International Conference on Transportation and Development 2022: Other Modes—Rail, Transit, and Aviation

The focus of this study is an examination of high-speed rail (HSR) as a cost-competitive, carbon-reducing alternative to long-distance air transport. A case study comparing HSR and air transport for cross-continental travel from Charlotte, North Carolina, to San Francisco, California, was conducted. Two potential HSR routes were identified for comparison to the air transport route and a life cycle analysis was conducted to calculate emissions for each travel option. Capacity utilization of each transport option was parameterized to evaluate the effect that loading has on per person emissions. Further, economic comparison of HSR and air travel was performed to determine what effective carbon tax may be necessary to incentivize HSR over air travel. The results indicate that the construction of HSR is likely to reduce emissions relative to air travel. The results may be useful to policymakers, researchers, and practitioners examining options for decarbonizing long-haul passenger transport.

This paper analyzes the slip temperature differential in a railway track. The formula for the slip temperature differential in a ballasted track from Lichtberger was used to derive alternate formulas in terms of longitudinal resistance and the sectional area of a rail for both ballasted and direct fixation tracks. Additionally, the formula form Esveld for the maximum slip of a rail in a fastener under elastic shear resistance was rearranged and reformulated to obtain the slip temperature differential in terms of longitudinal resistance and the sectional area of a rail for both ballasted and direct fixation tracks. The aforementioned formulas were compared, and some interesting findings were drawn after their application. The slip temperature differential was used to compute the rail neutral temperature range for natural distressing. Finally, the Transit Cooperative Research Programs’ procedure for estimating the longitudinal resistance in a direct fixation track was analyzed, and a review was called for.

Assessment of a rail break gap is important to ensure safety of rail traffic. An axial rail model available in current textbook yields a single formula in terms of breathing length to compute a rail break gap in a continuously welded rail (CWR) track. Transit Co-operative Research Program (TCRP) suggests three formulas to compute a rail break gap in a CWR track. The longitudinal resistance of a track or rail is a significant input to compute a breathing length which in turn determines a rail break gap. TCRP does not recommend value of longitudinal resistance of a track or rail but implicitly suggests dividing fastener restraint force by fastener spacing to determine longitudinal resistance value in the derivation process of its first and third formula. The theoretical and practical deficiencies of first and third formula are discussed. TCRP second formula, simplified version of the first one, appears to be acceptable provided the longitudinal resistance of a track or rail is not computed by dividing fastener restraint force by fastener spacing. The value of longitudinal resistance obtained by dividing fastener restraint force by fastener spacing is underestimated for a direct fixation track leading to a large rail break gap and is not appropriate for a ballasted track as well; an explanation for underestimation and inappropriateness is given. The track professionals would be benefitted by the clarity given in the paper. A call is made for review of TCRP formulas.

In this study the potential use of the critical temperature differential for buckling, ΔT_C, of a track alone and in conjunction with a rail neutral temperature, RNT, was explored. A brief review on ΔT_C was done for selecting an appropriate value of it. The value of ΔT_C was used to classify a track and to determine the longitudinal test load of a bonded joint, allowable thermal stress, and RNT range for natural distressing of a continuously welded rail track. Formulas were derived in terms of ΔT_C and RNT to determine cold and hot weather patrolling temperatures, the limiting temperature of tamping, and the ballasting of a skeleton track. An operational load was recommended for the consolidation of concrete and wood tie tracks, respectively, before opening for revenue traffic. The aforementioned formulas would be useful for the construction and maintenance of a track for any track condition, and geometry at any region.

As per current industry practice, lateral resistance per tie or discreet resistance [N] is divided by the tie spacing to convert it to longitudinal resistance or unit resistance [N/cm] although lateral resistance of a tie from field test often comes without the mention of tie spacing and/or tie type. This is demonstrated in the paper that the use of tie spacing is not legitimate and would offer unacceptable value in case of short tie spacing. The potential source of error of using tie spacing is discussed. As an extension of the work, this is demonstrated that longitudinal resistance [N/cm] of a fastener obtained by dividing longitudinal restraint [N] of a fastener by its spacing is also dubious.

In case of a single rail break, there are at least two known formulas to compute the breathing length, one uses half-tie resistance and the other uses full-tie resistance. As a result, the possible values of the breathing length and hence, the rail break gap, differ by 100%, thus warranting further research. Herein, we review these two calculations, plus a proposed method, to compute breathing length and rail break gap by using a more effective tie resistance value. The rail break gap in freezing temperatures under three contexts is computed, discussed, and summarised. The point at the end of the track where both rails are free to move and the axial force increases from zero to a temperature load of ΔT is typically referred to as the breathing length. The breathing length due to a single rail break in the non-breathing portion of a continuously-welded rail track may not be expected to have the same breathing length at the end of the track. Thus, breathing lengths under two scenarios are discussed. Further research is suggested to resolve the issue of breathing length definition and the two issues related to the effect of freezing temperatures on ballast resistance.

California is looking to build the United States’ first high-speed rail (HSR) network. The California High Speed Rail (CAHSR) is expected to have significant impacts on regional mobility. While the consensus is that high-speed rail will decrease intercity travel times, it is uncertain to what extent time savings will be realized for the residents of the major urban centers in California. The main goal of this research is to quantify and visualize the impact of the CAHSR on regional travel times across the state. Isochrone maps and origin-destination (OD) travel time matrices were developed using ArcGIS and advanced ArcGIS extensions (Network Analyst and Model Builder) for each of four different existing intercity transportation networks (car, train, bus, and air) and two additional future networks reflecting the two construction stages of the CAHSR (stage I: San Francisco to Los Angeles, and stage II: additional extensions to Sacramento and San Diego). Isochrone maps were produced for 11 major attractions across the state: the cities of San Francisco, San Jose, Los Angeles, San Diego, Redding, Sacramento, Fresno, and Bakersfield; and the Yosemite, Sequoia, and Joshua Tree national parks. Travel times (weighted by population) were computed between these 11 attractions and 460 cities across the state. The results of this study indicate that significant time savings (an average travel time reduction of 31%, in comparison to the car) will be achieved through the development, construction, and operation of the CAHSR system, and the impact on intercity travel times can be readily demonstrated and understood using GIS tools and isochrone maps.

Many universities in the United States provide students with subsidized transportation options through fixed-route buses or collaborative ridesharing services. Prior studies have explored the impacts of ridesharing services on the general public’s ridership of fixed-route transit, but none have narrowed their focus to the student population. In this study, real-time ridership data of a fixed-route shuttle and a shared autonomous vehicle (SAV) service that provides services to the students at a university in Arlington, Texas, is used to analyze the impacts of the SAV service on the ridership of the existing fixed route transit. To complement our findings from the ridership data, we also analyzed survey responses from the SAV service users to explore their preferences for different types of mobility services. Current data analyses show a shift of ridership from fixed-route transit to flexible on-demand modes and indicate that students prefer flexible on-demand services. The findings from this study provide guidelines for universities as they make decisions relative to improving and/or expanding their existing mobility services.

Bus rapid transit (BRT) has emerged as a cost-effective mode of public transportation in many cities worldwide. It has been argued that BRT carries more people per lane than the mixed vehicular traffic. However, road carriageways are designed for mixed vehicular traffic volume not people per lane. Paper measured the impact of BRT on roadway capacity utilization. The paper postulates that the design of BRT corridors affects adjoining vehicular traffic flows irrespective of whether BRT stations are located at midblock or curbside stations. The objectives were to measure traffic flow lane utilization in the presence of BRT facilities and compare outcomes with those taken without the influence of BRT. To that effect, traffic data at peak and off-peak conditions were surveyed at four selected sites on regional route R27 in Cape Town, South Africa, for a period of three months, under daylight and dry weather conditions. Based on the circumstances prevalent at the time of the survey, the pilot study assumed that density was a resultant of speed and flow hence not directly affected by BRT facilities. This implies that traffic flow utilization was fully the result of speed changes. The results of the study show significant anomalous capacity differentials from base year 2009 to 2021 post lockdown, with attendant consequences for capacity utilization. The study concluded that roadway capacity utilization is significantly enhanced by BRT mixed traffic operations.

COVID-19 emerged in December 2019 and changed transportation across the United States, especially for people dependent on transit buses to make trips. Most transit agencies saw a significant decrease in ridership; however, the impact on travel for agencies that offer demand-response transit hasn’t been examined completely. This paper examines ridership for a demand-response rural transit system from three perspectives: pre-pandemic, mid-pandemic, and pandemic-recovery. The paper examines total trips, origin locations, and destination locations at the three time intervals. In addition, the paper develops a transit social vulnerability index (SVI) to evaluate which riders continued to use the service and what factor or factors lead to those decisions. The results of the paper show that transit trip making and passengers of the system were not all affected uniformly.

Metro-bus transfer is a crucial step in last mile urban mobility and is where riders can waste significant amount of time. While total transfer time is a good measure for the integration of metro and bus services, it cannot tell how much time is spent for transferring and waiting, respectively. Therefore, a deeper understanding of transfer behavior and service qualities requires decomposing this metro-bus transfer into two legs: (1) walking from metro to bus stop and (2) waiting at bus stop. This study proposes to model total transfer time with exponentially modified Wald (ex-Wald) distribution, which estimates walking time via an inverse Gaussian distribution and waiting time via exponential distribution, respectively. Experimental results on a real-world smart card data set show that the proposed distribution describes the data more robust than other baseline models.

A prime component of any effective public transportation system is equitable access for all riders within the community. Building transportation networks with equity in mind can boost ridership and transit network coverage. The problem of social equity in transportation is further exacerbated by historically auto-dependent cities and the pandemic restrictions from COVID-19. This paper aims to quantify transit equity using a disadvantage index. These results are then applied to different periods of a transit system throughout the COVID-19 pandemic to see how it affected social equity in public transportation as part of a case study in Riverside, California. Heat maps are overlaid on the transit network’s lines and stops using geographic information systems (GIS) software to visualize the variables associated with transit equity calculations. Publicly released General Transit Feed Specification (GTFS) data is used to plot the state of the transit network at different points in time. The results show that route changes during the COVID-19 pandemic had a noticeable but minimal effect on social equity in transportation.

For much of the history of air transportation, aircraft such as planes and helicopters have relied on aviation fuel, causing a dependency on a supply chain of fuel, not to mention the issues related to noise and air pollution. As such, this paper highlights the latest developments in electric and hybrid aircraft, especially regarding electric vertical take-off and landing (eVTOL) vehicles and derivatives from it, that can be used in the regional air mobility transportation model to help connect underserved communities better and fill transportation gaps which can’t be adequately served by other means of transportation.

Recent advancements in unmanned aerial platforms have provided a quick and efficient tool to map areas and build three-dimensional models using photogrammetric techniques. Further, artificial neural networks (ANN) are being used widely to identify objects in such large data sets. This study aims to use both these techniques to develop models and identify objects of interest. Data collected from unmanned aerial vehicles (UAV) depends on various factors such as flight elevation, sensor resolution, overlap, stability of gimbal, environmental conditions, and others. In this study, the influence of the UAV flight elevation and resolution on the identification of stationary vehicles in the orthomosaic of a parking lot using convolution neural networks (CNN) was studied. For this purpose, several orthomosaics developed using aerial images collected at different altitudes and conditions were used to train various models. Image tiles were randomly divided into 90% and 10% representing training and validation data sets, respectively. The average precision in identifying the cars was used as a metric for comparing the CNN inference outputs of orthomosaics developed from the aerial data collected with different flight configurations. In addition, the location coordinates of the identified objects were also extracted automatically to integrate into GIS-based workflows. The results of this study will help understand the influence of those factors and apply this methodology for the identification of objects and compilation of their location with minimal manual effort.

Recent years have seen considerable progress in the development of autonomous, or self-driving, vehicles for use on streets and highways, as well as in more specialized environments, such as airports. Although autonomous vehicles have been operated in experimental trials, many of which are still in progress, they generally do so with a safety driver at the controls. In spite of this progress, it may be many years before fully automated vehicles are permitted to operate on public streets and highways without a safety driver present. Operation in controlled environments, such as airport airside areas or on fixed routes, may be authorized earlier. In spite of the current uncertainty on when such operations will be permitted, it seems inevitable that airports will need to address both the opportunities and challenges presented by fully autonomous vehicles within the foreseeable future, and most likely within the typical time frame of an airport master plan update or the anticipated life of airport terminal and landside infrastructure currently being planned or designed. This paper reviews the current state of development of autonomous vehicles and describes a number of proposed and experimental applications at airports. It also discusses the likely challenges that will be faced by airports as highly or completely automated vehicles become more widely available, as well as potential opportunities that such vehicles may present.

Early detection and repair of airfield pavement distresses are essential aspects of preserving airport pavements for the full spans of their design periods and maintaining their structural integrity, ride quality, and safety. In recent years, various studies have been conducted for pavement distress detection and mapping using alternate data collection methodologies, including small uncrewed aerial systems (sUAS). This study evaluates the usefulness of red-green-blue (RGB) orthophoto and digital elevation models (DEMs) generated from sUAS data in detecting various airfield Portland cement concrete (PCC) pavement distress. Fourteen airfield PCC pavement distresses with different severities were studied using RGB orthophotos with resolutions ranging from 0.8 mm/pixel to 21 mm/pixel and DEM resolutions ranging from 3.0 mm/pixel to 84 mm/pixel. The analysis showed that the RGB optical orthophotos and DEM were useful for detecting 13 out of 14 PCC pavement distresses and confirmation of the location of 11 out of 14 distresses, respectively.

With the rapid development of civil airports and the continuous growth of flight volume, it has brought huge challenges to the safety of apron operations and the improvement of ground operation efficiency. Unsafe events of the apron operation management (AOM) system are also emerging in endlessly. To reduce the probability of unsafe events, based on weighted system dynamics, this paper constructs a dynamic model of the AOM system and established a hazard indicator assessment system for the AOM system. In addition, this paper uses the entropy weight method to quantitatively analyze each index, then we propose the corresponding rectification measures according to the weight of hazard indicators. This method provides a new perspective for the apron operation safety assessment, and a certain scientific basis for the airport safety management department to evaluate the safety of apron operations.

Regional air mobility (RAM) focuses on building upon existing airport infrastructure to transport people and goods using innovative aircraft that offer an improvement in efficiency, affordability, and community-friendly integration over existing regional transportation options. These aircraft, which typically carry less than 20 passengers or an equivalent weight in cargo, are flexible in terms of where they can take off and land. Regional air mobility aircraft are able to operate physically close to communities due to characteristics such as low emissions, low noise, and steep climb/descent profiles reducing nuisance factors and environmental impacts. Their ability to operate from short runways at smaller local airports located closer to the populations they serve reduces vehicle miles traveled and lowers costs for both passenger and freight movement. This research studies potential operational scenarios identifying specific origin and destination airports, recognizing aircraft that will be available in the near future, studying infrastructure requirements from the landside and the airside, and evaluating the potential for integration with, and enhancement of, current and planned ground transportation services. Because electric aircraft will need charging support and potentially bring a large consumer of electrical energy to the airport, the return on investment for airport renewable energy solar projects may be improved and further support electrification of ground transportation at these sites. Regional airports typically sit on hundreds of acres of public-use land, which is well-suited for renewable sources such as solar power generation; this energy can be used to charge the batteries of electric regional air mobility aircraft, ground transportation, or sequester carbon as part of a fuel offset. As more electric/hybrid-electric aircraft are revealed to the public and begin to enter service, state and local governments around the country are beginning to evaluate the potential of this technology to improve connectivity, reduce greenhouse gas emissions, and generate new economic activity. Results show that RAM services offer the potential to transform regional airports into zero-emission, multi-modal transportation hubs to greatly increase the use of electric vehicles in communities currently not adopting this technology quickly because of the perception electric vehicles don’t have the range to travel the long distances between these communities and larger economic centers. Because the airports have large land areas that could support solar and battery storage, they can be new transportation hubs that support cars, delivery trucks, buses, and electric aircraft with renewable electricity to further reduce carbon emissions and leverage the valley’s solar power potential. The ability for these airports to support multiple modes of electric vehicle operations with on-site power allows them to operate in emergencies independent of the electric grid and support community transportation needs.

Historically, airfield pavement has and continues to be important to the nation’s commerce. It is clear that annual detailed pavement inspections of airport pavements are a critical part of airfield pavement management programs (PMP). In the 1970s, inspections were done with pen, paper, and rulers. In the 1990s, detailed inspections were conducted using tablet computers, GPS, and georeferenced photography. In the 2000s, pavement inspections were conducted using trucks with cameras, laser crack scanners, and even LIDAR with onboard inertial reference systems. The next leap in technology is using remote sensing UAS (UAS) imagery with high resolution data (sub 2 mm) and artificial intelligence (AI) processing of this data finding distresses to identify and catalog distress types, severity, size, and GIS data. We have collected UAS high resolution imagery for 33 airports in 14 states and in all nine FAA Regions. We have developed proprietary machine artificial intelligence neural network deep learning tools to teach the computer to identify pavement distresses and calculate lengths and areas, and establish a polygon of distress. After the AI model has identified each distress and assigned a confidence level, pavement analysts review the results and confirm the distress ID, size, shape, and severity followed by an update to the AI model. The authors have also developed digital elevation models to show the depth of 3D distresses such as rutting and depressions. All of this data is then be used to calculate a pavement condition index using the methodology of ASTM D5340. We have delivered imagery data and GIS location and PCI calculation for over 33 airports measuring 1,001,069,776 sq ft of pavement at a cost of $0.002 sq ft for 100% data collection. In contrast, traditional boots on the ground projects of these same airports may only have measured 10% of the pavement (100,106,977 sq ft) at a reported cost of $0.04 sq ft. We have successfully compared the UAS distress data with traditional data collection methods with world leading engineering firms, and the UAS results are repeatable, measurable, provided faster, and cost 88% less in a direct per square foot comparison and 1,000 times faster.

Airports contribute to the economy by facilitating the rapid transportation of the public and freight. Generally, windshield surveys are conducted annually to inspect the regional airports and maintain good operational performance. However, they are highly dependent on the experience of the inspector and other factors. The advancements in unmanned aerial vehicle (UAV) technology have propelled its application for mapping and assessing the conditions of runways, taxiways, apron areas, and helipads of a regional airport in Texas. For this, an optical camera sensor was mounted on the UAV to execute preplanned flight plans to map the paved surfaces of the airport in a single day. The ability to geotag images using high accuracy post-processed kinematic Global Navigation Satellite System (GNSS) was very helpful in obtaining high-quality data for making quantitative assessments. Three-dimensional models of the airport assets were developed from the aerial images to identify the condition of those assets. Airport asset conditions were quantitatively assessed to identify the pavement condition index. Many lessons were learned starting from the coordination of the inspections to the data collection stage. The research study presented in this paper is expected to guide the airport managers on the application of UAVs for airport asset management.

The type of aircraft envisioned as serving within the regional air mobility network will need to address several barriers to accomplish adoption feasibility. These barriers have been grouped into operational and infrastructure categories. In terms of regulations and existing infrastructure, the ideal locations to start regional air mobility operation with electrified aircraft are the already existing commercial airports. The purpose of this research is to figure out the potential usage of existing airport infrastructure, studying aircraft that will be available, analyzing infrastructure requirements from the landside and the airside, and evaluating the potential for integration with current and planned ground transportation services. This research will execute a limited inventory of the current ground infrastructure and study new electrical capacity needs at the selected airports to support the new service. A feasibility study will be prepared to assess the potential to leverage the electric ground vehicle infrastructure investment to support electric aircraft.