A Comparison of Mapping Methodologies Identifying Transportation Disadvantaged Populations and Extreme Weather Risk
Megan Morrow, Kittelson & Associates, Inc. (KAI)Show Abstract
Sumeeta Srinivasan, Tufts University
This analysis compares three increasingly granular mapping methodologies to predict where transportation disadvantaged (TD) populations live in the Boston area. The methods are as follows: (1) A standard choropleth method, which utilizes a binary system to determine levels of TD and a thematic shading symbology, (2) a dasymetric mapping method utilizing the Environmental Protection Agency’s Intelligent Dasymetric Mapping (IDM) Toolbox to redistribute population based on land use, and (3) a Cadastral-Based Expert Dasymetric System (CEDS) which utilizes parcel data to redistribute population and principal component analysis of vulnerability attributes. In addition to using these three methods to estimate TD population, this paper investigates vulnerability to extreme weather risk by estimating TD populations living within evacuation zones or within a reasonable walk to a shelter. The CEDS results suggest approximately 20% more people may require evacuation assistance than the Massachusetts Emergency Management Agency estimates. In total, 14% of the population in the study area (487,389 people) are at risk of being in a TD location, and of these people, 14% live within an evacuation zone and farther than a 20-minute walk from a potential shelter. In the future, planners could utilize this dasymetric mapping method, paired with outreach and training, to proactively plan for extreme weather events.
Climate Change Impacts on North Carolina Roadway System in 2050: A Systemic Perspective
Huiying Fan (email@example.com), Georgia Institute of Technology (Georgia Tech)Show Abstract
Rawlings Miller, WSP
Leta Huntsinger, North Carolina State University
This study predicts the climate change impacts on the roadway system in North Carolina in 2050. The analysis includes three steps: identifying climate change scenarios, modeling climate stressors, and transportation system analysis. This analysis shows potentially severe climate change threats in North Carolina, with an estimated 0.23 – 1.24% trips underserved across different scenarios. Climate change influence exhibits a clear spatial pattern. The northern coast is projected to experience more inaccessibility and the southern coast more congestion. Location-specific analysis for Jacksonville and Wilmington proves the importance of incorporating more redundancy in the roadway network and strengthening critical roadway corridors. This study demonstrates that science-based and location-specific analysis is crucial for climate change adaptation planning.
Development of a Climate Change Vulnerability Index for Pavement Infrastructure: A Case Study of Developing Countries
Boris Goenaga, North Carolina State UniversityShow Abstract
Saqib Gulzar (firstname.lastname@example.org), North Carolina State University
Benjamin Underwood, North Carolina State University
The most prominent effects of climate change are projected increases in average ambient and extreme temperatures leading to heatwaves, changes in frequency and intensity of precipitation, sea level rise, occurrence of hurricanes, floods, and storm surges. It is expected that all of these climate stressors will likely affect pavement performance, and hence affects the capacity of a transport network to provide an acceptable level of service over the period of time that it was intended for. In this study a new vulnerability index is proposed – Extent of Pavement Grade Reliability Loss ( E PGRL ), which has the capacity to capture both pavement performance and transport serviceability. Two developing countries, Colombia and India, were selected to analyze the capabilities of the E PGRL to quantify and express the vulnerability of the transport network to climate change. The results obtained indicate the E PGRL can be used as a tool to evaluate and to quantify the vulnerability of the transport infrastructure to future climate change.
Evaluating Impacts of Coastal Flooding on the Transportation System Using activity-based modeling: a case study in Miami-Dade County, FL
Yu Han (email@example.com), University of FloridaShow Abstract
Changjie Chen, University of Florida
Ruth Steiner, University of Florida
Zhong-Ren Peng, University of Florida
Recent climatic disasters have shown the vulnerability of transportation infrastructures against natural hazards. To understand the risk of coastal hazards on urban travel activities, this study presents an activity-based modeling approach to evaluate the impacts of storm surge on the transportation network under sea-level rise in Miami-Dade County, FL. A Markov-Chain Monte Carlo (MCMC) based algorithm is applied to generate population attributes and travel diaries in the model simulation. Flooding scenarios in 2045 are developed based on different adaptation standards under the 100-year storm surge and population projections from the land-use conflict identification strategy (LUCIS) model. Our analysis indicates that about 29.3% of the transportation infrastructure, including areas of the US No. 1 highway, roadways in the south and southwest of the County, and bridges connecting Miami Beach and Miami-Dade County, will be damaged under the storm surge when the low-level adaptation standard is chosen. However, the high-level adaptation standard will significantly reduce the vulnerable infrastructures to 12.4%. Furthermore, the total increased travel time of the low-level adaptation standard could be as high as twice of the high-level adaptation standard during peak morning hours. Our model results also reveal that the average increased travel time due to future storm surge damage ranges between 14.2-62.8 minutes per trip.
Corridor-Level Transportation Resilience Analysis Framework and Resilience Index for a Multi-Hazard Setting in Puerto Rico
Benjamin Colucci Rios (firstname.lastname@example.org), Recinto Universitario de Mayaguez Universidad de Puerto RicoShow Abstract
Alexander Molano Santiago, University of Puerto Rico, Mayaguez
Ismael Pagán-Trinidad, University of Puerto Rico, Mayaguez
Luis Aponte-Bermúdez, University of Puerto Rico, Mayaguez
Resilience against extreme weather events and multi-hazard environments is of growing interest in Puerto Rico, the Caribbean and worldwide among transportation professionals. The primary objective of this paper is to present a corridor-level transportation resilience analysis framework in a multi-hazard tropical setting applied to an NHS corridor in the western region of Puerto Rico. A secondary objective is the development of a Transportation Resilience Enhancement Priority Index (TREPI) to assist decision-makers in establishing priorities for resilience-enhancing investments. The TREPI incorporates variables representing traffic, natural hazards exposure, and highway conditions. Natural hazard exposure variables like extreme wind speed, are considered through the newly developed 2018 Puerto Rico Special Wind Region Maps (SWRM), funded by FEMA after Hurricane Maria. The new SWRM considers the topographic speed-up effects caused by the complex topography of Puerto Rico, thus representing wind hazard risk in a more general way than previous versions. The SWRM, like the latest ASCE 7 wind maps, design wind speeds are based on Risk Categories with defined mean recurrence intervals (MRI). Risk Categories are then associated with ADT follow AASHTO LRFDLTS-1 specification. Also, rainfall-induced landslide susceptibility from the USGS, annual and 30-year average rainfall maps from the National Weather Service, FIRM base flood elevations, and USGS seismic ground acceleration zones. Condition variables include the pavement condition maps from the 2028 Puerto Rico Transportation Asset Management Plan of the PRHTA and the FHWA sufficiency rating from the National Bridge Inventory of Puerto Rico.
The Effect of Different Road Pavement Typologies on Urban Heat Island: a Case Study
Vittorio Ranieri (email@example.com), Polytechnic University of BariShow Abstract
Stefano Coropulis, Politecnico di Bari
Nicola Berloco, Politecnico di Bari
Pasquale Colonna, Politecnico di Bari
Veronica Fedele, Politecnico di Bari
Paolo Intini, Politecnico di Bari
Claudio Laricchia, Comune di Bari
Nowadays the urban areas are increasingly facing the issue of the Urban Heat Island (UHI) phenomenon, related to climate change, which implies several negative consequences in many fields (human health, energy consumption, economic). One of the main attitudes to face and solve this issue is to rely on and to implement new materials with optimal thermic and evapotranspiration properties which can enhance the air temperature decrease in dense urban areas. This approach has been applied in this research, testing different materials in one parking area in Bari (Italy): the choice of a parking area is justified by its dimension which enables to have a wider view on the effects deriving from the implementation of mitigation strategies. Six different pavements have been tested to replace the current Macadam, by means of a thermal three dimensional non-hydrostatic simulation made with the ENVI-Met software: impervious asphalt pavement (IAP), asphalt permeable pavement (APP), green pavement (GP), green pavement + asphalt permeable pavement (GP+APP), grey porous concrete blocks (GCB), and light concrete permeable pavement (LCPP). The highest-performance pavements in terms of potential air temperature (PAT) reduction were the GP (-1.22°C), GCB (-1.26°C) and LCPP (-1.22°C). These pavements also showed a constant RH, suggesting their goodness in UHI mitigation. Also, a comparison of the structural properties, and construction and maintenance costs of such pavements was provided. Based on the results, the best pavement in terms of UHI mitigation, stress resistances, and construction and maintenance costs, is the grey concrete porous blocks pavement.
Experience is Not Enough: A Dynamic Explanation of the Limited Adaptation to Extreme Weather Events in Public Organizations
Fengxiu Zhang (firstname.lastname@example.org), George Mason UniversityShow Abstract
Spiro Maroulis, Arizona State University
This study advances theory articulating the micro-level processes behind public transit agency adaptation to extreme weather. It tackles a persistent puzzle about the limited adaptation to extreme weather in public transit agencies: why does adaptation remain limited after public transit agencies have experienced repeated extreme weather and some massive consequences? We develop a computational agent-based model that integrates extant theory and data from semi-structured interviews of U.S. public transit agency managers, and use the model to investigate how micro-level cognition and behavior interact with environmental constraints to facilitate or impede the diffusion of adaptation. We articulate in greater detail how experience with impactful extreme weather events is related to adaptation, highlighting that such experience is insufficient for adaptation. A key insight is that the potential benefits from both increased risk perception and additional financial resources stemming from extreme weather events can be underutilized, absent effective coupling between heightened risk perception and availability of resources that creates windows for adaptation. Using this insight, we further identify managerial and policy interventions with most leverage to promote adaptation to extreme weather in the public transit sector.
ASSESSING TRUSTWORTHINESS OF CROWDSOURCED FLOOD INCIDENT REPORTS USING WAZE DATA: A NORFOLK, VIRGINIA CASE STUDY
Shraddha Praharaj, University of VirginiaShow Abstract
Faria Zahara, University of Virginia
T. Donna Chen (email@example.com), University of Virginia
Yawen Shen, University of Virginia
Luwei Zeng, University of Virginia
Jonathan Goodall, University of Virginia
In recent years, climate change and sea-level rise have caused higher and prolonged high tides, which in combination with rainfall, storm surges, and insufficient drainage infrastructure, result in recurrent flooding in coastal cities. There is a pressing need to understand the occurrence of roadway flood incidents in order to enact appropriate mitigation measures. Agency data for roadway flooding events are scarce and are resource-intensive to collect. Crowdsourced data can provide a low-cost alternative for mapping roadway flood incidents in real time; however, the reliability is questionable. This study demonstrates a framework for asserting trustworthiness on crowdsourced flood incident data in a case study of Norfolk, Virginia. Publicly available (but spatially limited) flood incident data from the city in combination with different environmental and topographical factors are used to create a logistic regression model to predict the probability of roadway flooding at any location on the roadway network. The prediction accuracy of the model was found to be 91.9%. When applying this model to Waze data, 86.7% of the reports were predicted to be trustworthy. This study demonstrates the potential for using crowdsourced Waze incident report data for roadway flooding detection, providing a framework for cities to identify trustworthy reports in real-time to enable rapid situation assessment and mitigation to reduce incident impact.
Modeling Demographic Relocation in Response to Climate Risk Factors and Gentrification Displacement Pressures
Sneha Roy, AECOMShow Abstract
Pragun Vinayak, Cambridge Systematics
David Von Stroh, Cambridge Systematics
Climate risk factors, including wildfire, sea level rise, inland flooding, and extreme heat, as well as gentrification displacement pressures will be primary drivers of migration in coming years. Travel demand modeling relies on reasonable and appropriate forecasts of demographic totals at the detail of travel analysis zones (TAZs). Methodologies for developing scenarios in response to individual and combined climate risk factors are described, drawing on work done for the Southern California Association of Governments (SCAG) SoCal Regional Climate Adaptation Framework. Methodologies for developing scenarios in response to gentrification displacement pressures of low-income workers are described, drawing on work done for the California Statewide Freight Forecasting and Travel Demand Model (CSF2TDM). Climate adaptation and housing policy, respectively, are currently in need of greater integration and coordination. Future directions are explored to integrate these methodologies and create a combined demographic relocation model, sensitive to both climate risk factors and the affordability and gentrification displacement pressures of market based real estate dynamics.
Hurricane Resiliency Methods for the New York City Electric Bus Fleet
Maya Tessler, Barnard CollegeShow Abstract
Elizabeth Traut (firstname.lastname@example.org), Pennsylvania State University, University Park
Emissions reductions in the transportation sector are critical to decreasing the urban carbon footprint and ensuring resident health. Electric buses help municipalities work toward these goals. However, they require a steady electricity supply and therefore face challenges from power network disruptions in the aftermath of natural disasters, unlike diesel buses, which can receive fuel from regional storage facilities. The research to date has tended to focus on personal electric vehicle or diesel bus operations resilience methods, with little scholarship on the adaptability of these methods to electric buses. This study examines hurricane vulnerability on two New York City electric bus routes. We use power loss and flooding scenarios to determine the critical element of NYC’s electric bus infrastructure: on-street fast chargers, not overnight depot chargers. This conclusion holds true for both bus routes, despite significant differences in route length, charging pattern, and battery size. Based on available space, existing infrastructure, and cost analyses, we conclude that temporary flood barriers used in combination with diesel generators and solar panels are strong resiliency methods to protect NYC on-street chargers. This combination of methods is a departure from disaster planning focused on centralized diesel distribution facilities and bus depots. We also found that fast charging siting should take disaster projection models into consideration to avoid future repair and resiliency infrastructure costs. As New York and other US cities work towards fully electric bus fleets, this study and similar analyses will inform charger placement and resiliency spending in the transportation system.
How Extreme Weather Influences a Taxi Market: Spatio-temporal Analysis for Transport Policy Insights
R. C. P. Wong, University of Hong KongShow Abstract
P.L. Mak, University of Hong Kong
Wai Yuen Szeto, University of Hong Kong
Wenhan Yang, University of Hong Kong
Extreme weather conditions, strong gust, and torrential rainfall threaten the safety of the general public and restrict people’s travel options. Most of the transportation modes are suspended due to safety reasons. Taxis are one of the only few available non-private transport modes to provide services to those who have urgent and unavoidable travel needs. This study uses global positioning system data collected from 460 Hong Kong urban taxis during nine ordinary and one tropical cyclone periods aiming to find out and explain the differences in terms of the percentage of taxis not in operation, the number of served passenger-trips, average time spent for vacant taxi drivers finding a customer, and the percentage of taxi drivers in cross-district customer-search throughout the same 48-hour duration. The finding shows an inadequate level of taxi supply and a high passenger demand during the tropical cyclone-affected period. Up to 80% of taxis were not in operation to serve the urgent and necessary trips. The average customer-search time for taxi drivers, which is anticipated inversely proportional to the demand for taxi rides, was very short (about five minutes). Policy measures are discussed and recommended to the government to improve the taxi services during extreme weather conditions.
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