A Multi-Objective Optimization Model to Minimize the Gap in Accessibility to Multi-Use Paths While Maximizing the Economic Efficiency of Active Transportation Investments for Fresno, California

This research examines the accessibility to multi-use paths in a variety of neighborhoods in Fresno, California. This study aims to develop a multi-objective optimization modeling framework to be used by decision makers in transportation and policy to maximize the total accessibility to multi-use paths across the city while minimizing the gap between low- and high- accessibility neighborhoods by an optimal allocation of active transportation. Researchers calculate accessibility to multi-use paths by measuring the total length of multi-use paths (walkway and bikeway) a resident could reach from their own Census block group with a 30-minute cycling ride. The study employs a geographically weighted regression (GWR) model to capture the local relationships between accessibility to multi-use paths and previous transportation investments (walkways, bikeways, and primary and secondary roads), while controlling for other socioeconomic factors. The marginal-effect analysis for the GWR results categorizes the areas within Fresno into economically efficient, inefficient, and indifferent locations for further investments. The researchers embed the GWR results into a multi-objective optimization modeling framework to improve accessibility to multi-use paths over the city and simultaneously address inequality in active-transportation accessibility. This research provides decision makers with insight into the problem of making of an economically-efficient and socially-equal active transportation plan accessible to people from diverse backgrounds with the ultimate goal of fostering public health

Note on Soft Graviton theorem by KLT Relation

Recently, new soft graviton theorem proposed by Cachazo and Strominger has inspired a lot of works. In this note, we use the KLT-formula to investigate the theorem. We have shown how the soft behavior of color ordered Yang-Mills amplitudes can be combined with KLT relation to give the soft behavior of gravity amplitudes. As a byproduct, we find two nontrivial identities of the KLT momentum kernel must hold.Comment: 25 page

Building Momentum Kernel from Shapovalov Form

These notes are an extended version of the talks given by the authors at the XIV International Workshop on Lie Theory and Its Applications in Physics, Sofia, Bulgaria, 20-26 June 2021. The concise version published in the proceedings of the workshop contains additional discussions for the q-deformed scenario: https://link.springer.com/chapter/10.1007/978-981-19-4751-3_23 In these notes we identify KLT kernel with the Shapovalov form on Verma module with its highest/lowest weight given by the reference momentum and rest of the momenta as roots. We then take a step forward and show how the Feynman diagrams emerge naturally as the Shapovalov duals of the Verma module basis vectors. We show such algebraic construct offers a compact expression for the BCJ numerators. Explicit examples are shown for nonlinear sigma model and HEFT pre-numerators.Comment: 18 pages, 2 figures, Published in: Springer Proc.Math.Stat. 396 (2022) 287-29

Do Multi-Use-Path Accessibility and the Clustering Effect Play a Role in Residents\u27 Choice of Walking and Cycling?

The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above

Investigating the Resilience of Accessibility to Emergency and Lifesaving Facilities under Natural Hazards

Studying accessibility, including the resilience of city transportation networks, is critical to understand how these networks influence individuals’ mobility and lives. This study developed an analytical research framework to examine the resilience of accessibility to emergency and lifesaving facilities under the threats of natural hazards such as earthquakes and wildfires. With a cumulative-opportunity approach, the authors measured accessibility by counting emergency and lifesaving facilities (including parks, schools, hospitals, roads, and fire stations) that can be reached by driving at the census tract level in San Fernando Valley, CA. With the calculated accessibility, the authors run simulations to collect data showing what would happen if an area were affected by a selected disaster. They then used statistical analysis to identify those areas where accessibility is significantly reduced compared to the original status. A normalized difference accessibility index (NDAI) was further created to suggest plans and strategies to help those vulnerable areas through adding facilities/services or improving transportation infrastructure

A Gravity Model Integrating Land-Use and Transportation Policies for Sustainable Development: Case Study of Fresno, California

The idea of urban compaction has been long proposed and promoted to address the problem of urban sprawl in many American cities. However, there are still rare successful cases of such implementation in the United States. This study uses a classic gravity model, TELEM (Transpiration, Economic, and Land-Use Model) to examine to what extent a land-use or transportation policy must be regulated to make the urban compaction occur in a typical auto-dependent city—Fresno, California. Five scenarios are considered (BL, L1, L2, T1, and T2), in which the baseline (BL) is a natural growth scenario. Without any policy interventions, the city will inevitably expand outward. The L1 (high-intensity zoning) and L2 (growth boundary) results suggest that high-density zoning and growth boundary policies could make the compaction occur. The T1 (location impedance) and T2 (carbon tax) results reveal that transportation interventions would create barriers among regions/areas and therefore should be carefully used for compaction. This study not only adds to the literature on urban modeling but also contributes to the practice of smart growth or new urbanism policies for sustainability