Rural to Urban Intercity Transit User Characteristics Analysis, Demand Estimation and Network Design

Rural transit always plays a critical role in transporting rural residents, especially the ones who do not have a car, cannot drive, or choose not to drive. Intercity bus (ICB), deviated fixed route transit (DFRT) and demand responsive transit (DRT) are three major modes of rural public transportation. Although there are more DFRT and DRT service providers and services in the US, due to institutional issues, there are much more studies about ICB than DFRT and DRT. Meanwhile, state governments are struggling on how to improve the rural transit system with limited budget. This dissertation is aimed to fill the gap by studying the rural transit rider characteristics, ICB system evaluation method and DFRT route design. First, surveys were performed to understand who are using the rural DFRT and DRT services and why they use them. It was found out that DFRT and DRT passengers, whose characteristics are similar to ICB riders, are likely to be female, of minority races, have low personal and household income, low number of vehicles in the household and rent the house. 90% of the riders have difficulty finding alternative transportation mode, suggesting they are captive riders, not choice riders. Secondly, a methodology to locate the high ICB demand area and design ICB stops accordingly is proposed. The existing stop locations are compared to the high demand areas and meaningful destinations. It was found out that the ICB stops in Tennessee are well connected to the meaningful destinations but poorly located to cover the high demand areas. Finally, a methodology to find the most cost effective routes is developed. It uses DRT trip records of a local DRT service provider to construct a trip generation model. The model finds that the trip generation rate of a census tract is significantly positively related to the density of population over 16 years old and density of no-vehicle household in the census tract. The method to find the best routes is presented using Tennessee as an example. This dissertation provides useful information to state government on how to evaluate ICB system, improve rural transit and design DFRT network

Exploring the Spatially Heterogeneous Effects of the Built Environment on Bike-Sharing Usage During the COVID-19 Pandemic

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study explores spatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factors (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic, thus adjusting station locations, capacity, and rebalancing schemes accordingly

MSMEG_2731, an Uncharacterized Nucleic Acid Binding Protein from Mycobacterium smegmatis, Physically Interacts with RPS1

While the M. smegmatis genome has been sequenced, only a small portion of the genes have been characterized experimentally. Here, we purify and characterize MSMEG_2731, a conserved hypothetical alanine and arginine rich M. smegmatis protein. Using ultracentrifugation, we show that MSMEG_2731 is a monomer in vitro. MSMEG_2731 exists at a steady level throughout the M. smegmatis life-cycle. Combining results from pull-down techniques and LS-MS/MS, we show that MSMEG_2731 interacts with ribosomal protein S1. The existence of this interaction was confirmed by co-immunoprecipitation. We also show that MSMEG_2731 can bind ssDNA, dsDNA and RNA in vitro. Based on the interactions of MSMEG_2731 with RPS1 and RNA, we propose that MSMEG_2731 is involved in the transcription-translation process in vivo

Statewide Rural-Urban Bus Travel Demand and Network Evaluation: An Application in Tennessee

This paper examines the characteristics of intercity bus riders within Tennessee and proposes methods to identify service gaps and prioritize network expansion, particularly focusing on rural-urban connections. Data were collected through an on-board survey and compared with intercity auto trips. Compared to personal auto users, intercity bus riders are more likely to be of minority races, unemployed, unable to drive, and from low-income households. Five demand levels were determined based on the population distribution with these characteristics. The service areas of existing bus stops were identified and compared with the high demand areas. The result shows that an insufficient number of stops are located in high demand area. Still, approximately 80 percent of stops connect to meaningful destinations such as hospitals. The results imply that bus stations are well-connected to destinations but poorly connected to potential riders. Changes to the current network could better cover high-demand areas

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults’ walking behavior is determined by many factors, such as socioeconomic attributes and the built environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults’ walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box–Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults’ walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space—specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent

Variations in outdoor thermal comfort in an urban park in the hot-summer and cold-winter region of China

Global warming and rapid urbanization have exacerbated the urban heat island effect. Urban parks contribute to alleviating such an effect and achieving the “carbon emission peak before 2030” and “carbon neutrality before 2060” goals of China. Their popularity is considerably influenced by human thermal comfort. However, limited thermal comfort studies have been conducted in the hot-summer and cold-winter region of China. This study examines human thermal comfort in different landscapes of an urban park in Chengdu and determines the thermal benchmarks. A machine learning (random forest) analysis shows that human thermal sensation is affected by different meteorological factors in different seasons. In addition, the influences of landscape space on human thermal comfort have considerable differences in different seasons. Residents prefer strong solar radiation in winter but fast wind speed in summer. UTCI (universal thermal climate index) is better than PET (physiological equivalent temperature) for outdoor thermal comfort assessment in the study area. This study serves as a valuable baseline and technical reference, contributing to sustainable urban park design.</p

Variations in outdoor thermal comfort in an urban park in the hot-summer and cold-winter region of China

Global warming and rapid urbanization have exacerbated the urban heat island effect. Urban parks contribute to alleviating such an effect and achieving the “carbon emission peak before 2030” and “carbon neutrality before 2060” goals of China. Their popularity is considerably influenced by human thermal comfort. However, limited thermal comfort studies have been conducted in the hot-summer and cold-winter region of China. This study examines human thermal comfort in different landscapes of an urban park in Chengdu and determines the thermal benchmarks. A machine learning (random forest) analysis shows that human thermal sensation is affected by different meteorological factors in different seasons. In addition, the influences of landscape space on human thermal comfort have considerable differences in different seasons. Residents prefer strong solar radiation in winter but fast wind speed in summer. UTCI (universal thermal climate index) is better than PET (physiological equivalent temperature) for outdoor thermal comfort assessment in the study area. This study serves as a valuable baseline and technical reference, contributing to sustainable urban park design.</p

COVID-19 moderates the association between to-metro and by-metro accessibility and house prices

Previous studies extensively examined the role of accessibility to metro in shaping house prices but largely overlooked the contribution of accessibility by metro. In addition, limited studies examined the moderating effect of COVID-19 on the price effects of to-metro and by-metro accessibility. Based on multilevel hedonic price and quantile regression models, this study scrutinizes the association between to-metro accessibility, by-metro accessibility, and house prices in Chengdu, China, and examines the moderating role of COVID-19 in this association. We show that by-metro accessibility significantly influences house prices. COVID-19 significantly influences the value of to-metro accessibility but marginally affects that of by-metro accessibility. The value of to-metro accessibility is disproportionately affected by the pandemic. Specifically, small or low-priced houses are less affected than big or high-priced houses. In other words, the flattening of the to-metro price gradient is more discernible for big or high-priced houses. The changing preference of residents has also been verified by the decreases in house transaction volume in metro-adjacent areas

Direct modeling of subway ridership at the station level: a study based on mixed geographically weighted regression

Station-level ridership modeling is one of the ways to forecast metro ridership and reveal how factors influence ridership. Previous studies assumed that the relationships between the dependent variable and independent variables are either global or local, as indicated by the global model or the geographically weighted regression (GWR) model. This study explores the possibility that some independent variables have spatially varying relationships with metro ridership while others have constant relationships by employing the mixed GWR model. Data from the Chicago metro system were used. To establish an effective forecasting model, possible influencing factors are collected. OLS model results indicate that the proportion of recreational jobs to total jobs, number of bus stops, employment density, number of high-income workers, and the type of station (transfer or terminal) are significant variables influencing station-level metro ridership. By using the mixed GWR model, we find that the proportion of recreational jobs to total jobs is a global variable while the others are local variables. By comparing the results of mixed GWR, full GWR, and OLS models, we find that mixed GWR fits the data better and the residuals are less correlated. However, results of cross-validation indicate that the prediction power of the OLS model is better than that of the full and mixed GWR models.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author