Analysis of the Impact of the COVID-19 Pandemic Outbreak on Houston Bike Share Ridership

A bikeshare system is a transportation service which bicycles are available for shared use by individuals for a short term at low or no cost. It is affordable to users and a healthy system for both users and the environment. It is a solution for people who do not have a vehicle and to limit the increase of private car usage. The study aimed to investigate the impact of the COVID-19 pandemic outbreak on bikeshare ridership with a case study for the City of Houston. The data used for this study include ridership data for 2019 and 2020, COVID-19 cases of the city of Houston from the Harris County residents for the year 2020, and temperature and precipitation in Houston for the years 2019 and 2020. The methodology includes descriptive analysis as well as using Negative Binomial regression modeling to derive the relationship between the dependent variable and independent variables. According to the descriptive analysis, there was an overall increase in ridership during the COVID-19 period in 2020. The longer duration trips in 2020 are much higher than those in 2019, and most of the trips occurred during off-peak followed by evening and morning peaks. Moreover, the regression analysis revealed that the COVID-19 pandemic had a statistically significant positive impact of COVID-19 cases on the average daily ridership. The weekend indicator had the strongest statistically significant positive impact on the average daily ridership. The temperature indicator had no statistically significant impact on the average daily ridership and precipitation had the strongest statistically significant negative impact on the average daily ridership

Rebalancing citi bike : a geospatial analysis of bike share redistribution in New York City

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThis study provides a model to rate and visualize the bicycle redistribution of Citi Bike, the bikeshare system that operates in New York City. The share of rebalanced bicycles in proportion to total rides sharply decreased in the spring of 2015, which prompted the question as to what impact, if any, this change in operations had on the availability of bikes and the system’s ability to relay bikes to empty stations. In terms of public transit, a bikeshare system is only as effective as its ability to respond to commuter supply and demand. In order to circumvent the absence of data about redistribution routes and times utilized by Citi Bike’s operations team, publicly available trip data was reverse-engineered in order to recreate the rebalancing events over the three years of the bike share’s operation (2013-2015). Pairwise correlation revealed the stations between which bikes are transferred the most. Data on availability per station, derived from an accumulated JSON feed was integrated in order to derive an hourly score per station. The durations of consecutively empty and full stations were analyzed. Finally, a k-means clustering analysis of availability events was performed in order to visualize the spatial patterns of bicycle supply and demand. A negative correlation was found between the amount of rebalanced bicycles and the performance of stations based on indicators such as emptiness, fullness, and deliveries per empty instants

Factors Influencing the Usage of Shared E-scooters : A Case Study of Chicago

The rapid popularity growth of shared e-scooters creates the necessity of understanding the determinants of shared e-scooter usage. This thesis estimates the impacts of temporal variables (weather data, weekday/weekend, and gasoline prices) and time-invariant variables (socio-demographic, built environment, and neighborhood characteristics) on the shared e-scooter demand by using four months (June 2019- October 2019) period of data from the shared e-scooter pilot program in Chicago. The study employs a random-effects negative binomial (RENB) model that effectively models shared e-scooter trip origin and destination count data with over-dispersion while capturing serial autocorrelation in the data. Results of temporal variables indicate that shared e-scooter demand is higher on days when the average temperature is higher, wind speed is lower, there is less precipitation (rain), weekly gasoline prices are higher, and during the weekend. Results related to time-invariant variables indicate that densely populated areas with higher median income, mixed land use, more parks and open spaces, public bike-sharing stations, higher parking rates, and fewer crime rates generate a higher number of e-scooter trips. Moreover, census tracts with a higher number of zero-car households and workers commuting by public transit generate more shared e-scooter trips. On the other hand, results reveal mixed relationships between shared e-scooter demand and public transportation supply variables. This study\u27s findings will help planners and policymakers make decisions and policies related to shared e-scooter services

Forecasting Bike Rental Demand Using New York Citi Bike Data

The idea of this project is from a Kaggle competition “Bike Sharing Demand”① which provides dataset of Capital Bikeshare in Washington D.C. and asked to combine historical usage patterns with weather data in order to forecast bike rental demand. This dissertation will extend this work, working with a broader range of project not only just focusing on the phrase of model building but all phases of KDD (Knowledge Discovery in Databases). This dissertation focuses on Citi Bike which is one of the biggest bike share projects in the world, collects Citi Bike data, weather data and holiday data from three different databases, and integrates the data to a model ready format. Four basic predictive models are built and compared using multiple modelling algorithms, five techniques are used to enhance the accuracy of random forest model, and the final model’s RMSLE (with 10-fold cross validation) decreases from 0.499 to 0.265. This paper learns many experience from case study of Kaggle Bike Sharing Demand, and seek to build optimize predictive model with smallest error rate. This project generally answers a question of “How many bikes will meet users’ demand in a future certain time”, the future work of this project will be to focus on each docking station’s activity. The realistic meaning of this dissertation is to provide an overview solution for bike rebalance problem, and helps to better manage Citi Bike program

Quantitative Evaluation on Public Bicycle Trips and its Impact Variables among Different Land Uses

Public bicycle sharing systems for daily use have been effective for increasing cycling in China, which can significantly ease traffic congestion and the production of toxic gasses. Encouraging the development of bicycle transportation has become an important part of cities’ sustainable development policies. This paper explains the relationships among public bicycle trips,public infrastructure, road characteristics, the built environment, and temporal variations. The study area is the Xiasha Education District, which is located in the east of Hangzhou City, China. Using data on the Hangzhou Public Bicycle system, we utilized Pearson correlation analysis and multiple linear regression modelling to analyse how the variables affect public bicycle trip production for different land uses. This paper also analyses the temporal variations for hourly trip production for three land uses. The results show that public infrastructure and road characteristics significantly affect public bicycle trips. In addition,the effects of temporal variation vary across different land uses. Our findings will be helpful for planners and engineers to improve their understanding of public bicycle production

Development of a multivariate logistic model to predict bicycle route safety in urban areas

In response to the renewed appreciation of the benefits of bicycling to the environment and public health, public officials across the nation are working to establish new bicycle routes. During the past two decades, a number of methods have been endorsed for the selection of suitable bicycle routes. These methods are limited in that they do not explicitly address bicycle safety nor do they reflect urban conditions. The purpose of this research is to develop an objective bicycle route safety rating model based on injury severity. The model development was conducted using a logistic transformation of Jersey City\u27s bicycle crash data for the period 1997-2000. The resulting model meets a 90% confidence level by using various operational and physical factors (traffic volume, lane width, population density, highway classification, the presence of vertical. grades, one-way streets and truck routes) to predict the severity of an injury that would result from a crash that occurred at a specific location. The rating of the bicycle route\u27s safety is defined as the expected value of the predicted injury severity. This rating is founded on the premise that safe routes produce less severe accidents than unsafe routes. The contribution of this research goes beyond the model\u27s predictive capacity in comparing the safety of alternative routes. The model provides planners with an understanding, derived from objective data, of the factors that add to the route\u27s safety, the factors that reduce safety and the factors that are irrelevant. The model often confirms widely held beliefs as evidenced by the finding that highways with steep grades, truck routes and poor pavement quality create an unfavorable environment for bicyclists. Conversely, the model has found that increased volume and reduced lane width, at least in urban areas, actually reduce the likelihood of severe injury. Planners are encouraged to follow the lead of experienced bicyclists in choosing routes that travel through the urban centers as opposed to diverting bicyclists to circuitous routes on wide, low volume roads at the periphery of cities