International Conference on Transportation and Development 2023: Transportation Planning, Operations, and Transit

As the US Aviation Climate Action Plan sets out to achieve net-zero greenhouse gas emissions by 2050, it becomes important to understand how airports in the US view sustainability in the aftermath of COVID-19 and the novel sustainable practices they have implemented. The authors have recently undertaken a study to explore management practices and emerging technologies utilized by airports in addressing these challenges. This paper explores the current sustainability practices utilized at western US airports and the use of various types of technologies and solutions that can have positive impacts on improving the sustainable operations of airports of different sizes. The regulatory pressures in the West help push forward new solutions and techniques and make this study significant for all airports across the US. The survey results for 30 airports provide important categorical insights into the status of sustainability management practices and the use of technologies and other solutions.

In order to meet passengers’ demands for increasing travel efficiency, the structure of China’s airport network will become more and more complex, and its operating environment has become more complex, so, it is important to measure the robustness of China’s airport network under the influence of complex disturbance factors. This paper proposes an airport network robustness analysis method based on the multi-layer temporal complex network model (MTNM) by introducing the time attribute. Firstly, we establish the network structure characteristics such as temporal degree, temporal closeness centrality (TC), and temporal betweenness centrality (TB) of airport network nodes, and take temporal network efficiency and temporal maximum connected subgraph as indicators; then inflection points of loss fitting curves for temporal network characteristics metrics under random attacks and deliberate attacks can be identified to evaluate network robustness. The results show that compared with static network, using multi-layer temporal network model can describe the performance characteristics of the network more accurately.

This research proposes the volume delay function (VDF), which includes reliability [VD(R)F]. The VD(R)F tests a reliability term that is a function of the current VDF, while the VDF has traditionally considered to be the travel time index (TTI), and it is proved that it should be the planning index (PI) as used in reliability. The performance of the proposed VD(R)F was compared with BPR in terms of reducing errors in count, speed, and travel time estimation in corridor and link level. It was found that the VD(R)F increases the accuracy of traffic assignment and it works well with V/C over 1.2 compared to the BPR. The performance also was studied on route choice, and it was found that VD(R)F may represent better traffic detour compared to the BPR.

Studies of US Energy Information Administration indicate that transportation sector contributes 26% of total energy consumption, and 90% of total energy used for transportation is petroleum products. Among alternative fuel vehicles which use the technologies powering an engine that does not solely involve petroleum, electric vehicle (EV) has hence become a high priority for many governments and vehicle manufacturers around the world. However, a massive market penetration of EVs has invited urgent need of charging infrastructure. To support smart planning and deployment of the charging infrastructure systems and facilities, charging demand forecasting model has drawn a wider attention to researchers and practitioners, including planners and policy makers. This paper presents a comprehensive and in-depth literature review of the relevant studies that have identified various types of charging infrastructure versus facility areas, needs, as well as contributing factors that forms the basis of modeling charging demand forecasts.

Technological advancements in information, telecommunication, and supply chains have been bringing great changes to the fields of human travel behavior and transportation systems. This, in turn, affects transportation-related issues such as traffic congestion, energy use, and emissions. Shopping and errands trips represent a large percentage of human travel activity. Accordingly, changes in these trips could have significant impacts on our transportation systems. Various studies have investigated trip rates of online and in-store shopping. However, there appears to be a gap in the literature on studies that explore trends of activity times of online and in-store shopping. This paper presents the results of estimating trends of the amount of time a person spends each day engaging in online and in-store shopping activities. The developed trends are based on annual data from the American Time Use Survey (ATUS), from 2003 until 2019. Activity of online and in-store shopping activity was defined using three measures: (1) average online and in-store shopping time per person per day, (2) percentage of online and in-store shopping activities, and (3) average length of online and in-store shopping activities. Results of this work indicate clear and consistent evidence (across all three measures) of a slow but continuous increase in online-shopping and decline in in-store shopping activity.

This paper addresses the question “What is the post-pandemic travel behavior at the University of Texas at Austin (UT Austin)?” It is based on a travel preference survey distributed to the whole UT community during the spring of 2022. The respondents were asked to report their commuting to and from the UT campus/research centers after the university announcement to resume all in-person classes and campus operations. The questions included information about frequency, time, and purpose of commute as well as the mode of transportation used. The results of the survey clearly illustrate the persistence of the hybrid mode of operation and the failure to return to full-in person operations. About 43% of undergraduate students, 34% of graduate students, 28% of faculty, and 28% of staff commuted five days a week to campus. 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. To understand how travel patterns are evolving post-COVID-19, this survey could be repeated annually to analyze the shift in travel behavior as we gradually move to the “new normal” for operating conditions. This survey will help researchers and decision makers better develop travel demand modeling assumptions.

Evacuate efficiently from risk areas during an imminent rapid-onset disaster can significantly impact life safety. However, understanding the impact of mode choice during evacuation remains insufficient. The recent development of computer simulation methods, such as agent-based model, provides opportunities for researchers and emergency managers to explore this question and test various scenarios to prepare and plan for future events. This study analyzes the simulation results from tsunami and wildfire evacuation simulation case studies and compares them with empirical evidence from questionnaire surveys and evacuation drills. The findings show that there is the “sweet spot” of mode split to reach the best evacuation outcome across different communities and transportation networks. Simulation studies uncover that total mortality reaches the lowest in both tsunami and wildfire evacuations when 70%–80% of people evacuate by foot, and the rest of the people evacuate by vehicles. However, the empirical survey data reveal people may not comply with the protective action recommendations. Our research also suggests that information and knowledge play a critical role in mode choice; thus, risk communication is still the cost-effective way to improve disaster response. The insights from this study can help emergency managers enhance the effectiveness of communities’ rapid-onset disaster evacuation plans and response.

Electric scooters (e-scooters) have become popular in many urban areas around the world due to their contribution in the reduction of environmental impacts and the convenience to purchase and use them. Due to their innovative role within the transport system, a simulation software for modeling their dual behavior is not yet available. This research aims to develop an agent-based model (ABM) for e-scooters by incorporating users’ perceived safety. Perceived safety differs per urban road and individual; thus it is modeled for three transport modes that may perform first/mile trips: car, walking, and e-scooter. The resulting model is integrated in MATSim platform, and it is used to evaluate different travel scenarios within the central Athens, Greece. Results show that the perceived safety impacts differently the evaluated transport mode users. In small networks, perceived safety does not affect routing behavior of car drivers compared to e-scooter users, who change routes when experiencing safety discontinuities.

As a famous step in the transportation planning process, the mode choice problem has been traditionally studied by employing the concept of random utility optimization in the last 50 years. More recently and especially in the past decade, the emergence of big data and the computational power and insights brought by machine learning techniques has resulted in incorporating data-driven approaches that can lead to more accurate predictions. In this study, we developed travel mode choice models for commute trips in Chicago. Our data set is obtained from the Chicago Metropolitan Agency for Planning (CMAP) travel tracker survey conducted in 2018–2019, including approximately 12,000 households’ activity and travel records for 24 h. We also employed Google Maps Direction API to calculate travel times. We first constructed the home-based tour of each person’s trips to detect the main choice and eliminate the redundant non-choice trips. Then, we estimated a multinomial logit model to characterize the underlying factors that affect the decision of people in selecting commute travel mode. As one of our contributions and for the first time in Chicago, we have considered seven travel modes, including walking, biking, walking to transit, driving to transit (i.e., park and ride), auto driver, auto passenger, and TNC in the choice set. We also analyze the value of time to draw a comparison between auto, transit, and TNC travel modes. Moreover, we employed machine learning classifiers to model the mode choice problem and compare the performance of these models with the econometric model. Also, a feature importance analysis is performed to further scrutinize the most determining factors of the model prediction. The results of this study can improve the existing mode choice model of Chicago through the inclusion of an emerging mode (i.e., TNC) and better representation of existing modes using supply and operational characteristics.

Increasing cost as well as cost overrun in road projects is a worldwide reality, and efforts to determine cost drivers have therefore been a primary exercise undertaken by the stakeholders and even those with academic interests. Identifying the primary cost drivers becomes important because models to predict unit cost of road construction projects are generally pivoted around them. Given the established advantages of ANN models in dealing with noisy data as well as nonlinear relationships between the variables, this paper outlines an approach based on the relative importance of each variable, which can be used to rank the variables and decide on the primary cost drivers for road construction. Such relative importance is determined from information stored in the connection-weights of an ANN model, employing Garson’s algorithm. In addition, the paper shows that the principal component analysis (PCA) gives further insights, thereby complementing the determined relative importance. Now, one of the main reasons that the stakeholders are interested in knowing the primary drivers is that, once known, then the focus can shift to factors which dictate the cost for each individual cost driver. Given that variations in cost (for any particular cost driver) are contingent upon three factors, namely, bid-quantity, bided-rates, and project-length, it becomes imperative that questions are asked on how much they influence the cost (variations in the cost data) and also their relative influence. The paper demonstrates that a similar relative importance approach using connection-weights of an ANN model provides a means to flag the extents to which each of the aforesaid three factors impacts variations witnessed in the cost of any individual cost-driver. The results further demonstrate that the length of a project is indeed an important parameter, which lends a scale-effect when it comes to unit cost (cost per unit length), corroborating the widely perceived view that smaller projects tend to have a larger unit cost.

The State Highway 20 (SH 20) Alameda Avenue Corridor Study, led by the Texas Department of Transportation (TxDOT) and performed by Jacobs, evaluated the operational, safety, and aesthetic/community needs for a 35-mi stretch of SH 20 (Alameda Avenue) extending from downtown El Paso to Tornillo, TX. Jacobs developed VISSIM traffic models for existing and future conditions to demonstrate operational issues and proposed improvements. The VISSIM simulation data was then integrated into a 3D digital model of the main intersections along the corridor, including roadway aesthetics, and surrounding landscape and buildings. This digital model was used in conjunction with real-time game engine technology to develop an augmented reality (AR) application that let the public visualize the corridor improvements along with their impact on traffic operations and safety. Using handheld tablets and “targets” on roll plots at public meetings, patrons were able to hover over the roll plot and see the traffic animations come to life for that specific area.

Mobile devices (e.g., tablets and smartphones) have become increasingly popular for collecting and processing transportation data for various purposes without the need for complicated systems that require operator training. In recent years, several state departments of transportation (state DOTs) have equipped snowplow fleets with different systems to communicate road condition images to assist winter operations supervisors in making operational decisions during winter storms. The existing systems usually consist of several pieces of equipment and automatically transmit the road condition images at only predetermined intervals. They are expensive because they require multiple pieces of equipment installed in the snowplows. Furthermore, existing systems do not enable supervisors in the office to request images if the received images are insufficient to determine the conditions of the roads. This study proposes a cost-effective and easy-to-use mobile-based system for operators to collect on-demand road condition images from fields or offices using the capabilities of mobile devices. The proposed data collection system was implemented in the TxDOT Wichita Falls District for the 2021–2022 winter season. The collected images were visualized in an ArcGIS map-based interface and assisted winter operations supervisors in asset management and operational decisions during winter storms. The results revealed that even operators with little experience operating technological devices could still operate the system using mobile devices. Moreover, these devices could be easily removed from the fixed mounts in snowplows to capture images that were not directly in front of the snowplows. The developed system also facilitated requesting road condition images by supervisors in offices using a web-based add-on when the received images are insufficient to determine the state of the roads. The proposed system simplifies the on-demand data collection process for operators and eliminates the need to conduct extensive training for operators.

In 2021, the Speed Limit Setting Procedure (SLS-Procedure) and Speed Limit Setting Tool (SLS-Tool) were developed as part of the National Cooperative Highway Research Program (NCHRP) Project 17-76. The SLS-Procedure and SLS-Tool emphasize the importance of context sensitivity during the speed limit suggestion process. To evaluate the potential for implementation on Utah roads, the research team analyzed 29 locations that were divided into 66 uniform segments that could be individually analyzed with the SLS-Tool. Then, the suggested speed limits that resulted from the SLS-Tool were compared to the recommendations from the Utah Department of Transportation (UDOT) speed studies. The tool suggested using the 50th percentile speed in 54% of segments due to various roadway conditions resulting in a lower speed limit for 23% of segments. This resource can aid an analyst in complying with state guidelines on when to depart from the standard 85th percentile speed requirement.

At roundabouts, a driver’s decision to accept or refuse a gap has safety implications. Many factors influence the decision, including roundabout geometry, gender, age, driving experience, vehicle type, and traffic flow circumstances. This study looks at the effect of circulating flow rate and delays on a driver’s choice to accept or reject a gap. The required data included the accepted gap, rejected gap, circulating flow rates, and delay. More than 12,000 gap measurements were acquired using a video camera approach. A frame-by-frame method has been used to analyze the recorded videos to extract the data required for further analysis. These data were analyzed based on vehicle type and lane direction. The results demonstrate that delay and circulating flow rate have a significant impact on driver behavior. Higher circulating flow leads to higher delays; consequently, drivers accept a shorter gap. The critical gaps obtained for motorcycles, light vehicles, and heavy vehicles are 2.72 s, 3.25 s, and 4.5 s, respectively. Meanwhile, the observed critical gaps for the right and left lanes are 3.25 s and 2.82 s, respectively. The output of this study provides a vital base for understanding the impatience and aggressiveness of a driver’s behavior at roundabout entry, which has a significant influence on roundabout performance and safety.

The emergence of connected and autonomous vehicles (CAVs) provides an emerging CAV-generated data environment in which the late merge metering strategies can be explored to improve mobility and safety of a work zone through deploying a temporary integrating signal metering system. This study examines the safety and mobility of three late merge metering strategies; that is, the fixed-cycle late merge metering (FMM) and cooperative late merge metering (CMM) strategies perform in mixed connected vehicle environments, by comparing with the conventional late merge metering (LMM) strategy. This study tests a four-lane highway segment with one closed lane while the standard traffic signs are arranged according to MUTCD control strategies. The simulation-based outcomes indicate that under saturated traffic the CMM strategies can increase mobility under high CV penetrations and FMM can do it under low CAV penetrations. The CMM leads to safer merging area behaviors when traffic volume is approaching the capacity.

The understanding of crowd dynamics and pedestrian traffic behavior in congested areas requires increased attention to ensure better user safety and convenience. This study focused on microscopic and macroscopic pedestrian flow characteristics at bends and junctions to determine the individual and collective speed, flow rate, and trajectory based on empirical analyses. A series of experiments were conducted on a pedestrian group, all university students, belonging to the 21–24 age group, through angled corridors of 0, 45, 60, 90, 135, and 180 degrees. The participants were asked to slow walk, fast walk, and slow run in the designated corridor both singly and collectively. The entire procedure was video recorded and analyzed by Traffic Data Extractor (TDE) tool, a video image to trajectory extraction software. Besides, average speed and pedestrian flow were also calculated for different angles and walking conditions. The results showed that pedestrians’ speed decreases with the increase of the angle for both solo and collective walking. The average speed of collective walking was observed to be less than solo walking in all cases. Also, the trajectory of collective walking exhibited a more scattered pattern than solo walking. After passing the turning angle, a change in desired walking direction was observed among individuals in the group because of the congestion at the bend; thereby, overtaking phenomena became very frequent. However, the common tendency to lean toward the inner core while crossing the junction was found absent among the study group. The results obtained from this study can be beneficial to policymakers while assessing or designing pedestrian traffic management systems.

As an emerging short-distance mobility mode, the shared e-scooter system has been studied extensively; however, an unexplored question going forward is how equitably this system is accessible among different population groups. In this study, we utilize spatial analyses to identify disparities in accessing e-scooters during the Chicago 2019 pilot program. Moreover, previous studies assessing spatial accessibility of micromobility mainly use highly aggregated data overlooking the heterogeneity within spatial units. To address this issue, we employ a block group-level data set comprising location records of the e-scooters in the pilot program, socio-demographics, and built environment factors. Further, our approach constitutes both horizontal and vertical equity perspectives. To assess horizontal equity, we apply Lorenz curve analysis to characterize the spatial distribution of e-scooters. Subsequently, from a vertical equity perspective, we employ ordinary least square and geographically weighted regression models to investigate the underlying factors associated with e-scooter accessibility within the pilot area. The findings show a wide racial gap in e-scooter accessibility, where Asian areas have better accessibility than Hispanic areas with associated Gini indices of 0.467, and 0.752, respectively. Further, the GWR results revealed disparities in e-scooter accessibility for females, the elderly, and the Hispanic race. The findings of this study provide valuable lessons from Chicago’s 2019 e-scooter pilot program and can contribute to improving equity in e-scooter accessibility for future pilot programs.

Pedestrian and bicycle safety is among the top priorities of many departments of transportation (DOTs), and speeding is a major cause of pedestrian injuries and fatalities. This study investigated the impacts of traffic signal progression speed management and progression quality on the safety of pedestrians and bicyclists. A cross-sectional study design was used to estimate the random parameter negative binomial models and the safety effects of these measures. The study findings show the pronounced impacts of well-designed progression speed management with good progression quality on reducing the frequency and severity of pedestrian and bicycle crashes. Improving poor progression speed management to good or great progression speed management with signal retiming can reduce pedestrian and bicycle crash frequency by 55% and severe pedestrian and bicycle crashes by 75%. Improving poor progression quality to good progression quality can decrease pedestrian and bicycle crash frequency by 52% and severe pedestrian and bicycle crashes by 72%.

This exploratory study’s goal was to identify factors associated with suspected alcohol/drug impairment among active transportation mode users involved in crashes. Crash and other spatial data were assembled and joined for over 7,000 bicycle and 9,000 pedestrian crashes with motor vehicles that occurred in Utah over a 12-year period (2010–2021). Overall, 56 bicycle crashes (0.8%) and 220 pedestrian crashes (2.4%) involving an impaired non-motorized were identified from crash data. To identify associated factors, bivariate and multivariate analyses were conducted. For both bicycle and pedestrian crashes, impairment was more likely for older adults, on weekends, overnight, and in places with lower intersection density. For bicycle crashes, impairment was more likely in areas with smaller household sizes and with more liquor stores. For pedestrian crashes, impairment was more likely in the dark and in rural areas. While temporal factors are consistent with expectations about alcohol consumption on nights and weekends, locational findings indicate greater suspicion of impairment in places where walking and bicycling are less expected (rural areas and auto-oriented environments).

A large share of projects funded by state departments of transportation (DOTs) are for pavement preservation, which intends to maintain roadway pavement condition above a minimum acceptable level. Complete Streets is typically considered as opportunities to accommodate all road users and has been perceived as having conflicts with pavement preservation. To improve Complete Streets’ implementation, this paper reviews how state DOTs in the US integrate the Complete Streets concept into their pavement preservation programs in practice. Based on the review, the overall strategies for concept integration include low-cost safety improvements, road diets, and project upgrades. Major documents with policy implementation inputs and activities that support concept integration to achieve intended results were analyzed (i.e., cooperation, funding, staff training, and performance measure development). This review provides valuable insights regarding the critical challenges preventing effective concept integration in practice and introduces practical solutions implemented by some state DOTs.

Traffic conflicts and surrogate safety measures (SSM) have been used as an alternative to crash-based methods to study roadway safety. Extreme value theory (EVT) offers a modeling framework that can be used to expand the use of SSM to conduct proactive safety evaluations. This study explores the EVT modeling approach to analyze vehicle-pedestrian interactions and compare safety risks between a site with a right-turn flashing yellow arrow (RT FYA) indication and sites with a permissive circular green indication. Using trajectory data extracted from video using a frame-by-frame analysis approach, post-encroachment-time (PET) values were determined along with an obstructed right turn time (O_RTT) measure, which is defined as the time it takes a vehicle to complete a right turn maneuver when a conflicting pedestrian is present. At-site univariate and bivariate extreme value theory models were developed using the block maxima (BM) approach and the peak over threshold (POT) approach. Additionally, joint-site univariate and bivariate Bayesian hierarchical models were developed for each approach. Using the resulting estimates, the number of crashes was estimated for each model and compared to the observed crashes. Results showed that models using O_RTT produced a better fit model with their covariates indicating that O_RTT helps describe traffic interactions objectively. The number of crashes estimated from the Bayesian hierarchical models was found to also be closer to the observed number of crashes than those from other models. Particularly, bivariate Bayesian hierarchical models outperformed the at-site models (univariate and bivariate) and the univariate joint-site model in terms of crash estimation.

Approximately half of all traffic-related casualties across the globe involve vulnerable road users such as pedestrians, bikers, and bicyclists. The National Highway Traffic Safety Administration (NHTSA) reported that in the United States, crashes involving school buses resulted in a total of 111 fatalities in 2019. Specifically, almost one-sixth of the pedestrians involved in motor vehicle crashes in New Jersey over the last five years (2016–2020) were less than 18 years of age. Although various initiatives have been implemented to boost the safety of school children, crashes involving school children are still worrisome, leading to an emerging national issue. Finding unknown patterns in a complex, multivariate crash data set is often difficult for supervised algorithms, as they rely on a limited number of predetermined premises. Unlike supervised algorithms, unsupervised algorithms can find the relevant inherent trends between the variables and crash severity. Thus, to figure out the causes of these young pedestrian crashes, this study obtained and investigated five years of crash data sets (2014–2019) in New Jersey school districts using a multilayer artificial neural network. Shapley Additive Explanation of SHAP values of the top contributing factors is further performed to assess the impact of those factors on the crash severity. The analysis will help interpret the potential causes of the crashes, suggest countermeasures, and create awareness in combating pedestrian crashes in school districts.

India’s electric vehicle (EV) market penetration is relatively low compared to its counterparts. An online questionaries survey was done by adopting the unified theory of acceptance and use of technology (UTAUT) model, which included socio-demographic traits and public perception. Cluster analysis was done using hierarchical cluster analysis (HCA) with multiple correspondence analysis (MCA) on socio-demographic data, and factor analysis was conducted for perception-related variables that affect e-cars’ adoption. Kaiser-Meyer Olkin (KMO) measure of sample adequacy test was done. From the relation between socio-demographic and other influencing factors, the economy was a dominant factor for the elder age and middle to the low-income groups. Battery concerns were prevalent for the young-age, low-income group, while performance features with infrastructure facilities were essential for the high-income group. Moreover, public paid interest on the availability of facilities such as battery swap technology, charging facilities, or incentives which may boost the demand for e-cars.

The European Green Deal aims to reduce greenhouse gas emissions (GHG) to 90% by 2050, of which the transport sector currently contributes around 34%. Innovation in advanced vehicle powertrain technology and the massive use of energy from renewable sources are key factors in achieving these goals. Within this framework, the passenger rail transport system plays a strategic role. This paper discusses and evaluates the application fields and energy options of the hydrogen carrier in passenger rail transport on non-electrified railway lines where highly polluting diesel trains currently operate. In this specific case, the use of “green” hydrogen for vehicle traction seems to be one of the most promising and sustainable approaches, as the entire production and use cycle is GHG emissions-free. In this paper, a power train configuration without electrochemical batteries is proposed for the traction of rail vehicles. As a case study, a railway train powered by a hydrogen fuel cell hybridized with a flywheel energy storage system (FESS) running on an existing Italian non-electrified suburban line is simulated using a numerical model. Finally, the hydrogen consumption for traction requirements is carried out. The results show that, compared to a similar diesel train, a hydrogen train allows traction energy and emissions saving to be reached.

This study presents a California-focused comprehensive GHG emissions analysis, at an individual level and within the context of the transportation sector, by comparing public and shared means of transportation with private transportation. The main objective is to identify the threshold in which the number of passengers in a public mode of transportation, such as bus or rail, results in a lower GHG emissions per person than private transportation users. Since the number of emissions varies by location depending on multiple factors, such as population size, economic, industrial activity, etc., this study delineated the state of California as the area to be observed. Also, by using GHG emissions per person while using private transportation over one year, the corresponding thresholds in which public transportation becomes more efficient, in terms of fewer emissions, can be found. Generally, there’s always the perception that public transportation, in general, can result in lower GHG emissions. However, following the data analysis, this study has concluded that, for a bus ride to have a better GHG emissions than a passenger car, the minimum number of passengers needs to be, at least, six. For rail transportation, in turn, the minimum amount needs to be 13 passengers per ride. In this scenario, a person using a bus emits 43% less GHGs than a person using a car, and rail users emit 61% less than car drivers.

When evaluating pavement design and rehabilitation techniques, the importance of structural data for pavement performance modeling is widely recognized. For example, surface deflection measurements are used to determine the elastic moduli of the pavement layers and estimate the pavement remaining structural life. In the last 10 years, traffic speed deflectometer devices (TSDDs) have become recognized as the future of pavement deflection data collection. This is primarily due to their ability to collect continuous deflection data at traffic speeds, in contrast to the traditional falling weight deflectometer (FWD) systems, which have relatively low productivity rate, require traffic control, and have safety concerns for the operator and the traveling public. On this basis, the Virginia DOT has been pursuing research related to TSDDs for the past seven years. In 2017, deflection data were collected on a large portion of its pavement network using a TSDD. A rural primary four-lane divided route (US-460) was part of the collection in 2017, and one section of this road was also collected in 2022. This paper focuses on comparing the data collected in 2017 to the data collected in 2022. The required asphalt overlay thickness was also assessed using AASHTO-93 methodology. The results showed thicker asphalt overlay thickness required in 2022 compared to 2017. Some unexpected results were observed when comparing 2022 and 2017 measures deflection velocities, and vertical deflection.

The research program aims to investigate in-transit electric charging on high-capacity roads via dynamic contactless inductive charging that is adaptable to any type of vehicle. To achieve this goal an asphalt ring is built with a length of 1,050 m, fed with a power of 1 MW. First, the methodology for incorporating “dynamic wireless power transfer” technology to the different main ranges of electric vehicles (cars, buses, trucks) is evaluated. Then, the ring asphalt pavement construction is to study the interaction of the multiple variables that make up the DWPT system. The route has been divided into seven different typological sections, characterized by different construction details and materials. The purpose is to analyze the behavior of the system in terms of efficiency on different types of road network. The bitumen used for the mixtures is a modified hard with added fibers and innovative antioxidant additives in order to be able to ensure controlled aging under the inductive load of electromagnetic fields. The system will be ensured with advanced 5G connectivity in a view to guarantee maximum road safety and V2I communications. The next step concerns the optimization of the road pavement, in terms of the various component elements and mix design materials, for the purpose to make it more durable and not alter the efficiency of the inductive load. Simultaneously and at the conclusion of each experimental phase, benefits and environmental impacts will be evaluated.

There is a global belief that the electrification of our transportation systems will help make significant savings in energy use, and in greenhouse gas and air pollution emissions. Both the literature and practice are full of numerous articles and reports analyzing different aspects of benefits and costs resulting from such electrification. However, the size of the literature discussing the possible impacts of electrifying vehicles in our agricultural industry does not seem to be as abundant. This paper presents a well-to-wheel analysis that attempts to estimate greenhouse gas and air pollution impacts resulting from converting the agricultural tractors and trucks to electric. Two different models are employed in the estimations, one developed by the research team using national averages, and a second model that is developed by the California Air Resources Board (CARB). Additionally, the research team conducted field visits, recorded observations, and gathered field data for the use and performance of different diesel and electric tractors over a year at a farm in Reedley City in California. The analysis and impact estimates are developed for two geographic regions: California and the US. Results of all conducted analyses indicate significant reductions in greenhouse gas and air pollution emissions.

Urban transportation is the crux of economic growth in both industrialized and emerging nations, but it is also a significant contributor to energy usage, poor air quality, health impacts, and traffic problems. Sustainable mobility is one of the most comprehensive techniques for combating this global climate change. Hence, the decarbonization of the transportation sector by deploying electric vehicles (EV) and shared mobility is currently an environmentally friendly and cost-effective solution. This study sought to review the studies conducted on shared mobility and electric vehicles. A thorough literature search revealed that the EV encompasses the hybrid EV, the plug-in hybrid EV, and the battery EV. EVs also provide various advantages over conventional vehicles, including zero greenhouse gas emissions, ease, reliability, affordability, convenience, efficiency, and connectivity. In contrast, the most prevalent forms of shared mobility include car-sharing, micromobility, ridesharing, ridesourcing, and microtransit. The implementation of shared mobility helps reduce the difficulties created by pollution and traffic congestion by lowering the number of vehicles in transit, congestion, and the release of polluting gases in metropolitan areas. Combining shared mobility and EVs may accelerate acceptance of both and minimize energy usage and GHG emissions by relying on smaller, efficient shared EVs. In the future, we may anticipate the emergence of many car-sharing programs utilizing electric vehicles. The findings of this research can be utilized in long-range planning for shared electric programs.

Autonomous vehicles (AVs) have the potential to lower the number of vehicular crashes, as research has shown that most collisions are caused by driver error. Consequently, several companies in the US and other countries have conducted pilot tests on public highways to expedite the mainstream deployment of AVs and examine the effects that they have on traffic safety. Although a few studies have used AV crash data to attempt this task, their sample sizes were too small, and they failed to explore many of the factors causing the crashes. This study aims to analyze the characteristics of the collisions in terms of frequency per annum; company and make of AV; and vehicular, environmental, and roadway factors. The crash data, gathered for this research from the California Department of Motor Vehicles, pertains to the characteristics of 259 collisions involving AVs that occurred in California between September 2014 and June 2020 and reveals a sharp increase in the number of crashes after 2017. AVs were at fault for a small proportion of them, as most of them were rear-end collisions that occurred when they were operating in the autonomous mode, and the number of passengers sustaining a major injury was very low. Environmental factors such as weather and lightening did not have a significant impact, as the data shows that most of the accidents occurred in clear weather during daylight hours. The results of this study will help transportation experts more fully understand the advantages and disadvantages of autonomous vehicles by providing a comprehensive picture of collisions involving them.

The adverse impact of the coronavirus (COVID-19) on the health and safety of the construction industry has been studied by many researchers, but few have identified strategies for mitigating its effects and correlated them with the size of an organization. This research aims to identify the precautionary practices implemented by small, medium, and large construction companies and determine how much the size of the company influences the frequency with which they are practiced. To achieve this goal, an extensive literature search was performed and was followed by a questionnaire survey. A total of 63 responses to the survey were received and statistically analyzed, revealing that the most common practices were encouraging sick workers to stay at home, initiating health and safety procedures on job sites, providing paid sick leave, and instructing workers to wear personal protective equipment (PPE). The analysis also found that an organization’s size significantly influences how frequently the practices are executed. The study results will assist researchers and professionals in understanding how often the identified precaution practices were implemented by small, medium, and large-scale construction companies during the pandemic and will help them develop similar techniques for future health crises.

Proper pavement markings are essential in enhancing roadway users’ safety. This paper is to (1) carefully synthesize the usage of various types of pavement marking materials, (2) develop recommendations concerning different US states and several foreign countries, and (3) compare them with pavement marking practices by the Texas Department of Transportation (TxDOT). A comprehensive online survey collected a wide range of practical information for marking material types and quality control approaches, types of specifications, and application rate verification from field engineers of various US states and other countries. In addition, manuals and research papers/reports related to pavement marking materials for other state-level departments of transportation and foreign countries have been thoroughly reviewed and synthesized. The research team recommends adopting the best practices to improve the TxDOT existing handbook. This will promote the use of new marking materials and technologies and extend the service life of pavement markings in Texas.

The technological advancement brought forward by Construction 4.0 has been slowly transforming the industry. While many studies focused on vertical projects and private organizations, very few have been tailored to the horizontal sector and departments of transportation (DOTs). This study addresses the gap by utilizing the technology, organization, environment (TOE) adoption model to identify the critical technological, organizational, and environmental factors that influence the decision-making process when DOTs adopt technology. Twenty-eight factors were identified and defined from literature and evaluated via a survey distributed to DOT professionals. Statistical tests were employed to classify the factors using the MoSCoW method into “must be,” “should be,” “could be,” and “would be” factors. Findings can be important for both researchers and practitioners when understanding the decision-making process for technology adoption and can lay the foundation for tools and frameworks that enhance the technology adoption and implementation process at the DOT level.

Highway transportation systems are prone to disruptions due to the increasing threat posed by extreme rainfall on the stability of roadside embankments and cut slopes. The failure of roadside slopes leads to system-level traffic disruptions, increases travel times, and demands emergency repairs. Proactively identifying and rehabilitating critical roadside slopes with a limited budget is vital for improving the performance and safety of the highway transportation system. This research aims to create a risk-averse decision framework to support the proactive rehabilitation of roadside slopes with a limited rehabilitation budget. The proposed risk-averse decision will facilitate the proactive slope rehabilitation decisions ensuring the expected generalized cost (i.e., cost of emergency repair and traffic disruption), and conditional value at risk (CVaR) linked to the rehabilitation decision is reduced during extreme precipitation. The proposed rehabilitation decision framework was implemented in a highway network in North Texas. The result shows that the proposed approach identified a list of rehabilitation policies with different expected failure costs and conditional values at risk (CVaR) along the Pareto efficient frontier, thereby enabling transportation agencies to select a rehabilitation strategy at desired risk aversion level. The proposed decision framework aids transportation agencies to make the best use of maintenance funding to improve the serviceability of highway transportation systems during extreme rainfall events.

Poor-performing bridge deck drains result in water standing on the bridge deck. The standing water threatens the safety of bridge users and deteriorates bridge structural elements. Identifying the problems with deck drains helps transportation agencies minimize the consequences of poor drainage. Anecdotal evidence shows that deck drains are not in good condition. However, the root and extent of the problems and countering methods are unclear. The objectives of this research are to synthesize the current state of practice on bridge deck drains to understand the extent of bridge deck drains’ problems and to provide recommendations for the design, construction, and maintenance of these systems. The methodology is comprised of creating and distributing structured surveys made up of questions related to the failures, designs, construction, and maintenance and inspection of bridge deck drains to evaluate the current state of practices related to bridge deck drains in Texas and other states. The surveys were distributed to personnel across all 25 TxDOT districts, as well as staff from state DOTs across the United States. Upon analyzing the survey results (responses from 17 TxDOT districts, 21 states, District of Columbia, and Quebec, Canada), it became apparent that deck drains face many problems after installation. For example, broken grates would allow larger and heavier debris to reach the underlying PVC pipe, which could result in the pipe breaking over a period of time. A set of recommendations were offered based on the survey results as well as available literature that addresses the lifecycle of bridge deck drains (from design to periodic inspections). One of the most important recommendations is to develop a rigorous asset management process to help track the condition of the different components of bridge deck drains. The inspections should be conducted annually before the winter season to ensure the systems will perform when necessary.

Cairo Monorail line is one of the largest transportation projects in the Middle East. The project involves the construction of two monorail lines, which will create the first public transport connection from both New Capital City (approximately 54.2 km) and 6th of October City (approximately 42.9 km) to the Greater Cairo area. The bidding and tender stage started in 2015, followed by extensive studies performed by the contributing consortiums all the way up to the final selection was made in 2019. A consortium of Alstom Transportation, Orascom Construction, and Arab Contractors was assigned to design, construct, operate, and maintain the two lines under a $4.5 billion contract signed with Egypt’s National Authority for Tunnels in August 2019. The project has significant changes and updates to best serve the public, including comfort of ridership, minimize disruption of the traffic, production quality enhancement, and expedite construction. The alignment passed through significant changes towards optimization, which highlights the importance of extensive alignment study during the bidding stage. The construction was expedited through several techniques, which is considered as a double-edged sword between quality and time. These techniques were highlighted in the foundation construction, piers and pier caps execution, guideway beam adjustment, and assembly as well as the precast process of the guideway beams. On the other side, close coordination was performed between the construction and team and the system supplier to assure the satisfaction of all the system requirements. Finally, several learned lessons are presented, which shall be considered for future projects.

Pavement condition data is important in providing information regarding the current state of the road network and in determining the needs of maintenance and rehabilitation treatments. However, the condition data is often incomplete due to various reasons such as sensor errors and non-periodic inspection schedules. Missing data, especially data missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing methods in dealing with missing data usually discard entire data points with missing values or impute through data correlation. In this paper, we used a collective learning-based graph convolutional network, which integrates both features of adjacent sections and dependencies between observed section conditions to learn missing condition values. Unlike other variants of graph neural networks, the proposed approach is able to capture dependent relationship between the conditions of adjacent pavement sections. In the case study, pavement condition data collected from Texas Department of Transportation, Austin District, were used. Experiments show that the proposed model was able to produce promising results in imputing the missing data.

The applications of geospatial technologies and positioning data embrace every sphere of modern-day science and industry where geographical positioning matters. Among all other fields, geospatial technology plays a remarkable role in the transportation sector and has the potential to play an even more critical role in future autonomous transportation systems. In this regard, the GNSS-real-time network (GNSS-RTN) technology is promising in meeting the needs of automation in most advanced transportation applications. The GNSS-RTN is a satellite-based positioning system that uses a network of reference stations to provide centimeter-level accuracy in positioning data in real time. The technical aspect and working technology of GNSS-RTN are widely studied; however, only limited research has been conducted on the various GNSS-RTN business models currently in use nationally and internationally. Therefore, this study aims at assessing the various GNSS-RTN business models currently used in practice as well as those that are deemed potentially viable but have not yet moved to practice. Eight different business models were cataloged and used in the current assessment. All business models were assessed using three criteria: state control, sustainability, and state/agency costs. The findings of this research are important in helping state agencies make informed decisions as they build, expand, or manage their own GNSS-RTN systems.

One of the major factors contributing to the degradation of reinforced concrete slabs, especially in bridges and roads, is corrosion due to the diffusion of corrosive ions. All such infrastructure would experience different types of loading, from the moving live load caused by the vehicles to the thermal cycles and ion exposure. These ions can be internally produced due to chemical reactions or introduced to the medium of the structure from outside by spraying the brine and rock salt in the winter. In recent years, different aspects of chloride and oxygen-induced corrosion have been studied through analytical/numerical modeling or accelerated experiments. The problem with this subject is its sensitivity and dependence on many factors. In this paper, numerical simulations have been implemented to display the relative patterns and comparative rate of cracking due to the diffusion of oxygen (in a wet medium) and chloride ions (after brine spraying). This comparative approach can offer a better visual perception to the engineers about the corrosion process in field projects. Based on the results, the aggression of chloride ions in corroding the reinforcement and cracking the concrete medium is much higher than oxygen ions. Thus, the rate at which the corresponding section reaches the critical state is much faster under the attack of chloride ions, leading to a dramatic decline in the service life of infrastructure.

The electrification of all automobiles and truck fleets has been seen as an important way of reducing emissions and further sustainability efforts. This transition proves difficult for heavy-duty vehicles, which require larger batteries and more power. Among heavy-duty vehicles, class 8 drayage trucks make regular trips and have predictable operation making them an ideal candidate for electrification. With the ability to charge while driving becoming possible, dynamic wireless charging lane technology can be a means of extending feasibility and lowering barriers to electric vehicle conversion. Using the GPS trip data from 20 diesel drayage trucks from ports in Southern California, we observed route choice behavior. We then constructed trips and modeled energy consumption for all truck trips. Considering the battery life of each vehicle, we selected candidate locations for infrastructure implementation and modeled how they affect the original set of trips. The goal of this study is to provide researchers and practitioners insights into wireless charging lanes by creating a model and ways to analyze dynamic wireless charging lane behavior in conjunction with electric trucks.

Emergency declarations by the president, state governors, or the Federal Motor Carrier Safety Administration (FMCSA), temporarily suspend certain federal safety regulations. Drivers that provide “direct assistance” to an “emergency” are exempt from applicable safety regulations such as hours of service limitations on their route to the emergency. Policymakers expect that the FMCSA temporary safety regulation waivers can facilitate the supply of essential items such as fuel and food in the aftermath of disasters. Particularly, fuel supply is critical for transporting people and goods to accelerate disaster recovery. Despite the significant volume of qualitative discussions on the FMCSA safety regulations, the impact of the waiver on fuel price stabilization in the post-disaster recovery process has not been elucidated. The current research examines whether the FMCSA safety regulation waiver following Hurricane Harvey in August 2017 impacted the daily fuel prices in South Texas and Louisiana. We estimated the pre- and post-differential in the regional fuel prices while controlling for the disaster effect and other factors for South Texas and Louisiana, separately. The results show that the post-disaster FMCSA regulation waiver had a statistically significant impact on regional fuel price stabilization, decreasing the daily fuel prices in South Texas and Louisiana in the aftermath of Hurricane Harvey. While the fuel prices increased by 3.74% in South Louisiana after Hurricane Harvey, the regulation waiver decreased the daily fuel prices by 2.65% and 3.47% in South Texas and Louisiana, respectively. Also, the current study investigated the short-run relationships between regional fuel prices and their key determinants. The findings are expected to assist policymakers and decision-makers in understanding the FMCSA safety regulation waiver’s impact on post-disaster fuel prices for enhancing their recovery strategies and policies.

Freight transportation represents a significant amount of traffic and all its associated externalities, such as traffic safety, congestion, energy demand, greenhouse and air pollution emissions, and infrastructure costs. However, it also plays a primary role in supply chains and costs and availability of goods and is a major player in the economy. This study aims to identify, assess, and utilize different data sources to uncover and understand the patterns and movements of the different types of freight in different counties of the San Joaquin Valley. The San Joaquin Valley region consists of eight counties, namely, San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, Kern, and Tulare. This research has explored some major data sets with freight information, such as GTA, PIERS, Transearch, and Streetlight Data Insights for the year 2019 (to explore movements of freight pre-COVID). The primary software programs used for this analysis are MS Excel, MS Access, ArcGIS, and Streetlight Insight. This research investigated all modes of freight transportation for domestic and international trade, which are air, water, rail, and road. The findings of this research demonstrate the strengths and limitations of different data sources for understanding freight movements in the San Joaquin Valley. These findings should be valuable for different government and private agencies for various use cases such as developing transportation infrastructure, freight business, and environmental assessments.

The prevalence of information and communication technologies has catalyzed the emergent on-demand meal delivery (ODMD) service. Order dispatching policies play a crucial role in determining the operational performance and environmental impact of ODMD system. In this paper, we proposed a comprehensive framework to evaluate meal delivery efficiency and quantify the corresponding environmental impact of ODMD system under different order dispatching policies. It consists of three components: (1) the real-world meal delivery operational context that is generated to simulate daily activities and travel patterns in the city of Riverside and construct the on-demand meal delivery scenarios; (2) an efficient optimization approach: rolling horizon-based adaptive large neighborhood search algorithm, to obtain order dispatching and routing decisions with the dynamic order demand and driver resources; and (3) an energy consumption and emission evaluation model developed via EMFAC to quantify the corresponding fuel consumption and pollutant emissions. With the proposed framework, three order dispatching policies are evaluated: One-Order-Per-Trip (One-O), One-Restaurant-Per-Trip (One-R), and Multi-Restaurant-Per-Trip (Multi-R). Simulation results show substantial benefits of having orders bundled in the One-R and Multi-R policies. The total travel distance is reduced by 14% and 30%, respectively, compared to the One-O policy. Meanwhile, the total number of delivery drivers is reduced by 36% and 60%, which shows great potential to relieve the urban traffic burdens, together with 14%–30% fuel consumption, GHG emissions, and criteria pollutant emissions reductions. Meanwhile, the service quality is maintained with only 1% of late orders.

Truck transportation remains the dominant mode of US freight transportation because of its advantages, such as the flexibility of accessing pickup and drop-off points and faster delivery. Because of the massive freight volume transported by trucks, understanding the effects of population and employment characteristics on truck flows is critical for better transportation planning and investment decisions. The US Federal Highway Administration published a truck travel origin-destination data set as part of the Next Generation National Household Travel Survey program. This data set contains the total number of truck trips in 2020 within and between 583 predefined zones encompassing metropolitan and nonmetropolitan statistical areas within each state and Washington, DC. In this study, origin-destination-level truck trip flow data was augmented to include zone-level population and employment characteristics from the US Census Bureau. Census population and County Business Patterns data were included. The final data set was used to train a machine learning algorithm-based model, Extreme Gradient Boosting (XGBoost), where the target variable is the number of total truck trips. Shapley Additive ExPlanation (SHAP) was adopted to explain the model results. Results showed that the distance between the zones was the most important variable and had a nonlinear relationship with truck flows.

Digital material delivery such as electronic ticketing (e-Ticketing) is a proven method for increasing the productivity of field inspectors and engineers. A wide range of studies have been conducted that focused on implementation standards, pilot testing, and challenges in the deployment of e-Ticketing technology; however, few have quantified its benefits for the highway construction sector. Therefore, the objective of this study is to quantitatively evaluate the benefits of e-Ticketing technology for highway construction projects, based on available project data and generated electronic tickets. To accomplish this objective, a literature review was performed; a survey was developed, conducted, and analyzed; and on-site project data were evaluated to identify the amount of time that could be saved in highway construction projects. The results showed that e-Ticketing technology could significantly increase the productivity of field inspectors, as it would reduce the manual ticketing process by approximately 80%, enabling inspectors and administrative workers to focus on other demanding tasks. The findings of the study also revealed that it could save up to four inspector man-hours, which is crucial in mitigating the workforce shortage faced by the sector. The time savings and increase in productivity levels would allow departments of transportation (DOTs) to focus on other emerging technologies such as digital as-builts, intelligent compaction, and drone monitoring. The findings of the study will aid DOT decision-makers and engineers in quantitatively analyzing the benefits of the e-Ticketing platform, thereby decreasing project costs, improving inspector safety, mitigating the shortage of inspectors, and focusing on other emerging technologies.

This paper deals with the threshold radius that warrants the use of single restraining rail for transit and railroads. A simple and quick assessment method is developed. A restraining rail reduces the angle of attack (AOA) to reduce the likelihood of wheel climb derailment and to reduce wear provided it is installed close enough to the inner (lower) rail to prevent flange contact with the outer (higher) running rail. Angle of attack depends on wheel base, curve radius, and free play; the threshold radius is formulated by considering these three parameters. A critical angle of attack on a circular curve is assumed to be 1.5 times the angle of attack on a tangent track to warrant a restraining rail. The assumption is rational because angle of attack of 10 mrad or more might cause derailment. The formula is validated from three different points of view, for example, by comparing the threshold radius by the suggested formula with real life examples, etc. If the radius of the curve is less than threshold radius, then the restraining rail needs to be extended into the spiral up to a point where the radius matches with the threshold radius. A formula is suggested to determine the extension of single restraining rail into the spiral subject to a minimum requirement. The threshold radius for non-conventional truck is determined to avoid restraining rail on super sharp curve.

This is demonstrated in an earlier paper that the current practice to determine the unit resistance of ballast, τ [N/cm] from resistance per tie, RPT [N] by dividing it with tie spacing is not appropriate. Ballast resistance depends on the quality of track support, which mainly depends on tamping with subsequent stabilization of track and ballast quality. The quality of track support is characterized by the characteristic length of a track. The higher the quality of track support, the shorter the characteristic length or vice versa. Thus, unit ballast resistance is inversely proportional to the characteristic length of a track. The constant of proportionality is assumed to be equal to the RPT; this leads to a relation between unit resistance of ballast and the characteristic length of a track. Using the relationship, unit ballast resistances are computed and validated by comparing with unit ballast resistance values suggested by code or used by railways. The track stiffness can be determined quickly by a simple test at site as opposed to the characteristic length. The characteristic length is related with the track stiffness. Thus, τ-value is also related with the track stiffness. Using the relationship, unit ballast resistances are also computed and validated. From Prud’homme law resistance values for concrete and wood tie in a stabilized track is related by a factor of 1.5. Based on literature and analysis done in the paper, a conservative qualification of RPT (discreet resistance of tie) and τ (unit resistance of ballast) is presented. Track professional may use the τ-values presented in the paper to estimate breathing length, rail break gap, and track buckling load. The paper would help track professionals to choose their own values too.

Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an “on-demand” mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors’ experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.

During the COVID-19 pandemic, orders such as a declaration of emergencies and shelter-in-place caused people to shift their travel behaviors to stay at home. It is unclear whether governmental orders, COVID-19 cases themselves, or a combination of both were the direct drivers causing the change to daily transit ridership. Using a popular Austin, TX, route to demonstrate the methodology, we developed three causal analysis models using the daily cumulative number of COVID-19 executive orders enacted by three levels of government as the mediation variables and COVID-19 cases to determine causality. The decrease in public transit ridership along Route 7 is partially mediated by orders from the city-level and federal-level orders, whereas orders by the state level fully explain the decrease in transit ridership, meaning, the increase in COVID-19 orders by the state governor seemed to directly decrease transit ridership. This case study shows the importance of state-level policy changes on public transit.

The METRORapid Inner Katy Project proposes an exclusive bi-directional guideway for rapid transit and regional express buses connecting riders to destinations between the Northwest Transit Center and major districts and employment centers in downtown Houston. The approximately 7.6-mi. Inner Katy Project alignment will travel through various parts of Houston from an elevated guideway parallel to Interstate 10—a major freeway facility to the downtown area where it would be interlined with existing light rail transit service along surface streets with signalized intersections. This study focused on the analysis of traffic impacts for the Interstate 10 Inner Katy corridor from west of Interstate 610 to the east of Taylor Street including the central business district ramp, which directly connects to downtown Houston. Highway Capacity Software was used to determine the anticipated performance of the proposed BRT guideway. Results showed that the proposed BRT service would have minimal impacts to no-build traffic and transit conditions.

Our ability to move about and travel is essential to our quality of life. A healthy nation is dependent on a safe, efficient, and robust transportation system accessible to all citizens, regardless of their age or personal mobility capabilities and is essential for a diverse, inclusive, and equitable transportation system. Fundamentally, bus and rail transit systems exist to provide riders with transportation options, whether a necessity or a choice. The success of our transportation systems is tied to intermodalism, the ability to travel from one mode of transportation to another. Whether to transfer between modes or simply walk to the transit stop, at some point on the journey, all transit riders are pedestrians. We extend much attention and effort to the various fixed facilities (roads and rails), flow entities (busses, trains, trolleys), and control systems (traffic signals, rail signals, autonomous systems), but relatively little attention to the pedestrian walkways between the various modes and boarding areas that allow access to the flow entities. This paper discusses that “500 feet,” the transition between modes that could be the most dangerous part of the journey for the transit user.

Social disparities in public transportation were exacerbated by the COVID-19 pandemic due to transit authorities reducing transit based on ridership demand. By not observing rider socio-demographics, transit authorities potentially left captive riders/essential workers who commute by bus without a means of travel. This raises the question “Were captive riders/essential workers able to commute by bus during the pandemic?” This research sought to answer this question with the alternative approach of geospatial extracting census data and creating partial least squares regression (PLSR) models. Composed of census demographics within a 0.25-mi radius along three bus routes in Austin, TX, that either continued, reduced, or canceled operations during the pandemic, the PLSR models profiled riders who commute via transit. Results indicated that equitable travel was provided to riders along the continued route; however, the reduction and cancellation of the other routes forced essential workers, such as people in education and healthcare, to search for alternative forms of travel to commute.

An important aspect of public bus transit is its reliability of being on-time, which has a major impact on bus ridership. Currently, there is no unified national standard to measure bus on-time performance in the United States. This paper proposes a novel approach to improve the on-time performance (OTP) of buses at transit agencies throughout the country, based on factors unique to each agency. In this study, information on 18 public transit agencies data were obtained from the National Transit Database and were verified by the authors after contacting the respective agencies. A correlation analysis was conducted to evaluate the influence of different factors on bus OTP. The results show that a higher “congestion ranking” of the cities, that is, lesser traffic, has a positive correlation with OTP, while a lower number of passengers per hour has a negative correlation with OTP, both at a 95% confidence interval. “Total miles per active vehicle” and “average speed” of the bus were found to have a positive correlation with OTP at a 90% confidence interval. This potentially suggests that, if the average speed of the bus is higher, that is, if the bus operates at its scheduled speed, total miles per active vehicle will increase, which will in turn lead to a better on-time performance. A flow diagram interpreting the results of the correlation analysis was shown, where the main bus service process was considered and the predecessors and successors which could potentially affect the final OTP were determined.

In the United States, healthcare has long been viewed as a “right,” and residents of Ohio are no exception. Socio-spatial characteristics can have an impact on access to healthcare, particularly among vulnerable groups. This article seeks to measure the spatial accessibility of Cleveland, Columbus, Cincinnati, Toledo, Akron, and Dayton using a two-step floating catchment area technique. Within cities (except Cleveland), people of vulnerable census tracts have less access to hospitals via public transportation. Other cities fail to ensure equity and accessibility for the vulnerable census tracts. This study emphasizes the issues connected with the suburbanization of poverty in Ohio’s large cities and the need to provide adequate public transportation to reach hospitals on the periphery. In addition, it shed light on the need for additional empirical research to inform the implementation of guidelines for healthcare accessibility in Ohio. Planners and policymakers can incorporate findings to ensure health accessibility.

Extensive efforts have been made to quantify equity of public transit accessibility; however, the developed measures are usually aggregated over the population masking the disparities within the disadvantaged groups. Further, there is a lack of high-resolution spatial analyses in the literature. In this study, we employ several spatial analysis techniques to assess the equity of transit accessibility, focusing on the bus, train, and Metra (i.e., commuter rail) services in Cook County at a block group level. Lorenz curve analysis reveals that the train system has the most inequitable accessibility distribution (Gini = 0.83), followed by Metra and bus with Gini indices of 0.67, and 0.46, respectively. Further, we develop a disaggregate transit equity index to compare the accessibility among the disadvantaged groups. The findings suggest that the elderly, Hispanic, and Asian communities are the most underserved groups by bus, train, and Metra services, respectively. Moreover, to better understand the characteristics of the underserved groups, we employ the ordinary least square (OLS) and geographically weighted regression (GWR) models. According to the estimation results, there are disparities in accessing public transit services for Hispanic, the elderly, and individuals with disabilities. The findings of this study can elucidate the distribution of transit accessibility among disadvantaged groups and help policymakers develop strategies to move toward an equitable public transit system in Cook County.

To best serve their communities, public-sector transportation agencies need better information about the degree to which these new mobility services complement or substitute publicly provided fixed-route mass transit service. A lack of information on transportation network company (TNC) usage is a significant barrier to efficient transportation planning and decision-making. This paper addresses the need for detailed TNC use data by developing a method to identify TNC trips, TNC drivers, and TNC passengers based on mobile device data. To this goal, the researchers have collected TNC pickup and drop-off information from city of Chicago data portal. The research team has developed a metric to define “equitable access” to transit for the TNC users. The metric is tested using the TNC database and comparable transit trips as queried from OpenTrip Planner 2 software. The team has compared how TNC drivers and passengers differ in terms of transit richness (transit service density) and in terms of transit supportiveness (housing and employment density). This paper includes numeric measures of complementarity and competition among TNC and public transit providers. The competition is defined by determining what trips were taken via TNC that could have been served with existing transit service. The TNC users are classed into two major groups—the group which had adequate spatio-temporal access to transit vs. the group that had inadequate access.

Understanding the travel challenges faced by low-income residents has always been and continues to be one of the most important transportation equity topics. This study aims to explore the mobility gaps between low-income households (HHs) and not low-income HHs, and how the gaps vary within different socio-demographic population groups in New York State (NYS). The latest National Household Travel Survey data was used as the primary data source for the analysis. The study first employed the K-prototype clustering algorithm to categorize the HHs in NYS based on their socio-demographic attributes. Five population groups were identified based on nine different household (HH) features such as HH size, vehicle ownership, and elderly status of its members. Then, the mobility differences, measured by trip frequency, trip distance, travel time, and person miles traveled, were examined among the five population groups. Results suggest that the individuals in low-income HHs consistently took fewer trips and made shorter trips compared to their not low-income counterparts in NYS. The travel distance gaps were most obvious among white HHs with more vehicles than drivers. In addition, while the population from low-income HHs made shorter trips on average (2.7 mi shorter per trip), they experienced longer travel time than those from not low-income HHs (1.8 min longer per trip). These key findings provide a deeper understanding of the travel behavior disparities between low-income and not low-income households. The findings could also support policymakers and transportation planners in addressing the critical needs of residents in low-income households in NYS and provide inputs for designing a more equitable transportation system.

The strong impact of the coronavirus (COVID-19) on the construction industry has been studied by many researchers, but few have identified the individual factors that affected the efficiency of construction projects and analyzed them based on the size of the organization administering the project. This study aims to do just that: identify the factors that altered the efficiency of construction projects during COVID-19 and analyze how they impacted small, medium, and large construction companies. To accomplish this goal, a comprehensive literature search was performed and was followed by a questionnaire survey. Sixty-three responses to the survey were statistically analyzed to determine the critical challenges to project efficiency faced during the pandemic and revealed that the most common were shortages of materials and workers, including material transportation/delivery problems and mental stress originating from an imbalance of work and life; reduced efficiency and increased absenteeism of employees due to illness were also significant. Interestingly, it was found that an organization’s size played an important role in how it was affected. The results of this study will assist researchers and professionals in understanding the difficulties that various sizes of construction companies face during a pandemic and will help them develop preventive measures for future events.

Post the COVID-19 pandemic, all industries of different sizes across nearly every state of the United States are facing unprecedented challenges trying to overcome the current labor shortages. To combat labor shortage, organizations such as transportation agencies started investigating and implementing new approaches and interventions to attract and retain new workers and support their existing workforce development. Since highway work zones are considered highly hazardous for drivers and workers who build, repair, and maintain them, a significant concern of the highway construction and maintenance sector is the workers’ safety performance. In the scope of occupational safety and health, training is one of the essential tools for informing and empowering employees about workplace hazards and increasing their awareness of safety controls to remove and minimize associated risks. Thus, this research provides a roadmap to redesign safety training specific to highway construction and maintenance employees and address safety issues beyond the OSHA 10-h training course.

Eliminating job hazards and mitigating risks is a multilevel concern for organizations to help improve their safety performance. Organizations adopted several approaches to identify and eradicate job hazards by establishing health and safety management systems such as safety talks, near-miss reporting, and analysis of leading indicators. Near-miss incidents are considered a critical metric for health and safety management systems since they present an early indicator than an injury or incident waiting to occur. Several organizations struggle to promote this reporting and fail to collect enough reports necessary for data analysis to inform safety opportunities and consider corrective measures. The Kentucky Transportation Cabinet (KYTC) invested in a well-executed near-miss reporting program but is facing challenges in enhancing its practical use. As such, this research aims to investigate and document KYTC’s employees’ perceptions, concerns, and pain points that prevent them from widely using the program. Also, identify strategies that KYTC can readily adopt to increase the use of its existing near-miss program.

The transportation industry employs a wide range of workers, from truck drivers to construction workers operating in active traffic zones. Workers are constantly exposed to unfavorable temperatures that affect their physical and mental health, making it important for them to have longer and more frequent rest periods to recover. Therefore, the objective of this study is to investigate the various factors that contribute to the need for longer breaks when working in cold temperatures. To achieve this objective, a comprehensive questionnaire survey was developed and distributed online to workers in the construction industry. Total responses gathered from the survey distribution were 100 responses. The survey data was used to develop a predictive logistic regression model. The results revealed that dexterity, ineffective cold protective clothing, and the lack of a sufficient cold protection plan are significant indicators that contribute to the need for workers for longer breaks. However, other factors, such as worker age, gender, fatigue, and mental stress, were not significant enough to contribute to the need for extended breaks. The results also indicated that reduced exposure to cold weather with sufficient protective clothing can help in improving their performance in cold temperatures. The outcomes from this study will help employers in construction industry to develop an efficient cold weather work-rest regime, as they understand the various health challenges construction workers experience in cold temperatures, as well as the factors that contribute to their need for extended recovery periods.

The coronavirus impacted various sectors and economies around the globe in a very drastic way because of its limited manpower, materials, and machines, and the construction sector is not an exemption. As the industry is the most prominently dependent on labor and material, they have experienced an adverse effect on their economy, causing delays in the schedule and ultimately leading to cost overruns. Although many studies have been conducted to understand the causes of the cost overruns among different sectors during COVID-19, no study analyzed the factors affecting cost based on the size and type of construction sector. Therefore, this study aims to identify various challenges the construction industry faced during the pandemic and analyze the identified challenges affecting project cost based on the size and type of construction sector. A comprehensive literature search was performed, followed by developing and distributing a questionnaire survey. Sixty-two responses were received and were analyzed based on the size and type of the organization. The study’s results revealed that cost overruns due to escalating and inflation of material prices and increased demand from suppliers and manufacturers were identified as the most critical challenges that affected the project cost during the pandemic. Interestingly, it was found that an organization’s size and type play a vital role in how it is affected. The results of this study will assist researchers and professionals in the industry in developing preventive measures to manage cost overruns based on the size and type of an organization during any similar pandemics in the future.

Geological and mining employees working on coal mining projects are vulnerable to accidents involving powered haulage equipment, and their safety is dependent on effective safety programs. It has been demonstrated that virtual reality training programs facilitate participants to recognize the hazards present in the workplace, but their efficacy in preventing accidents has not been proven. This study aims to evaluate the effectiveness of the training programs, not only for improving geologists’ and miners’ awareness of the hazards inherent in mining and transportation operations but also for teaching them how to avoid being harmed by them. To achieve this goal, several geology and mining workers in Texas received virtual reality training, and their knowledge was assessed before and after the training, based upon six questions that were relevant to the training’s subject matter. Their responses were descriptively evaluated, and the results revealed a substantial increase in their knowledge after the virtual reality training. The outcomes of this study will guide policymakers in developing and implementing safety procedures that will protect the well-being of geologists and other miners who operate or work around power haulage equipment.

Vehicles with a safety function for anticipating crashes in advance can enhance drivers’ ability to avoid crashes. As dashboard cameras have become a low-cost sensor device accessible to almost every vehicle, deep neural networks for crash anticipation from a dashboard camera are receiving growing interest. However, drivers’ trust in the Artificial Intelligence (AI)-enabled safety function is built on the validation of its safety enhancement toward zero deaths. This paper is motivated to establish a method that uses gaze data and corresponding measures to evaluate human drivers’ ability to anticipate crashes. A laboratory experiment is designed and performed, wherein a screen-based eye tracker collects the gaze data of six volunteers while watching 100 driving videos that include both normal and crash scenarios. Statistical analyses of the experimental data show that, on average, drivers can anticipate a crash up to 2.61 s before it occurs in this pilot study. The chance that drivers have successfully anticipated crashes before they occur is 92.8%. A state of the art AI model can anticipate crashes 1.02 s earlier than drivers on average. The study finds that crash-involving traffic agents in the driving videos can vary drivers’ instant attention level, average attention level, and spatial attention distribution. This finding supports the development of a spatial-temporal attention mechanism for AI models to strengthen their ability to anticipate crashes. Results from the comparison also suggest the development of collaborative intelligence that keeps human-in-the-loop of AI models to further enhance the reliability of AI-enabled safety functions.