2015-2016 Projects
This work aims to address a key research gap: There has been extensive scholarship devoted to identifying, classifying and quantifying the conditions by which existing transportation infrastructures and system operation produce uneven access to services and needs relative to socioeconomic status and its spatialization. However, there has been little design-research undertaken to examine ways in which intervention within existing transportation infrastructures might strategically alter and / or improve the conditions of uneven access. Based on interviews with thought leaders globally and in the U.S., recent Rockefeller Foundation-supported work by SMART (Zielinski, Anand) has offered preliminary observations relating to how transport needs and conditions of the underserved (low income, growing seniors population, disabled) could be, and are being, more widely addressed by emerging multi-modal systems enhanced by new service models and information technologies. To date these emerging or "disruptive" systems, regionally customized and generally implemented through public-private innovation, have been associated with the shifting needs, preferences, and cultures of the growing urban population at all income levels, including specifically the millennial demographic. However it now appears these disruptive systems-based solutions may have wider application across a broader demographic including the vulnerable and undeserved. Yet very little concerted physical design research and prototyping has been carried out to a) confirm these initial observations, b) support decision makers and practitioners and c) inform policy.
The research question that this work will aim to address is: "what may be the ways and processes by which the physical and spatial assets of transportation systems themselves might be retrofitted, coupled, or hybridized with other systems not only to address the National Transportation Goals of Livable Communities, Environmental Sustainability and Economic Competitiveness, but also to positively impact Quality of Life for all users".
TRID Database: https://trid.trb.org/View/1447165
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Traffic flow theory has come to a point where conventional, fixed time averaged data are limiting our insight into critical behavior both at the macroscopic and microscopic scales. This study developed a methodology to measure relationships of density and vehicle spacing on freeways. These relationships are central to most traffic flow theories but have historically been difficult to measure empirically. The work leads to macroscopic flow-density and microscopic speed-spacing relationships in the congested regime derived entirely from dual loop detector data and then verified against the NGSIM data set.
The methodology developed in this study eliminates the need to seek out stationary conditions and yields clean relationships that do not depend on prior assumptions of the curve shape before fitting the data. Upon review of the clean empirical relationships a key finding of this work is the fact that many of the critical parameters of the macroscopic flow-density and microscopic speed-spacing relationships depend on vehicle length, e.g., upstream moving waves should travel through long vehicles faster than through short vehicles. Thus, the commonly used assumption of a homogeneous vehicle fleet likely obscures these important phenomena.
TRID Database: https://trid.trb.org/View/1490121
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EPA-MOVES is a modal-based emissions estimator that accounts for vehicle operating modes defined by factors like speed, acceleration, road grade, curvature, and so on. MOVES has the ability to include alternative types of fuel and different type of vehicles as well. Analyses at different scales including regional, state, and project level (e.g., small road network at county level) can be done with MOVES. Integration of MOVES with a microscopic traffic simulator can be outlined as an input-output process. The second-by-second vehicular activities from traffic simulation serves as input for MOVES and the emissions inventory for a transportation network can be estimated. The input from traffic simulators can be any of the following formats: (a) average speeds for the links in the network, (b) Link Driving Schedule (LDS) for each link of the network. LDS is time dependent speed profile of a link (generally done for a representative vehicle typically by sampling), and (c) Operating mode distribution of vehicles on the link. While average speed is commonly used in practice, operating mode distribution and LDS can take the advantages regarding vehicular activity data and dynamic capability of MOVES to report time dependent emissions.
In this project, we propose a novel decision-support tool to find the representative vehicle trajectories and accordingly the LDS for links on transportation networks. The technique will use the similarity measure such as the dynamic time warping distance and/or longest-common-subsequence measures in clustering that are more appropriate for curve alignment as compared with the Euclidean distances and its variants.
TRID Database: https://trid.trb.org/View/1490120
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TRID Database: https://trid.trb.org/view/1514188
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Chicago State University, through the Fred Blum Neighborhood Assistance Center, will perform outreach for the NEXTRANS Center to community organizations needing assistance with transportation and accessibility related GIS projects on the South Side of Chicago and southern suburbs.
The specific research project CSU will work on will be a study of the potential effects of the expansion of a commuter rail station that is adjacent to campus. The station is currently underutilized. Trains only stop at the station once every one to two hours. If expanded, the station would become an express stop, with much more frequent service and would better link Chicago State to Chicago’s growing southern suburbs. The station also currently has security issues. A new station, located in closer proximity to the center of campus, with adequate security, is likely to increase public transportation usage to and from campus, and hypothetically could boost enrollment. CSU is currently working to gain funding for the station from the USDOT. The CSU NEXTRANS team would work with the CSU administrative group working on this issue to perform a GIS analysis of current and potential CSU students, to model commute times by car and public transportation currently and with the opening of the station. In addition, the CSU RA team will work to develop a survey of current students focusing on how the building of the station might affect their transportation to and from CSU, as well as whether the new station might affect the enrollment decisions of potential students in areas served by the rail line.
TRID Database: https://trid.trb.org/View/1472667
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While states have data and information systems for managing spatially related data to support decision making, those systems are not populated to support freight-related planning and operations decisions. For example, at least one agency has constructed roundabouts on designated truck routes and then endured the extreme dismay of trucking companies. To address coordination issues, some agencies establish new offices for freight but the comprehensive scope of business activities and issues do not fit neatly into the organizational structures of typical transportation agencies. Another approach is to enhance access to the agency-wide data and information necessary to support development of transportation infrastructure that balances the needs of both passenger and freight users.
This research will build on the investigators' expertise in asset management and relationships with the DOTs in the Mid America Freight Coalition. This project will review existing data sources, identify new data sources and create a catalog of business processes, data and information items necessary to support consistency, and quality in decision making and asset management of freight infrastructure and operations. The results will support implementation of the MAP-21 requirements for freight performance.
This project deals with strategies for managing and integrating spatially related freight transportation data both intra- and inter-agency from the perspective of a state transportation agency. The scope of data is freight data, defined as data about freight for making decisions regarding the planning, design, construction, operation, and maintenance of transportation infrastructure. Some of the freight data items include truck routes, ESAL charts, truck traffic, variable speed limits, intermodal connectors, spring-thaw restrictions, bridge clearances, posted bridges, steep grades, rail crossings, port entries, foreign trade zones, longer combination vehicles rules, OS/OW routes, weight stations, and road uses agreements.
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TRID Database: https://trid.trb.org/View/1505246
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The Ohio State University (OSU) Campus Transit Laboratory (CTL) is a living laboratory that provides the infrastructure for integrated transit-related research investigations, educational activities, and applied studies. The CTL benefits from advanced automatic data collection and information technologies deployed on the OSU Campus Area Bus Service (CABS), accessibility of the CABS system and the OSU community to researchers, instructors, and students for data collection and in situ observations, and regular interaction between CTL investigators and CABS operators and decision makers. This NEXTRANS project would continue to: Sustain, develop, and showcase the CTL; Collect, process, and archive CTL data; Exploit the CTL for research, education, and outreach activities; and Develop collaborations with transit agencies and investigators.
This project is expected to have impacts related to transit planning and operations on each of the research, education, and outreach dimensions. Improvements in transit planning and operations should lead to a more efficient, sustainable, and environmentally responsive balance in the use of urban transportation modes. It is also hoped that publicizing a set of integrated activities centered on the use of a living lab would result in the expanded development and use of such labs in multiple transportation areas.
TRID Database: https://trid.trb.org/View/1505249
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TRID Database: https://trid.trb.org/View/1435263
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TRID Database: https://trid.trb.org/View/1473077
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TRID Database: https://trid.trb.org/View/1505250
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Under disasters and emergency situations, a successful implementation of the evacuation plans depends on the provision of reliable information in a timely manner, especially so in the context of short- and no-notice evacuation. With the increasing usage of portable personal devices (e.g., smart phones) as sources of information dissemination and acquisition, there is a need to understand the role of heterogeneity of information characteristics across different dissemination sources on the travel evacuation behavior of individuals.
This study proposes a systematic framework to address the potential effects of heterogeneity in information in a travel evacuation context. We will seek to understand the characteristics of information propagation from different dissemination sources and the impact of heterogeneous information on the evacuee behavior in terms of travel-related decision-making. The study will also analyze how heterogeneous decisions affect the system performance under evacuation. It will aid multiple nodal agencies associated with disaster response in developing evacuation plans, and coordinate information dissemination through multiple sources by factoring the potential for asymmetric (non-uniform) distribution of information across individuals. Thereby, the proposed study seeks to understand the role and significance of emerging social media in evacuation situations.
TRID Database: https://trid.trb.org/View/1482212
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This study will take advantage of large datasets the NEXTRANS investigators have amassed on two transit systems: OSU’s Campus Area Bus Service (CABS) as part of OSU’s Campus Transit Lab (CTL) and the Central Ohio Transit Authority (COTA) as a result of completed and ongoing projects. On CABS, extensive APC, onboard survey, and mobile device tracking data are available. On COTA, extensive APC, onboard survey, and AFC data are available. Thus, each system has three separate sources of data the integration of which will be considered as part of this study. More reliable transit OD flow information has the potential to enhance the quality of transit planning and operations control functions that rely on such information. Moreover, the more accurate route-level estimates are expected to improve the quality of the network-level OD flow estimates, where several methods rely on route-level OD flows as inputs.
In addition to supporting practical service planning and design applications, the developed integration methods are expected to establish new foundations for researchers to build on in addressing questions related to integrating transit data from multiple sources, whether for the purpose of OD flow estimation or other uses of the data. In light of the data quality analyses and their implication on OD flow estimation, the results of this study could inform the design refinements of the next generation of transit data collection and fare collection services such that the overall quality of the data is improved, whether in the context of the primary use of the data or its secondary use, for example for OD flow estimation.
TRID Database: https://trid.trb.org/View/1472665
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TRID Database: https://trid.trb.org/View/1447169
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A Traffic Scanner (TScan) is being developed with the joint pool of NEXTRANS's and INDOT/JTRP's funds to enable collecting accurate microscopic traffic data at road intersections with an innovative use of Light Detection and Ranging (LiDAR) 3D laser scanning technology. LiDAR sensing promises to overcome certain limitations of video cameras because it yields 3D point clouds that have a one-one correspondence with the environment being sensed. The current effort is focused on developing elements of the LiDAR's tracking algorithm with self-calibration and adjustment for the sensor's motion.
The results of the current project show that LiDAR calibration and tracking with clear statistical guarantees are possible. The guarantees are functions of the characteristics of the sensor itself: its resolution, and precision. We expect that our sensing system will work in a variety of environments and will produce results of a uniform quality. The proposed second phase will be focused on developing algorithms for object identification, classification, and tracking.
TRID Database: https://trid.trb.org/View/1447167
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Accessibility measurement has been integral to transportation and land-use scholarship since the 1950s, and since the 1970s researchers have argued that it forms the theoretically correct basis for transportation and land-use evaluation. Accessibility-measurement metrics are well-defined and can be based on data readily available to transportation planners. Yet accessibility-based evaluation has largely failed to make the leap from laboratory to practice, and has nowhere displaced traditional mobility-based evaluation. The proposed project seeks to understand barriers to the greater use of accessibility evaluation in practice; to develop qualitative, quantitative, and graphical approaches to overcoming those barriers; and to work cooperatively with practitioners on assessment of accessibility-based evaluation.
The project will be informed by lessons derived from a previous multi-year project at the University of Michigan that measured regional accessibility among the top 50 metropolitan regions of the United States, and will proceed in four interrelated stages: (i) Research political and technical barriers to the adoption of accessibility-based evaluation, (ii) Develop case studies of accessibility- and mobility-based evaluation, (iii) Develop approaches to incorporating accessibility-based evaluation, and (iv) Collaborate with practitioners and decision-makers on assessment of accessibility-based evaluation.
TRID Database: https://trid.trb.org/View/1473091
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Travelers' decisions regarding transportation can be conceived of along a long-term to short-term spectrum. Decisions of residential locations, vehicle ownership, and work destination are usually established over the scale of years. Over a shorter time period of perhaps months, people make decisions regarding parking purchase and non-work destinations. Despite this broad range of time frames, current strategies for the dissemination of transportation information concentrate at the short-term end of the spectrum.
To foster more sustainable transportation choice behavior, an effective information strategy should be ideally designed to work along the full time-scale range, particularly since longer-term decisions frequently constrain the shorter-term options. However, the insights on the sensitivity of choices at varying time scales to information interventions, or the impact of long-term choices on those made over the shorter terms are limited.
This project will develop practical approaches to the delivery of accessibility related information and new decision-making models in the full time-scale range that are informed by multiple disciplines including cognitive science, behavioral economics, marketing, transportation, and urban planning. It will design information interventions intended for the full range of transportation-relevant decisions and test their impacts on people moving to the Greater Lafayette area, Indiana. The research is designed to test the sensitivity of: (i) long-term decision of residential location choice to information, and (ii) the sensitivity of short-term travel characteristics to long-term residential location choice.
TRID Database: https://trid.trb.org/View/1482211
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Individual firm supply chains metrics of competitiveness include time, cost (as measured by transport cost, in transit and buffer inventory) and environmental impact. These factors impact their choice of competitive global logistics paths. In such contexts, stable transit times with larger capacities may, in cases, be more competitive than unpredictable mode choices with random delays. Several product manufacturers and retailers have committed to tracking their environmental impact (green scorecards), with sourcing preference given to suppliers who offer competitive prices while improving their environmental performance.
Thus, multimodal/intermodal facilities that consciously consider their impact on firms’ choices can use their impacts on time, cost and the environment to serve as competitive alternatives for firms. In this study, we propose a firm level and an aggregate level analysis to understand the economics of emerging logistics opportunities facilitated by multimodal or intermodal terminals. By considering new multimodal or intermodal offerings as being required to compete with existing choices by firms, plans for such facilities will be more realistic because they would have anticipated market impact. Thereby, the proposed research would provide a mechanism to link intermodal infrastructure investment decisions to economic competitiveness.
TRID Database: https://trid.trb.org/View/1482213
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Green House Gases (GHG) are connected to global warming and hence to climate change. Emissions from on-road vehicles significantly contribute to GHG in the atmosphere. This situation calls for environmental sustainability with the least impact on the transportation sector. Environmental sustainability of the transportation industry should start with the understanding the complexity and the evaluation of the existing status with respect to economy, social behavior and movement patterns, and geography. We propose to monitor changes in climate and environmental parameters with concomitant changes in technology enabled integration and other sustainability factors.
In the first phase, an inventory of current GHG emissions across spatial, temporal, and stakeholder levels from on-road vehicles are studied as a baseline to monitor impacts from the existing technological, policy, social, health, and economic drivers, including disparities that are controlling the transportation sector. In the second phase, alternative scenarios to the existing on-road mobile transportation system will be evaluated in order to reduce the GHG emissions. This will be looked into the aspects of the transportation mode, traffic network systems and alternative energy.TRID Database: https://trid.trb.org/View/1447168
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In recent years, the US Department of Transportation has identified less traditional goals for transportation performance centered on environmental sustainability. This project will assess the adoption of non-traditional measures of transportation success that the federal government has identified as a strategic goal. To what extent is the goal of environmental sustainability measured at the federal level? Has the federal government provided reporting requirements to the states in regards to environmental sustainability? How is it measured? Are there some measures that could provide models for states to follow? What are the sources of resistance to these alternative measures of transportation?
The USDOT is an anticipated user of the results from this proposed project. In recent years, the USDOT has identified less traditional measures of transportation performance centered on livable communities and environmental sustainability. To what extent are these goals measured at the state level? How are they measured? Are there some measures that could provide models for states to follow? What are the sources of resistance to these alternative measures of transportation? Answers to these questions would inform the USDOT as to the success of its efforts in shifting state transportation departments to direct attention to its more novel measures of transportation performance.
TRID Database: https://trid.trb.org/View/1482214
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TRID Database: https://trid.trb.org/View/1467796
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TRID Database: https://trid.trb.org/View/1490123
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An extensive multimodal transportation network serves both passengers and freight movement in the FHWA Region V states: Illinois, Indiana, Michigan, Minnesota, Ohio and Wisconsin. This region covers 322,530 square miles, approximately 9 percent of the United States. According to the 2014 U.S. Census, these states are home to 52,196,212 residents, approximately 17 percent of the U.S. population. Three of the 15 most densely populated areas in the United States are in this region: Chicago, Indianapolis, and Columbus. Ohio is among the top ten most densely populated states in the U.S. Other states ranking near the top ten most densely populated states include Illinois (12th), Indiana (16th), and Michigan (17th). These states are moderately sized, contain both rural and urban areas, and demonstrate geographic, economic, and resource variance. This variation can be assumed to affect the dynamics of the transportation system as well as the corollary workforce composition.
The project objective was to characterize the transportation industry and employment trends in Region V by identifying and analyzed information from federal, state, and private sector research, technical reports, conference presentations, case studies, and human resources documents (e.g., position descriptions, job advertisements, career ladders, trainings, strategic plans, etc.).
TRID Database: https://trid.trb.org/View/1490124
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Data were manually collected from The Ohio State University transit buses in regular operation. The average flow rates estimated for the same roadway segments determined from data manually collected from buses operating on different bus routes for the same time-of-day period are found to be much more similar than the estimates for the same roadway segments determined from data collected in different time-of-day periods or different academic terms. Moreover, the differences in the estimates for the different time-of-day periods correspond to known commuting traffic patterns (greater inbound flows in the morning, larger outbound flows in the afternoon), and the differences for the different academic terms correspond to known traffic activity (less traffic in the Summer term than in the Spring term).
Flows were also collected from LiDAR and video sensors mounted on a van that traversed several of the same segments traversed by the transit buses. The LiDAR data were automatically transformed into vehicle counts and times. Software was developed to allow individuals watching the video recordings in a playback mode to click to record locations and times of vehicle detections. These data were then transformed to input values for use with the modified moving observer method to estimate traffic flows. Since the raw LiDAR and video data are recorded simultaneously from the van, they record the same vehicles. Therefore, the differences in flows estimated from the LiDAR and video data would be expected to be smaller than the differences in flows estimated from data manually collected from the buses. The magnitudes of the relative differences between average flows estimated from the LiDAR and video data are, indeed, much smaller than the magnitudes of the relative differences between flows estimated from buses in different time-of-day periods and different academic terms.
TRID Database: https://trid.trb.org/View/1490125
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TRID Database: https://trid.trb.org/View/1482210
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