta-analysis, the paper explores the factors influencing equitable outcomes, examining aspects such as assumptions and considerations, the extent of benefits and costs, treatment of long-term and indirect effects, and the integration of environmental and social concerns. Additionally, the paper underscores these factors’ importance in the discussion on reforming BCA practices. It also examines the current concept of efficiency in BCA, emphasizing the potential for redistribution through taxes and transfers and the theoretical possibility for policy beneficiaries to compensate those worse off through a costless, lump-sum transfer, ensuring overall societal improvement. The paper provides policy and practice implications, recommendations for enhancing BCA practices in transportation and infrastructure projects, and suggests ways to promote more informed decision-making.

This study examines gender differences among bikeshare users in Chicago, focusing on a dataset of over 21 million Divvy bikeshare trips between 2013 and 2019. Using a binary regression model, the research investigates the association between gender and trip attributes. Results indicate that women tend to take longer bikeshare journeys than men. As a “dependent” or “subscriber” user, a rider is more likely to be a man. While both genders exhibit similar ridership patterns on weekdays, women show a greater tendency to use bikeshare on weekends compared to men. Peak ridership for both genders occurs between 8 a.m. and 5 p.m., suggesting recreational or weekend use. The study suggests the potential development of a more accessible and equitable bikeshare system by incentivizing weekday passes and ensuring adequate bikes and docking stations during rush hours. These findings contribute to informed decision-making, supporting initiatives to address gender gaps in Chicago’s bikeshare system.

The 15-min city concept promotes active transportation and sustainable lifestyles by ensuring essential amenities within a 15-min radius from residences. While attracting significant interest, concerns about potential limitations on freedom of movement have been raised. This study aimed to gain broad insights into public perceptions and understanding of the 15-min city concept. Text mining and sentiment analysis techniques were applied to extract prevailing sentiments. The findings indicated that positive and negative sentiments were evenly distributed among the collected tweets, suggesting a balanced and diverse range of opinions. These opinions are what helps shape the policy and legislation, especially when prominent individuals with a large online presence endorse or reject a specific idea or theory. To further enhance the analysis, three different machine learning classifiers, namely naïve Bayes, logistic regression, and support vector machine, were employed to classify the sentiments expressed in the tweets. The framework developed in this study and the insights derived from the sentiment analysis offer valuable resources for policymakers and urban planners seeking to comprehend and embrace emerging urban concepts like the 15-min city.

In the context of natural disasters such as earthquakes and tsunamis, efficient and equitable evacuation strategies are critical for safeguarding human lives. This paper introduces a multi-objective optimization model that seeks to minimize both the overall transportation distance for affected individuals and the average unutilized space in designated evacuation centers. The model explicitly incorporates fairness, ensuring that all residents are equally prioritized during the evacuation process. We tested the model’s applicability using a real-world scenario in Seaside, Oregon, which is a vulnerable town to frequent natural hazards. The case study aims to address the specific logistical challenges associated with evacuating a population of approximately 6,000 residents under tsunami hazard. Our analysis indicates that the transportation mode and capacity are pivotal variables for achieving an effective and equitable evacuation procedure. This research extends the existing literature on disaster preparedness by emphasizing the crucial role of transportation logistics in ensuring successful and fair evacuations.

Mechanized tunneling technology using a tunnel boring machine (TBM) has a great space in the modern era as it provides a safe and workable environment for the working crew and a high advance rate in favorable geologic conditions. Despite being a good solution in the favorable ground scenario, it will create a challenging situation for the weak and fragile rock masses. Himalayan mountain ranges are the most unstable region in the world, and seeking the researcher’s attention for proposing a suitable methodology for successful tunneling operations using TBM. Complex geology, tectonically intricate zones, squeezing ground, faults, and ingress of water are some of the inevitable challenges in this region. The selection of appropriate TBM machines and other pressure parameters is a significant task. Keeping all in view, this research work proposed the study approach in the assessment of geo-risk evolved in TBM tunneling. A finite element-based program using Midas GTS Nx was used for the three-dimensional analysis of the mega tunnel. Mega tunnel refers to a large dimensional artificial passageway as an underground structure beyond 10 m in diameter. Soft computing models were prepared considering actual ground conditions and fault characteristics. Maximum vertical displacement of the surface considering plastic region up to three times of tunnel diameter (3D) was observed along with the crown settlement. Specification of single shield machine was considered as per literature at initial analysis. It was found that applied machine parameters were inadequate at the actual fault zone and led to excessive displacement of the ground, which may lead to blocking and jamming of the particular machine. The application of a combination of drilling thrust and cutter head torque provides a workable solution to avoid the above-mentioned risk. This research work anticipates a viable solution in the application of machine pressure parameters and provides insight for decision-making in the practical condition of the Lesser Himalayan region.

This paper aims to understand the post-COVID-19 travel patterns at the University of Texas at Austin (UT Austin). It constitutes a continuation effort of the 2022 study based on a travel preference survey distributed to the whole UT community during the spring of 2022. The 2022 travel preference survey was redistributed during the spring of 2023 to understand how travel patterns are evolving post-COVID-19. The survey questions include information about frequency, time, and purpose of commute as well as the mode of transportation used. They aim to analyze the shift in travel behavior as we gradually move to the new normal for operating conditions. The results of both surveys are compared and analyzed to help the Office of Sustainability at the University of Texas at Austin make informed decision regarding university transportation needs. Both surveys clearly illustrate the persistence of the hybrid mode of operation: 40% of undergraduate students, 40% of graduate students, 32% of faculty, and 27% of staff commuted five days a week to campus in 2023. Respondents expressed their tendency to use the car mode as it is the fastest way to get to campus and due to the lack of transit options available to them. This effort will help researchers and decision makers better develop travel demand modeling assumptions.

Parking is a growing problem globally due to the growth of urbanization, and it is particularly problematic in densely populated areas, such as college campuses, where the demand for parking is high. This research investigates the complexities of parking management at the University of Texas at Arlington (UTA) and looks carefully at its implications for urban planning and sustainability. Qualitative interviews with 19 participants who were either students, faculty, or UTA provided insight into their experiences and satisfaction levels with the campus parking system and found that socioeconomic characteristics, travel behaviors, and specific parking challenges have a significant impact on an individual’s parking experience. The interviewees revealed a profound reliance on private vehicles; a marked preference for close, convenient, on-campus parking; and a keen interest in technological advancements such as smart parking solutions and real-time availability systems, suggesting a campus-wide readiness to embrace innovative parking management strategies. As a result of the findings, the study proposes policy recommendations such as tiered parking permit pricing, strategic parking lot placement, flexible pricing strategies, and the promotion of alternative transportation methods to alleviate parking pressure and support sustainability goals. This paper contributes to the discourse on transportation challenges within the microcosm of a university campus, offering insights that are pertinent to the development of responsive, efficient, and sustainable parking management policies.

Transportation agencies nationwide have been shifting toward strategic planning goals to reduce automobile dependence and promote diverse and sustainable multimodal systems. With the increased government funding for pedestrian and bicycle transportation, a resulting increase in shared-use pathways, pedestrian infrastructure, bicycle lanes, and sidewalks has been recognized across the United States. It is indeed important for state agencies, local municipalities, and governments to develop research studies to address the overall feasibility, design, and operational issues of such facilities. Integrating diversity in the transportation modes such as transit, pedestrian, bicycling, and ADA-accessible facilities has been one of the main focuses of Frederick County Government (FCG). The primary objective of this study is to analyze and investigate the feasibility of potential routes in the county for shared-use path corridors. This paper evaluates a case study of multiple alternatives for a shared-use facility along a generally north-south alignment between the city of Frederick and Urbana District Park. Two final alignment alternatives for the proposed shared-use path were shortlisted and evaluated using feasibility-level construction cost estimates based on a cost-per-mile estimate. Additional consideration was given to potential design minimization alternatives that could be considered as part of future design efforts to reduce the cost of construction. Transportation researchers and the American Society of Civil Engineers (ASCE) community are believed to be benefitting from this study.

The functionality of a road network is disturbed by seismic events. The disturbance in the road network is associated with damage to the network’s assets (e.g., pavement, bridge) and unexpected changes to the network’s functionality. Proactive road network rehabilitation helps reduce damage to the network’s assets and prevent unexpected changes in network functionality due to seismic events. However, all road network assets cannot be rehabilitated at once due to the limited availability of rehabilitation resources. This research aims to identify the optimal combination of a network’s concrete assets that should be proactively rehabilitated to reduce the road network’s functionality changes when limited resources are available for rehabilitation. The analysis was conducted in three stages: characterizing concrete assets’ performance uncertainties, estimating the pre- and post-disaster road network’s functionality using traffic simulation, and formulating and solving a stochastic combinatorial optimization problem to identify the optimal combination of a network’s concrete assets. A road network located in California was selected for this study. Probabilistic seismic fragility functions were used to identify all critical road network concrete assets (e.g., pavement, bridge) and their associated failure probabilities subject to seismic events. Traffic simulation was performed to calculate the changes in functionality due to the damage to identified critical concrete assets. Simulation of Urban Mobility (SUMO), an open-source software, was used to conduct the traffic simulation. A simulated annealing-based optimization algorithm was used to solve the optimization problem and identify the optimal combination of road network assets. This study supports optimal resource allocation for road network seismic rehabilitation, which is essential for post-disaster transportation infrastructure safety and efficiency.

Housing affordability and transportation efficiency refer to a household’s ability to spend less than 45% of their gross income on housing and transportation costs. Past research indicates that low-income households generally allocate a significantly higher portion of their income to housing and transportation costs. In areas of Southern California like the Inland Empire (a common term given to the two-county area including Riverside and San Bernardino counties), relatively low housing costs (compared to neighboring coastal counties) place financial strain on households, particularly those in underserved communities, defined by FEMA as socioeconomically disadvantaged people, people of color, ethnic and national origin minorities, people with limited English proficiency, and others, who face increased transportation costs due to car-dependent environments. The research investigates how housing costs, affordability, transportation efficiency, and accessibility affect disadvantaged communities in the Inland region, particularly on the challenges faced by underserved populations. The research findings indicate that disadvantaged populations in the region encounter disproportionate obstacles within housing and transportation policies, with geographical disparities exacerbating these challenges. This includes housing development occurring away from job centers, which not only burdens residents financially but also limit their access to essential opportunities, thereby perpetuating cycles of poverty. It’s worth noting that similar challenges can affect higher-income individuals and families, albeit to a lesser extent. These findings underscore the critical need for more conclusive actions to address these disparities and create more equitable housing and transportation solutions for all residents.

This research examined the infiltration rates on the surface of a pervious concrete (PC) test slab in Beaumont, Texas, and the infiltration rate into the soil below the underground aggregate storage bed. The site has very slow infiltrating clayey soils, and the trials are intended to provide information on the efficacy of using green stormwater infrastructure (GSI) in similar areas. The slab and associated storage bed measured 10 ft by 20 ft, with a total depth of 22 in. An analysis of the void ratio of the aggregate bed provided a relationship between the water depth in the aggregate bed with the soil infiltration rate. Furthermore, the relationship between the water depth in the bed was studied for a combined PC and rain garden (RG) system. The surface PC infiltration rate measurement was carried out using a single ring infiltrometer and for the newly placed slab was on average 1,450 in./h. The soil infiltration rate was measured with a real-time water level sensor with an associated rain gauge. The soil infiltration rate was estimated to be 0.01 in./h. With the combined PC/RG system the rate of the draindown of the water level in the pervious concrete storage bed at times doubled for larger storms, indicating that the rain garden may have aided in its effectiveness, possibly by evaporation or other processes.

The accelerating adoption of electric vehicles (EVs) poses challenges to the power grid, necessitating precise representation of mobility patterns for effective infrastructure upgrades. Traditional simulation-based charging demand estimation faces limitations in generating trip chains reflective of actual travel patterns without complex network modeling. Hence, an innovative agent-based trip chain generation model is introduced to overcome these challenges. Drawing from the National Household Travel Survey (NHTS) and the NextGen NHTS origin-destination add-on data for Clarke County, Georgia, this study proposes a simulation method capturing both temporal and spatial mobility patterns without relying on extensive network topology data. The resulting trip chains predict EV charging load at the Census Block Group level, validated with a 1.03 correlation to actual trip counts, affirming their reflective accuracy. Two charging scenarios, residential-only and charging-everywhere, reveal distinct demand profiles. The charging-everywhere scenario aligns closely with the trip profile, while the residential-only scenario exhibits an afternoon peak slightly surpassing the former. This study contributes a data-driven charging demand estimation methodology, offering critical insights for grid resiliency planning amid the evolving landscape of EV adoption.

Dynamic tolling stands as a pivotal tool in contemporary highway management, aiming to balance traffic flow and generate sustainable revenue. Yet, a somewhat paradoxical outcome, the jam-and-harvest (JAH) phenomenon, has emerged. This research scrutinizes the jam-and-harvest (JAH) effect in dynamic tolling, revealing its potential to exacerbate congestion on free lanes as traffic diverts from managed lanes. This simulation-based study highlights the revenue increase for operators during peak traffic times but also underscores a significant equity issue: lower socio-economic groups, with a lower value of time (VOT), face harsher delays in congested unmanaged lanes. Based on the findings, this paper comes up with policy suggestions to address the inequalities caused by JAH, advocating for more accessible public transport and regulated toll caps to ensure fairer access to roadways.

This study explores the adoption of e-scooters as a transportation mode in Chicago and examines various aspects of the mode, including trip patterns, temporal analysis, and the influence of sociodemographic factors on equity. The analysis reveals a diverse user base, with both men and women participating in e-scooter trips, and a mean trip duration of around 19 min. Temporal analysis highlights peak usage during mid-weekdays, especially Tuesdays to Thursdays, and on weekends, particularly Sundays, emphasizing their role in addressing commuting needs. Furthermore, the hour-level analysis identified 5 p.m. to 6 p.m. as the peak usage period, emphasizing the role of e-scooters in addressing commuting needs during rush hours. A Poisson regression model indicates a significant negative relationship between socioeconomically disadvantaged areas and the starting points of scooter trips, suggesting potential disparities in access. This study contributes valuable insight into the evolving understanding of e-scooter usage, emphasizing the importance of collaboration between city planners and policymakers for an equitable e-scooter system.

Vehicle transactions are the events that lead to the modification of household vehicle fleet structure, better modeled in a longitudinal framework. Household composition, life-course events, existing fleet characteristics, and neighborhood characteristics are the time-varying aspects that impact transaction decisions. Heterogeneity and state dependence are the two main issues in such statistical models. To simultaneously account for the heterogeneity and state dependence, this study applies longitudinal latent class cluster analysis to a retrospective response on vehicle transaction behavior from 320 households in Kolkata, India, over the last 10 years. It was found that households can be classified into biographic states and transition from one to another over the years. Apart from that, as societies evolve, biographic states’ characteristics also evolve. From 2013 to 2020, two states and from 2021 to 2022, a three-biographic state model could explain the variation in vehicle transaction propensity.

Bikesharing has emerged as a transformative mode of transportation, contributing to increased cycling, urban mobility, and public transportation use. This study investigates bikeshare dynamics in Washington, DC, focusing on classic bikes, electric bikes (e-bikes), and docked bikeshare options. The analysis of 3,365,045 trips from January to October 2023 resulted in spatial patterns that reveal concentrated trip origins in downtown Washington, DC, emphasizing urban core utilization. Temporal trends highlight peak mid-weekday ridership, aligning with daily work commutes. Multinomial logistic regression discerns nuanced preferences: docked bikes are favored on weekdays, electric bikes on weekends, and members are less likely to choose classic bikes. Findings provide actionable insights for policymakers, informing bikeshare system optimization, station placement, and marketing strategies. Weather-aware planning and promotion, coupled with tailored incentives, are crucial for enhancing bikeshare sustainability and effectiveness in urban transportation.

This study investigates the accessibility of bike infrastructure, including bikeshare stations and bike lanes, in New York City (NYC), with a focus on sociodemographic factors. Utilizing a combination of buffers, the bicycle equity index (BEI) and binary logistic regression, the research analyzes the spatial distribution of bike lanes and bikeshare stations, emphasizing the importance of equitable access. The BEI methodology integrates variables such as minority population, poverty, age groups, and zero-vehicle households to assess equity. Binary logistic regression identifies significant predictors, revealing that households with no vehicles, age-dependent populations, and minorities significantly influence bikeshare station availability. The outcomes provide valuable insights for urban planners, policymakers, and transportation authorities to address sociodemographic disparities and enhance the inclusivity of bike infrastructure in NYC. The research contributes to the understanding of factors shaping bike infrastructure access and informs targeted interventions for creating a more equitable and sustainable urban transportation landscape.

While widespread, relevant policies and incentives play an active role in promoting extensive adoption of electric vehicles (EVs), the forecasting of EV charging demand emerges as a crucial consideration with implications for power grid management. This paper addresses this imperative by presenting a research methodology aimed at innovating EV charging forecasting models and integrating them into computational algorithms. This integration facilitates the analysis of overall EV charging demand and distribution issues within a specific geographic area. This study employs a bottom-up heuristic approach to construct a methodological framework for incorporating the developed EV activity-based travel demand forecasting (EVA-TDF) models into a process of trip-chain-simulation based process. To evaluate the effectiveness of the proposed methodology, the greater Cincinnati area in Ohio serves as an exemplary study site. The findings suggest that urban areas characterized by high population density may be optimal locations for deploying level 1 and 2 EV charging stations.

In urban settings like Dhaka, the capital of Bangladesh, poor sidewalk conditions pose challenges for pedestrians, affecting usability and aesthetics. This study introduces a novel approach utilizing the sidewalk condition index (SCI) to assess sidewalk conditions. Through a comprehensive methodology involving literature review, walking and video surveys, distress assessment, and SCI calibration, 58 sidewalk segments along key routes were analyzed. The study identifies the top eight hazardous areas, leading to a calibrated SCI that categorizes conditions from excellent to poor. These findings provide actionable insights for local government authorities, facilitating prioritization of sidewalk maintenance and budget allocation. The proposed sidewalk management strategy advocates for policy improvements, emphasizing construction, rebuilding, and maintenance, with recommendations including a five-year planning term and cost analysis for each segment. This research serves as a practical guideline for enhancing pedestrian safety and urban aesthetics, underscoring the importance of adequate budget allocation for improving sidewalk infrastructure in Dhaka and similar cities worldwide.

Electric scooter sharing has emerged as a promising and environmentally friendly alternative for short-distance urban mobility. This research paper aims to analyze the travel patterns and accessibility impacts of electric scooter sharing in low-income neighborhoods of Chicago. Geographic information system (GIS) was used to conduct a spatial analysis of scooter sharing station locations, transportation infrastructure, and socio-economic indicators. The methodology involves data collection on scooter sharing station locations and socio-economic factors, followed by travel pattern analysis. Thematic maps are created to visualize the spatial distribution of scooter sharing stations and identify gaps in service coverage. Network analysis tools are used to assess the accessibility of scooter sharing services in relation to public transit stops and essential destinations. Statistical modeling is employed to evaluate the relationship between scooter sharing adoption and socio-economic factors in low-income neighborhoods. The results reveal insights into travel patterns and accessibility impacts, indicating areas of high scooter sharing demand and potential expansion opportunities in underserved communities.

Motorcycle fatality rates in the US in 2018 and 2019 based on vehicle miles travelled were 28 times greater than passenger car fatality rates. The objective of this study was to evaluate how socio-economic characteristics, law enforcement, weather-related factors, road-related characteristics, and rider-related factors in each state of the US were associated with motorcycle fatalities in the 2018−2019 period. A multiple linear regression model was developed with a 90% confidence level (p < 0.1), and per capita alcohol consumption, annual average temperature, helmet law, percentage of the population aged 25 and over who have completed a bachelor’s degree or higher, per capita personal income, percentage of people holding a valid driving license, and overall percentage of acceptable road miles variables were statistically significant in the model with an R-square value of 0.694. These findings would be useful to improve the awareness of motorcycle riders, passengers, and other motorists to improve traffic safety.

Educational institutions often face unforeseen situations that necessitate rapid access to emergency services within their vicinity. However, in larger cities with extensive urban sprawl, universities encounter challenges in accessing healthcare facilities. Therefore, this study aims to analyze the accessibility of emergency care services to universities. The research adopts the gravity model as a framework for assessing accessibility and uses real travel time as the primary dependent variable. The results indicate that universities located in the central area of the city exhibit higher accessibility compared to those situated in the periphery. The results highlight an imbalanced spatial distribution of emergency care services in relation to universities, emphasizing the need for additional measures. The study holds significant implications for decision-makers, enabling them to identify areas with limited accessibility to emergency services and plan for new or relocated emergency care services to meet standard minimum response time. Furthermore, universities are encouraged to integrate the findings into their emergency plans, enhancing their preparedness and response strategies.

Many communities have underserved broadband service. With a wide variety of broadband technologies, funding sources, development models (e.g., city-owned, and operated infrastructure with a single internet provider), and revenue models, it is unclear which ones allow broadband to reach underserved and disadvantaged communities. The objectives of this research are to capture the successes and challenges of developing broadband projects and to acquire information about the funding, financing, development, and revenue of different broadband development projects. The approach is comprised of developing and distributing a structured survey that aims to investigate the funding sources, and financing and revenue models utilized for broadband projects, with a critical focus on their success and failure lessons. The survey targeted participants from private and public sectors experienced in broadband projects. A total of 36 responses were collected from 17 states and the District of Columbia. The study identified a broad range of public and private funding sources, as well as the development and revenue models adopted in broadband projects. Moreover, barriers, successes, and strategies for enabling broadband market entry were identified and can be used as lessons learned for future broadband projects. For example, survey results show that private entities own the last mile in most projects which creates the barrier of broadband reaching underserved and disadvantaged communities. However, the results also reveal measures that can be taken to prevent the emergence of broadband monopolies and expand broadband to underserved and disadvantaged communities (e.g., the use of open access networks).

A successful transportation project’s planning and execution depends on accurate cost estimation. This paper focuses on the preliminary cost estimation techniques used in transportation projects and provides the results of an extensive survey targeting transportation professionals at state departments of transportation (DOTs) in the United States. The purpose of the survey is to learn about the practices, methodologies, tools, and challenges associated with preliminary cost estimation as it is employed across various states. The survey’s methodology and participant demographics are covered in full in the first section of the paper. The survey findings are then explored in detail, indicating the variety of practices employed by DOTs in terms of estimation methods, data sources, and degrees of satisfaction with the current procedures. It also emphasizes the types of estimating tools utilized, the procedures applied to contingency cost estimation, and whether agencies adhere to federally authorized approaches. The research further investigates how agencies handle unit costs, inflation, and cost indices, offering light on regional factors. About 36% state DOTs responded to the survey, and according to the survey, there was no federally prescribed preliminary cost estimating approach and the most common tool was Excel-based tool. It is clear from the survey responses that there is wide variation in practices across the various state DOTs. This study contributes to the field of transportation project management by offering the current state of practice when it comes to preliminary cost estimating approaches for transportation projects. The paper also presents future directions for developing risk-based cost estimating approaches that are suitable for practical usage.

The coronavirus took toll on human lives and catalyzed a crippling economic recession that led to soaring unemployment rates. The construction industry, due in part to its labor-intensive nature, has a high incidence of COVID-19 cases that severely affected various aspects of project management, such as cost, schedule, contracts, finance, and occupational health and safety. To bridge a research gap, this study embarked on two primary objectives. First, it identified the key challenges that lead to construction cost and schedule overruns, contractual implications, and occupational health and safety issues by reviewing more than 131 articles. Second, it proposed strategies to mitigate or at least minimize the effects of the challenges during the pandemic. The study identified 26 unique challenges and provided respective mitigation strategies for each. Labor shortages, escalating material prices, and occupational health and safety issues were revealed as major factors contributing to cost overruns, and delays in material delivery, reductions in efficiency and productivity rates, and deferrals of ongoing projects for new ones also emerged as key issues. Contractual implications included a lack of guidance on the applicability of force majeure contract clauses, and health and safety concerns were primarily driven by the fear of job loss and reduced income in a volatile job market. This research provides invaluable insights for stakeholders and project managers in the construction sector, enabling them to understand and effectively manage the challenges induced by the pandemic.

The COVID-19 pandemic has brought about significant operational disruptions in the construction industry. While a wealth of literature suggests best practices, a discernible gap remains in gauging the consensus among industry professionals on these recommendations. Specifically, uncertainty arises regarding the degree to which construction engineers and managers align with these best practices during the pandemic. Additionally, there is a notable knowledge void concerning the real-world effectiveness and adaptability of these strategies. Addressing these concerns and aiming to fill the identified gaps, the primary objective of this research is to elucidate the favored mitigation strategies in the construction sector during the COVID-19 crisis. To achieve this objective, survey responses from 131 construction engineers and managers were analyzed using relative importance index (RII). The results revealed that establishing an effective communication system emerged as a top priority among industry professionals. Furthermore, restructuring, diversifying supply chains, and the inclusion of a force majeure clause specific to COVID-19-related delays were also highlighted by respondents. The study’s contributions aim to enhance the construction sector’s resilience, adaptability, and preparedness for both immediate and long-term challenges, such as the COVID-19 pandemic. It offers comprehensive guidelines and positions itself as a quintessential reference for engineers, managers, policymakers, and other industry stakeholders.

The COVID-19 pandemic’s profound effects on various sectors have been widely reported, yet the long-term physical and mental health ramifications for construction professionals remain underexplored. This study aimed to address this gap, drawing on data from 131 responses collected from construction engineers and managers to evaluate the enduring health implications of the pandemic on these professionals and ranked the impacts using mean score analysis. Preliminary findings indicate a notable increase in specific physical ailments such as fatigue, headache, and attention disorders. Concurrently, there was a marked rise in mental health challenges, with anxiety, depression, and fear being the predominant emotional states reported. These results underscore the pressing need for health interventions tailored to the construction sector, emphasizing both physical and psychological well-being. As the construction industry seeks to adapt and evolve in the pandemic’s wake, these insights can prove invaluable. Understanding and addressing these health concerns can guide engineers, managers, policymakers, and decision-makers in crafting strategies and policies that ensure a healthier and more resilient workforce, paving the way for a more robust industry in future global crises.

This study examines the underrepresentation of women in leadership roles in the civil engineering industry, despite them having similar qualifications, experience, and degrees as men. It focuses on how unconscious biases may affect this disparity, especially in how female leaders present their skills and competencies on social platforms like LinkedIn. Analyzing 2,800 LinkedIn profiles of leaders from civil engineering companies, the study compares their self-presentation with colleagues’ evaluations in the recommendation section. It finds that female leaders often align their skill and biography presentation with others’ expectations, showing a 14% higher text similarity in received recommendations compared to male leaders. Despite being viewed as equally competent, female leaders are perceived as less likable, with a 28% likability score versus 51% for males. The study highlights the need to address these biases to enhance gender equality in leadership positions.

The COVID-19 pandemic has significantly impacted people’s travel behavior, leading to a surge in remote work adoption, a trend many companies continue to embrace. This shift toward working from home has not only directly influenced travel demand, traffic congestion, and emissions but also yielded benefits, such as increased productivity and improved work-life balance for individuals. However, as the pandemic recedes, it becomes crucial to investigate whether the opportunity to work from home is equitably distributed among diverse population groups. In this paper, we delve into identifying which population segments have better access to remote work in the post-pandemic era. We analyze survey data collected from individuals in the United States between July and October 2020, employing machine learning techniques such as neural networks, gradient boosting, and logistic regression to model people’s expectations regarding working from home post-pandemic. Through permutation importance analysis, we unveil the significant factors influencing these expectations. Notably, we find that less educated individuals, low-income individuals, and females are less likely to work from home in the post-pandemic era. The results of this study hold the potential to guide policymakers in addressing gender and income disparities in access to remote work opportunities. By developing targeted strategies, policymakers and employers can foster a more inclusive and equitable workforce, ultimately benefiting individuals, communities, and the economy.

This paper presents lessons learned through intensive workforce development projects with the USDOT ITSJPO PCB initiative connected and automated vehicle education (CAVe). With the support of Ohio Department of Transportation (ODOT) and DriveOhio Student Transportation Advancement Research (STAR) program, the University of Cincinnati Infrastructure Institute (UCII) team has assembled CAVe-in-a-Box and CAVe-Lite Infrastructure and Mobile kits. In this project, CAVe-in-a-Box assembly integrated with latest computer vision based detection technology has been utilized as a demonstration tool to showcase real-world benefits such as the vulnerable road user (VRU) safety at intersections. Furthermore, CAVe-Lite module has been utilized to actively engage 5–12 grade students in a scaled, hands-on desktop level smart mobility technology deployment.

Aeromovel consists of a new concept of steel-wheel steel-rail automated people mover (APM). The operation procedure depends on a propulsion system based upon the use of pneumatic power systems that insufflate pressured air inside a duct where a rodless piston connected to the vehicle is driven. In the traditional configuration of the Aeromovel, the power system is designed and adjusted for operating with open loop control strategies. In recent years, Aeromovel application have significantly expanded, resulting in new challenges to be overcome, especially that associated with different trajectories that the vehicles must be performed due to geometric restriction imposed by existent airports’ architectural configurations. Aiming at overcoming these difficulties, a novel automatic driving strategy, based on feedback control, is being developed for the vehicle cruising control. This article deals with this new challenge and the design of the feedback cruising control algorithms that are being developed, including numerical results achieved by simulation performed with a suitable physical model of the system. The closed loop strategy is based on the definition of a desired velocity trajectory that is tracked by driving the blower based pneumatic power system to increase or decrease the effective force applied to the vehicle, forcing the trajectory to converge to the desired one. In this work, the design of the closed loop algorithm and the simulation results using a comprehensive model are addressed, showing the effectiveness of the proposed cruising control strategy. The new line that is being developed to operate in GRU Airport—São Paulo-BR main’s airport—is used as a case-study for availing the analysis strategy.

Monorail guideway structures (MGWS) exhibit distinct performance characteristics compared to traditional bridges due to the flexibility of their guideway beams. Therefore, a comprehensive study of the seismic behavior of MGWS is imperative to ensure their ductility and flexibility through seismic events. Shear and torsion failures are brittle modes of structural failure, necessitating that elements designed for seismic protection possess sufficient resistance capacity to ensure safety and integrity during extreme ground motions. While design codes have addressed shear over-strength ratios for bridges, they do not offer specific guidance for determining the over-strength ratio necessary to safeguard the shear and torsion capacity of MGWS elements. This paper presents a methodology for establishing the shear and torsion over-strength ratio required to protect MGWS elements from brittle failure and to achieve ductile failure. This in research was developed using displacement-controlled pushover analysis (PA) conducted with SAP2000 software. The finite element model created for this study was calibrated based on experimental test results from the existing literature. Shear and torsion over-strength ratios were determined according to the 2022 publication of AASHTO Guide Specifications for LRFD Seismic Bridge Design and were compared to the values calculated from the SAP2000 model. This comparison was conducted in both longitudinal and transverse directions, considering various parameters such as span length, pier height, pier cross-section, reinforcement ratio, confining stirrups, and concrete compressive strength. Ultimately, proposed over-strength ratios for structural members of the MGWS are provided to maintain ductility and prevent brittle failures, both in terms of shear and torsion.

Walt Disney originally envisioned the monorail as a reliable and safe public transit system for the future. The first Disney monorail system opened in June 1959 as merely an attraction taking people on a scenic trip around Disneyland in Anaheim, California, while also showcasing the practicality of monorails for use as public transportation. When Walt Disney World opened in Orlando, Florida, in 1971, the Disney monorail was an important feature of the new amusement park, and today over 50 million annual passengers ride the monorail system. Although monorails have not become a popular public transit alternative in the United States, they are probably the most recognized icons of Disney parks, and what many visitors consider as future transportation. This paper explores the history of the Disney monorails at Disneyland, Walt Disney World, and the Tokyo Disney Resort, the evolution of the Disney monorail trains, characteristics of the systems, operations, and challenges.

Conductor rail systems (CRS) typically comprise less than 5% of an APM system’s total cost, yet they can have an outsized impact on an APM’s reliability. Critical variables to conductor rail performance include performance specifications; design, development, and testing; interface coordination with other subsystems; installation quality; and maintenance. Interface coordination between the conductor rail system and other APM subsystems such as the vehicle, guideway, guideway switches, and power distribution system must receive proper attention in the specification and design stages of a project. This paper identifies a sampling of critical aspects of interface coordination and draws upon specific examples of incomplete or incorrect interface specifications and their consequences. The author provides recommendations for interface specifications between the CRS and the associated APM subsystems.

The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory’s (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a “jurisdiction having authority,” a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL’s most recent phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during phases I, II, and III, with special emphasis on phase III results with respect to a descriptive example of the proposed SEM when a “digital twin” analytical model is used to simulate the transport fleet’s battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.

Shared autonomous vehicles (SAVs) are the result of the convergence of autonomous vehicles (AVs) and on-demand transportation. They are expected to significantly impact the mobility landscape; however, they must be thoroughly assessed prior to their widespread deployment. This study examines the repercussions of widespread use of SAVs and analyzes how it will affect the environment, public transportation, land use, and vehicle ownership. The impacts were identified and categorized in accordance with their causes, and the frequency with which they were cited in the literature was established for each. The findings demonstrate that a variety of factors influence the effects of SAVs, and while they have the potential to reduce barriers to mobility and transportation inequity, there is a need for policymakers to enact policies that will ensure that the technology facilitates improvements to the transportation system.

The global crisis precipitated by the novel coronavirus brought forth considerable psychological distress and financial strife, notwithstanding governmental economic relief. This situation highlighted the degree of dependency among international markets by demonstrating the ripple effects from disturbances in one industry to another. Within this context, the construction sector endured significant hardship, facing operational disruptions, health and safety concerns, project deferrals, cancellations, and financial discrepancies. While extensive research has been conducted on the repercussions of the pandemic on the construction sector, the specific area of cost escalation has been less explored. This research endeavors to bridge this knowledge gap by exploring the contributory factors to cost escalations within the construction domain during the COVID-19 health event. A methodical inquiry was initiated through an online questionnaire targeting construction engineers and management personnel across the United States. From this, 124 complete responses were harnessed to construct an analytical model. The results from this analysis indicated that increased labor expenses, inflationary pressures, a hike in material costs, intensified demands from vendors and producers, the financial burdens of implementing COVID-19 testing, and the presence of medical staff on-site, as well as the additional expenditure on personal protective equipment and the ambiguity in determining COVID-19 as an allowable or non-allowable delay within contracts, were pivotal in causing budget overruns in construction projects during the pandemic. The outcomes of this investigation offer critical knowledge for decision-makers and project overseers within the construction field, delivering a clearer understanding and preparation for efficiently navigating the complexities introduced by a pandemic.

The increased use of personal vehicles presents environmental challenges, prompting the exploration of public transportation as an affordable, eco-friendly alternative. However, obstacles like fixed schedules, limited routes, and extended travel times impede widespread adoption. This study investigates the temporal evolution of spatial inequality in the travel time gap between public transportation and personal vehicles, reflecting disparities across states and time periods. Analyzing Census Transportation Planning Program data for six northeastern states in 2010 and 2016 reveals no significant increase in the travel time gap, but notable growth in inequality in a few urban and disadvantaged communities. Comprehending these trends is vital for fostering equitable advancements in transportation infrastructure and enhancing public transportation competitiveness.

Transit signal priority (TSP) is a traffic management strategy to enhance the quality of public transit service while also providing substantial safety benefits. This study used a binary Bayesian logit model with random effects, which accounts for unobserved heterogeneity, to explore impacts of TSP on severity of corridor-related crashes in Florida. The analysis revealed deploying TSP was linked to lower crash severity, reducing the likelihood of fatal plus injury (FI) crash by 7.96%. The study also investigated other factors contributing to crash severity, including crash characteristics, driver characteristics, roadway geometry, and environmental factors. Distracted driving, vulnerable road users, and higher speed limits increase the risk of FI crashes, while higher average annual daily traffic (AADT) was linked to lower risk. Certain crash types, such as rear-end and sideswipe crashes, were also associated with lower risk of FI crashes. These findings hold crucial implications for transportation agencies when planning future TSP deployments.

Public transportation accessibility has a remarkable impact on the well-being of marginalized communities, especially those situated in underserved regions. However, disproportionate geographical distribution of transit services often results in disparities in accessing public transportation. Therefore, it is imperative to pinpoint areas where there exists a disparity between transit demand and supply, commonly referred to as “transit deserts,” particularly concerning disadvantaged population groups. This study investigates the spatial accessibility of public transit with a specific focus on minority communities in northeastern Illinois, US. To accomplish this, we have introduced a needs gap index, which characterizes the incongruity between transit demand and supply for different demographic groups. Our analysis is based on data from the 2020 American Community Survey, aggregated at the census tract level. The outcomes of our study provide valuable insights into the geographic pattern of transit deserts, considering both the overall population and specific minority groups, encompassing various demographics such as race, age, and income. Notably, our analysis underscores that the Hispanic community experiences the most pronounced underservice compared to other racial groups. Furthermore, this study underscores the significance of concentrating on particular demographic cohorts when identifying transit deserts, instead of solely focusing on the entire population. By doing so, we aspire to provide a more comprehensive understanding of the disparities related to transit deserts in the study area. Ultimately, our findings can be instrumental in aiding policymakers and transit authorities in formulating strategies to enhance transit accessibility and promote equity within the public transportation system throughout the region.

Bicycling is experiencing rapid growth as a mode of transportation in the United States. Cities are actively working to enhance the safety and appeal of biking, with these initiatives easing traffic congestion on roads and in public transit, promoting a cleaner environment, and encouraging physical activity. A better understanding of cyclists’ travel habits could assist researchers in examining trip frequency in various scenarios. To address this, the study employed a regression model to analyze the correlation between bikers’ race, physical activity, health, and gender as independent variables and the number of biking trips as the dependent variable. The findings revealed a significant correlation between health, gender, and physical activity, serving as independent variables, and the frequency of cycling trips, the dependent variable. However, there appears to be a minimal connection between race and the frequency of cycling journeys. This research provides valuable insights for transportation planners and decision-makers, enhancing their understanding of how health, gender, race, and physical activity impact cycling trip frequency.

Network macroscopic fundamental diagrams (MFDs) have recently been shown to exist in real-world urban traffic networks. When present, MFDs can be used to model traffic dynamics within an urban network by dividing the network into a set of spatially compact homogeneous regions and tracking the average level of congestion in each region. Existing analytical methods to estimate MFD mostly focus on the behavior of a single type of vehicle and do not capture the patterns of mixed traffic (e.g., cars and buses). The existence of buses matters since a bus will block the movements of other vehicles when it dwells at the bus stop. This paper proposes an analytical method to estimate the impact of bus dwelling on a network’s MFD based on the network’s geometric features, traffic control strategies, and bus operation parameters, and validates the performance of the proposed method using simulations based on microscopic traffic models. Comparisons of the analytical and simulation results show that the proposed analytical method can generally provide a good estimate of the lower bound and upper bound of the network’s MFD.

This paper aims to resolve confusion among track professionals regarding the shape of vertical curves on railroads, specifically whether they should be considered parabolic or circular. Through mathematical, geometric demonstrations and CAD drawing, the paper establishes the equivalence between parabolic and circular vertical curves on railroads. It derives a general formula for vertical curves that serves as the foundational source for existing formulas in reputable literature, such as the Transit Cooperative Research Program (TCRP) report and the American Railway Engineering and Maintenance-of-Way Association (AREMA) manual. The paper reviews these vertical curve formulas and recommends a maximum acceptable value for vertical acceleration. It also provides installation criterion of a vertical curve. The minimum radius required to ensure stability of a vertical curve under thermal load under unloaded condition is analyzed; this important safety aspect is ignored in literature. The paper then suggests the absolute minimum values for the radius of a vertical curve on both ballasted and direct fixation track. By addressing these aspects, the paper aims to offer a comprehensive guidance for professionals involved in designing and implementing vertical curves on railroads, ensuring safety and efficient operation of the rail network.

Shortlines are smaller railroads operating on shorter, light-density rail lines with marginal track conditions. With insufficient revenue and declining employees, shortlines depend on labor-intensive yet timely inspections to ensure their infrastructure remains safe and effective to operate. To increase track geometry inspection efficiency, research efforts have focused on developing automatic and effective rail extraction methods using LiDAR and artificial intelligence (AI), so that track geometry can be measured based on the extracted 3D rail model. However, many rail extraction methods have specific data collection settings or algorithm assumptions, lacking adaptability for deployment on shortlines with variable conditions. One recent study developed an AI-assisted rail extraction method, using publicly available Federal Railroad Administration grade crossing datasets, intended for shortlines without needing human interaction. However, this method has not yet undergone testing and validation with actual shortline data. This research collects additional LiDAR point cloud data from a shortline track to validate performance of the AI-assisted rail extraction method. The results show the method generates equally good extraction on single straight tracks and improved extraction on single curve tracks compared to the original study. This confirms the robust extraction performance of the method across different track types and sensor configuration and confirms that the method proposed by the original study is, indeed, independent of sensor property and configuration.

The implementation of emerging technologies is shaping the future across various aspects of society. Engineers face significant challenges in implementing these technologies to improve existing infrastructure systems as well as associated services. A key component for engineers is the continuous innovation and upgrading of aging infrastructure like water sewage, electric power, transportation, and communication systems, while considering both current and future technology landscapes. In addition to the advanced hardware and instruments like LiDAR (light detection and ranging), GPS (global positioning system), wireless transponders, or camera sensors, the introduction of enhanced database-integrated information, Internet of Things (IoT), digitization techniques, and building information modeling (BIM) are a few examples of emerging software technologies. Amid current design practices adhering to codes, regulations, and standards, engineers encounter difficulties in fully integrating these emerging technologies. This article explores the realm of current and future technologies as well as the complexities associated with improving rail transit infrastructure systems. This article further extends the introduction and challenges of implementing autonomous intelligence (AI), machine learning, and communications-based train control (CBTC) in the rail transit environment, enhancing the efficiency and safety of rail transit systems. This article also discusses the value engineering analysis which may sometimes necessitate a complete overhaul or replacement of intended improvement.

Recently, there has been an increase in public transportation ridership in the United States. The transit users tend to walk or bike to the transit stations. As such, more pedestrian and bicycle crashes have been reported near transit stations. Thus, understanding the pedestrian and bicycle crash characteristics may help to improve their safety near transit stations. This study evaluated the safety of pedestrians and bicyclists near commuter rail transit (CRT) stations in South Florida. The study used five years of pedestrian and bicycle crashes in South Florida. The negative binomial and zero-inflated model were used to identify factors that contribute to the crashes in the census block group (CBGs) near the CRT station. The results indicated that the residential density, distance to the nearest CRT stop, and frequency of transit service per square mile were significantly associated with the number of crashes near the CRT stations in the nearby CBGs.

The number of fatal traffic accidents at railroad crossings has decreased, but concerns persist about certain locations that remain significant safety hazards. The Britton Highway Railroad Grade Crossings (HRGC) in Oklahoma City is one such perilous intersection in Oklahoma, with 24 documented highway-railroad collisions occurring over the past 25 years. This paper conducts an in-depth safety analysis of this critical intersection, leveraging data from the HRGC inventory and crash database maintained by the Federal Railroad Administration (FRA) since the 1970s, as well as the Collision database managed by the Oklahoma Department of Transportation (ODOT) since 1998. A total of 514 highway-only crashes have been reported at the Britton intersection. A considerable portion of these incidents involved motor vehicles, with a significant number transpiring during daylight hours. It was observed that the nearby ungated HRGC, situated within 500 ft, which intersects with a local road, often served as an alternative route when the Britton HRGC was temporarily closed for passing trains. This secondary intersection witnessed eight highway-railroad accidents during the same timeframe, along with 67 highway-only crashes. Consequently, an analysis was conducted to assess the influence of traffic characteristics, highway geometry design, HRGC control devices, and other factors, to gain deeper insights into the safety implications of this HRGC. The findings in this paper could underscore the potential for reducing accident occurrences by implementing effective safety measures and supporting data-driven decision-making to enhance safety and curtail accidents at this intersection.

Despite government efforts, railroad trespassing persists, largely due to widespread unawareness of its risks. To investigate public knowledge and motivations regarding this issue, we surveyed 591 people (August 2022−May 2023). Employing descriptive statistics, for example, frequency distribution, and non-parametric tests, we preliminarily analyzed the findings in this paper. Alarmingly, 90% of respondents never received rail-related safety training, 40% believed private railroad property was public, and 48% were unaware of its illegality. Notably, 52% admitted to trespassing. Regarding the reasons for trespassing, 68% of the respondents who trespassed before reported shortening a trip, an official crossing is too far away, not convenient, or a busy highway were the main reasons. For the destinations after trespassing, 32% chose hiking, walks, and walking dogs, 26% chose going to the town/city/center, restaurant, or shopping, and another 22% reported way to (from) school/work. These findings highlight significant gaps in railroad safety education in the US.

Most railroad bridges in New England were designed and built more than a century ago with unique designs and have historic significance. The Cos Cob Bridge is one of the historic bridges of New England located in Greenwich, Connecticut. The structure is a steel truss bridge, with the members of the bridge trusses connected through gusset plates at the joints. Considering the age of the structure and its significant importance in regional transportation, it is important to understand the dynamic behavior of the bridge in response to the train loads to which it is currently subjected. This paper presents the results delineating the vertical displacement of the Cos Cob Bridge obtained from field tests, as well as that using the finite element (FE) modeling of the structure. Field test data were acquired to evaluate the vertical displacement of the bridge under several service trains passing over the bridge using the laser Doppler vibrometer. These field test results were then compared to the vertical displacement obtained from FE results performed under realistic train loading conditions using the commercial FE modeling tool, ANSYS. The FE model was developed which could depict the field test results with an accuracy of over 90%. This study aims to identify discrepancies between the field test results and the FE model analysis prediction as well as the potential areas for model refinement.

Social media networks, like X (formerly Twitter), emerged as a revolutionary means of communication, drastically changing how information is disseminated. This study attempted to synthesize the current state of social media usage by transit agencies, accomplishing three primary tasks: (1) conducting a comprehensive review of agencies’ social media practices, (2) surveying and analyzing representative United States agencies to understand their social media usage, and (3) performing text mining on selected agencies’ archived tweets. Notably, the study revealed that the COVID-19 pandemic has significantly impacted information-sharing patterns among transit agencies. The text network analysis indicated that the information shared was primarily related to services, alerts, delays, detours, and promotional offers, such as free rides. However, more recent discussions have started to include evolving topics like equity and disability-related issues. By understanding these insights, agencies can better plan and develop strategies to leverage social media effectively in their operations and communication with the public.

As ride-sharing services increasingly redefine how people move within urban areas, the curb environment (the public space between roadway and sidewalk) will have to be able to accommodate new uses and new users. This study seeks to understand how formalizing a space for curbside pick-up and drop-off (PUDO) activity typical of new transportation modes such as ride-sharing will impact traffic flow and curb use. Microscopic simulation models calibrated using data collected in Atlanta, GA, were devised, and performance metrics such as delay and occupancy rate were collected. By varying traffic flow conditions and changing the percentage of PUDO parking events, an analysis of different curb configurations was conducted, and results were compared with those from a traditional curb design. The introduction of dedicated PUDO zones created significant reductions in delay. With high utilization, these zones have the potential to reduce double-parking, increase curb utilization, and positively affect through traffic.

Traffic congestion, caused by incidents such as vehicle crashes and lane closures, has been annoying to commuters. To reduce the delay caused by unexpected incidents, a detour might be a good option even at the cost of a longer travel distance. We used the smart corridors under the integrated corridor management program: Interstate 24 (I-24) and State Route 1 (SR-1) as a case study and examined the conditions under which detour decisions should be made. We collected 460 crashes and computed the travel time of the direct route (i.e., staying on I-24) and the detour route (i.e., using a stretch of SR-1). Three different detour scenarios were identified at a departure time: strongly recommended, alternative, and not recommended. Additionally, we classified the three detour scenarios into two groups: detour and non-detour by estimating the probability of detour scenarios following 1 h after the incident occurred. A bootstrap logit regression was conducted to determine the impact of various factors on the decision to take a detour. Several important findings are: (1) One-unit increase in injuries leads to a 59.1% increase in the likelihood of taking a detour. (2) When a crash occurs in peak hours, staying on I-24 smart corridor seems to be a better choice. (3) If a crash occurs in HELP patrol area, detour is highly discouraged. This research could provide insights into incident management and give commuters suggestions about the circumstances in which detour is a good choice.

This study delves into the dynamic model of heavy vehicle motion on steep road gradients, employing a semi-empirical method to unravel the intricate interplay of various factors on vehicle performance. Guided by the principles of Newton’s second law, the model takes into account forces acting along the longitudinal, lateral, and vertical axes, incorporating critical elements such as road geometry, surface friction, and vehicle characteristics. The propulsive force, rolling resistance, aerodynamic drag, and grade resistance are examined as pivotal forces. The developed MATLAB program serves as a tool to assess the impact of key variables, including weight-to-power ratio, engine power, road conditions, truck type, and tire type, on heavy vehicle performance. Weight-to-power ratio emerges as a primary influencer, while road conditions and tire type exhibit minimal effects. This study provides valuable insights into the analysis of heavy vehicle behavior on steep upgrade sections of highways, contributing to a broader understanding of truck performance dynamics.

In this study, we propose a novel method for managing traffic lights by integrating deep Q-networks (DQN) with auto-encoders. The aim is to enhance traffic fluidity and mitigate congestion in simulated environments. To achieve this, we incorporate the average vehicle velocity as a key metric within the system’s observation space. Additionally, we employ optimization techniques such as HyperOPT and leverage the data compression features of auto-encoders to improve decision-making quality. Our approach was evaluated on a two-way, single-intersection network, subjecting to a vehicle flow rate varying between 100 and 600 vehicles per hour. We compared our hybrid DQN-auto-encoder method against a DQN-only baseline, using average waiting time as the evaluation metric. The results indicate that our model markedly outperformed the baseline, reducing the average waiting time to 212 s (standard deviation 12 s), as opposed to 340 s observed in the baseline, while a traditional non-algorithmic approach yielded an average waiting time of about 1,100 s.

Transportation project prioritization relies heavily on assessments of performance and outcome; however, inconsistencies in analytical techniques of performance measures can significantly skew the results. This study conducts a comprehensive examination of free-flow speed (FFS) estimation methods and the impact of delay threshold selection, demonstrating their pivotal roles in result interpretation and project prioritization. A comparative analysis of several FFS estimation methodologies reveals that methods based on the 85th percentile of observed speeds yield highly correlated FFS estimates, while the 95th percentile methods or simple arithmetic operations of posted speed limits produce divergent results. Moreover, the study underscores the substantial influence of congestion threshold selection on the calculated delay, exhibiting significant variation at statewide, district, and corridor levels. These thresholds not only affect the magnitude of calculated delays but also alter the perceived severity of congestion. The research results underscore the vital need for transportation agencies to adopt standardized methodologies for FFS estimation and congestion delay threshold selection in order to enable consistent, unbiased project evaluation and data-driven prioritization.

Smartphone navigation applications and in-vehicle navigation technologies provide drivers with real-time traffic conditions and route guidance among other numerous benefits. These technologies promise transformative improvement in daily commutes and prevent the possible increase in traffic delays and congestion. The limits of such benefits are dependent on the frequency of using the navigation applications, diversion rates, and the percentage compliance with the suggested re-routing. Commuters in Broward County, southeastern Florida, were surveyed to understand their preferences and the factors affecting route choice decisions. A multiple linear regression model was used to predict actual traffic diversion rates and to validate the survey results. Major findings of this research included a variety of characteristics associated with navigation applications, drivers’ behavior, and roadway facility usage. In general, navigation applications have a significant re-routing potential, and drivers are likely to divert 20% to 39% of the time. It was found that most drivers would consider a re-routing suggestion when the delay per 1-h trip is approximately 15−30 min. Finally, the results were consistent with the regression model and showed that the actual diversion rate ranged from 1% to 20%.

Traffic signal controllers are devices that regulate the timing and operation of signalized road intersections, promoting efficient flow and safety. Traffic signal controllers can fail for several reasons, but there is limited knowledge of signal controller failure mechanisms in previous research. Understanding the failure methods can help predict the lifespan of traffic signal controllers. This study was the first known attempt to analyze the full lifespan of a traffic signal controller using a fault tree model. The lifespan obtained from the literature ranged from 4 to 20 years, and fault tree analysis predicted an average lifespan of 12 years. In addition, a sensitivity analysis (±20%) was performed to address uncertainty with the inputs for fault tree analysis, which predicted a range between 9 and 15 years. Transportation agencies can use life span predictions to estimate funding needs and improve asset management practices.