How does our natural and built environment affect the use of bicycle sharing?

Public bicycle-sharing programs (PBSP) are short-term bicycle hire systems. In recent years their popularity has soared. This study examined Brisbane’s CityCycle scheme, the largest PBSP in Australia, and investigated the role of (natural and built) environmental features on usage. The study addressed four research questions: (1) What are dynamics of PBSP use in terms of travel time, speed, and distance? (2) What is the relationship between PBSP participation and cycling infrastructure? (3) How does land-use affect PBSP usage? (4) How does topography affect PBSP usage? To answer these four questions, the authors analysed large existing datasets on CityCycle usage, land-use, topography, and cycling infrastructure, which were each obtained through multiple sources. Correlation and regression analysis were employed to establish significant relationships amongst variables. It was found that: most users take short trips within the free initial period provided under the CityCycle scheme and do not incur any charges other than for membership; PBSP use is strongly correlated with the length of off-road bikeways near each CityCycle station; CityCycle is more frequently used on weekends and for recreational purposes; loop journeys, which are also associated with leisure trips, are popular in Brisbane, especially on weekends; leisure trips are taken at a relatively slower pace than utilitarian trips; during weekdays, a trimodal peak is clearly evident, with PBSP commute trips in the morning and evening peaks and a smaller but significant peak around lunchtime; and users avoid returning CityCycle bicycles to stations located on hilltops. These findings can collectively enhance both the siting and design of PBSP, thereby optimizing investments in sustainable mobility

Spatial analysis of cyclist mobility patterns using geovisualization to improve public bike-share system in a small size city

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesNowadays, a large amount of data with high spatio-temporal resolution is increasingly produced in cities arising from different systems. One system on trend is Bike share systems, which creates data that allow tracking bicycles positions located at the stations network when a bike is taken from one station and later parked at the destination point. These data can be used to explore how cyclists move around the city. In this work, we use data provided by Bicicas, bike share system provider in Castellón de la Plana, the focus is set in the analysis of the cyclist mobility patterns through the use of geo-visualization tools. We propose a method which goes from data collection to the development of an interactive dashboard used for the visual analysis of the movements. The data collected and the analytical code developed during this study will be available on GitHub to enhance reproducible research practices

Tennessee Strategic Highway Safety Plan 2020-2024

https://digitalcommons.memphis.edu/govpubs-tn-dept-transportation-strategic-highway-safety-plan/1000/thumbnail.jp

Advanced Econometic Models for Modeling Flows: Application to Shared Economy

Travel and tourism industry is undergoing transformation with the flourishing of online sharing economy marketplaces such as Bike Share services, Uber/Lyft (for taxi services), Eatwith (for community restaurants), and AirBnB (for accommodation). The current research effort contributes to literature on sharing economy service flow analysis by formulating and estiamting econometric approaches for analyzing frequency variables. The sharing economy alternatives investigated include: (a) accommodation service (AirBnB), (b) bikeshare service (Citi bike, NYC) and (c) ride hailing service (UBER/LYFT/Taxi). In the first part of the dissertation, we develop a copula based negative binomial count model framework to count AirBnB listings at census tract level to capture the snapshot of accommodation supply for tourists in NYC. In the second part, considering bike sharing as one of the transportation sharing systems, the dissertation identifies two choice dimensions for capturing the bike share system demand: (1) station level demand and (2) how bike flows from an origin station are distributed across the network. In the third part of the dissertation on ride sharing systems, we identify two choice dimensions: a demand component that estimates origin level transportation newtwork company (TNC) demand at the taxi zone level and (2) a distribution component that analyzes how these trips from an origin are distributed across the region. A linear mixed model is considered to estimate station or taxi zone level demand while a multiple discrete continuous extreme value (MDCEV) model to analyze flows distribution is employed. In the final part of this dissertation, we develop an innovative joint econometric model system to examine two components of the rapid ride share market transformation: (a) the increase in ride hailing demand and (b) the shift from traditional taxi services to TNC services. The first component is analyzed adopting a negative binomial (NB) count model while the second component is analyzed using a multinomial fractional split (MNLFS) model

City of Hitchcock Comprehensive Plan 2020-2040

Hitchcock is a small town located in Galveston County (Figure 1.1), nestled up on the Texas Gulf Coast. It lies about 40 miles south-east of Houston. The boundaries of the city encloses an area of land of 60.46 sq. miles, an area of water of 31.64 sq. miles at an elevation just 16 feet above sea level. Hitchcock has more undeveloped land (~90% of total area) than the county combined. Its strategic location gives it a driving force of opportunities in the Houston-Galveston Region.The guiding principles for this planning process were Hitchcock’s vision statement and its corresponding goals, which were crafted by the task force. The goals focus on factors of growth and development including public participation, development considerations, transportation, community facilities, economic development, parks, and housing and social vulnerabilityTexas Target Communitie

Data-Driven Optimization for Bicycle Station Location in a Small to Medium-Sized City: The Case Study of Cuenca, Ecuador

Bike sharing systems (BSSs) are an important transportation alternative, and station distribution is a key component of these that is driven by user demand and resource constraints. Designing an effective BSS with appropriate station distribution requires a method that consists of steps structured in a flexible, parameterizable, repeatable, and organized way, based on and aligned with proven or accepted standards---particularly in resource-limited environments. This includes data-driven analysis of information relevant to BSS station design from various sources and in different formats. Models and algorithms are used to organize and examine the data, reduce redundant data, standardize factors, and find patterns that can inform the efficient design and implementation of a BSS. The algorithms and models used in the present study provide a data-driven approach to determining effective BSS station distribution in a city. Factor analysis and principal component analysis (PCA) were used as the various sources of data involved in the design of a BSS (i.e., data on traffic, demographic, and land use) can often overlap and/or have redundant data and these techniques allow minimizing superfluous data without losing relevant information. Econometric models were also used to identify the costs of pollutants, with the aim of locating stations in areas where pollution is a problem, and an emission-free BSS might be of greatest benefit. Patterns of potential users and mobility are derived from unsupervised learning algorithms. Finally, the set covering model (SCP), an optimization model for the distribution of stations, is used to define the number of stations in the city and their locations. This model\u27s objective is to minimize costs while still satisfying user demand. Using this data-driven approach can help guide the strategic design and planning of a BSS. A case study using this method was carried out using data from the city of Cuenca, Ecuador, the third most populous city in this developing country. Cuenca is considered a mid-sized city and is a UNESCO World Heritage Site. When compared to the costly Spanish--Ecuadorian consortium that implemented the currently BSS running in Cuenca, applying the proposed data-driven approach to this real-life practical case study resulted in a 70--90\% match in the locations of stations. The consortium had to study the place of implementation in a great amount of depth and obtained a similar design to that obtained in this case study. This demonstrates the potential of the proposed method as a simple, effective, and low-cost method for the strategic planning of BSSs in small and mid-sized cities. The present study provides an affordable solution to the design of BSS station distribution for cities without many resources. Using this method, cities can take advantage of a standardized platform to define a network of stations through an established step-by-step process. The method of BSS design proposed here demonstrates three significant advantages: 1) in-depth knowledge of the area in which a BSS is to be implemented location is not required, as the design can be driven by existing data and can even be adapted to new data sources; 2) implementation is economical as this reduces the need to hire expensive expert personnel with knowledge and experience in implementing BSSs; 3) the method is versatile since it can accept input data of various kinds, which enables the adaptation of the solution to any small or mid-sized city. This method, therefore, provides small and mid-sized resource-limited cities with a simple and cost-effective method to design a BSS that can be tailored to particular contexts and can be adapted to the specific goals of BSS implementation in a given city

Land Use Identification of the Metropolitan Area of Guadalajara Using Bicycle Data: An Unsupervised Classification Approach

El siguiente trabajo propone diferentes maneras de resolver una problemática que se encuentra en la actualidad, que es el hacer la investigación en el área de land-use, mapeo y comportamiento humano evaluando su movimiento por medio de fuentes de información que contienen información geo referenciada, también se comparte la meta de clasificar diferentes secciones y su relación entre ellas. Se utilizó como fuente de información MiBici que es una plataforma de compartimiento de bicicleta que existe en la ciudad de Guadalajara, Jalisco, la cual comparte mes tras mes un archivo consolidado de los viajes que se realizan en cada mes, cabe mencionar que el acceso de esta información es totalmente libre. Las metodologías utilizadas fueron agile para planeación del proyecto, KNN, Decision Trees y KMeans para la cauterización de las zonas, el lenguaje de programación utilizado fue Python, además se anexo una propuesta de implementación utilizando la plataforma de Amazon Web Service con el objetivo de proponer una solución más “sencilla” de implementar, pero con el mismo valor que hacerlo con puros recursos libres. El proceso se dividió primordialmente en 3 partes en donde la primera fue limpiar datos y entenderlos, se aplicaron algoritmos machine learning que fueron Decision tree y KNN, para la segunda etapa evaluando los resultados de la etapa anterior se hicieron modificaciones a los datos en donde se agregaron nuevos campos para mejor los resultados y se aplicó KMeans para la creación de grupos y como último paso se creó un flujo que inicio con la limpieza de los datos en crudo utilizando herramientas de AWS y se terminó con la interpretación de los resultados finales. Los resultados obtenidos fueron demasiados alentadores ya que los grupos que se obtuvieron fueron demasiados marcados y revisándolo con las zonas relacionadas a los nodos se encontró una gran relación. Sin duda alguna queda aún demasiado trabajo a desarrollar en esta rama de investigación

Rockport Comprehensive Plan

This document was developed and prepared by Texas Target Communities (TxTC) at Texas A&M University in partnership with the City of Rockport, Texas Sea Grant, Texas A&M University - Corpus Christi, Texas A&M University - School of Law and Texas Tech University.Founded in 1871, the City of Rockport aims to continue growing economically and sustainably. Rockport is a resilient community dedicated to sustainable growth and attracting businesses to the area. Rockport is a charming town that offers a close-knit community feel and is a popular tourist destination for marine recreation, fairs, and exhibitions throughout the year. The Comprehensive Plan 2020-2040 is designed to guide the city of Rockport for its future growth. The guiding principles for this planning process were Rockport's vision statement and its corresponding goals, which were crafted by the task force. The goals focus on factors of growth and development including public participation, development considerations, transportation, community facilities, economic development, parks, and housing and social vulnerability