Extracting patterns from Twitter to promote biking

Emphasis on non-motorized travel modes (for example, biking) reduces motorized trips and provides positive effects on the environment and the quality of human life. Understanding factors that influence people to biking or bike commuting can help decision makers, transportation planners, and bike commuting networks. Historically, conventional methods like surveys and crash data analyses were conducted to understand relevant factors. Survey and crash data analysis are difficult to perform in broad scale due to data availability and efforts. An innovative approach to determining these factors is to conduct social media mining to understand sentiments or motivations of bike commuters. People use terms (with hashtag at the beginning of the term) in Twitter, a popular social media network, to express their thoughts, activities or information. This study developed a framework for using Twitter data in understating the sentiments of the bikers with minimal effort. In this study, Twitter data associated with bike commuting hashtags were obtained for eight years (2009–2016). This study provided a framework of data collection and application of various natural language processing (NLP) tools (for example, text mining, sentiment analysis) to extract knowledge from the unstructured text data. Findings show that biking is associated with weather and seasonal patterns. The general sentiment towards biking is positive. However, negative sentiments are associated with bad weather, crime, and other challenges. The polarity scores indicate somewhat positiveness in the recent few years. The developed framework and the findings of this study will help planners and decision makers to promote biking on a broader scale.     

True Walking Distance to Transit

Abstract

People riding transit in the city of Detroit walk on average 0.8 miles (1.3 km) per round trip. The straight-line walking distance was found by buffering the bus stop locations and comparing them to the weighted US Census blocks. However, the true walking path follows the street pattern. Rather than undertaking network analysis, which would require connecting all addresses in the city with all bus stops, a Monte Carlo simulation was performed in geographic information system with random addresses. The simulation was performed over several addresses until convergence was achieved. The distances were converted to walking times and compared to the US National Household Transportation Survey.     

Estimating bus component failure distributions from censored samples

A technique is developed and demonstrated which can be used to estimate bus component failure distributions in an operational setting. The advantage of the described method is that it estimates the distribution of an entire population when only a portion of that population has experienced a failure (censored samples). Further, in an operational setting, components in a population do not age at the same rate and thus, their ages (in miles) are not ordered with respect to time. A sampling technique is derived to draw an ordered, censored sample from a population that has been aged in an operational setting. The resulting sample is used to estimate the life distribution of the component of interest.