Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms

This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms (MLAs). Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors for four popular MLAs, namely, logistic regression, artificial neural networks, random forests, and gradient boosting. Experimental results suggest gradient boosting to consistently provide higher prediction performance. Specific locations, certain time periods and weekdays consistently emerged as critical predictors.

Improving Services Offered by Internet Providers by Analyzing Online Reviews using Text Analytics

With the proliferation of digital infrastructure, there is a plethora of demand for internet services, which makes the wireless communications industry highly competitive. Thus internet service providers (ISPs) must ensure that their efforts are targeted towards attracting and retaining customers to ensure continued growth. As Web 2.0 has gained traction and more tools have become available, customers in recent times are equipped to make well-informed decisions, specifically due to the colossal information available in online reviews. ISPs can use this information to better understand the views of the customers about their products and services. The goal of this paper is to identify the current strengths, weaknesses, opportunities, and threats (SWOT) of each ISP by exploring consumer reviews using text analytics. The proposed approach consists of four different stages: bigram and trigram analyses, topic identification, SWOT analysis and Root Cause Analysis (RCA). For each ISP, we first categorize online reviews into positive and negative based on customer ratings and then leverage text analytic tools to determine the most frequently used and co-occurring words in each categorization of reviews. Subsequently, looking at the positive and negative topics in each ISP, we conduct the SWOT analysis as well as the RCA to help companies identify the internal and external factors impacting customer satisfaction. We use a case study to illustrate the proposed approach. The proposed managerial insights that are derived from the results can act as a decision support tool for ISPs to offer better products and services for their customers.

Recommendations for Emerging Air Taxi Network Operations based on Online Review Analysis of Helicopter Services

The effects of traffic congestion are adverse, primarily including air pollution, commuter stress, and an increase in vehicle operating costs and accidents on the road. In efforts to alleviate these problems in metropolitan cities, logistics companies plan to introduce a new method of everyday commute called air taxis, an Urban Air Mobility (UAM) service. These are electric-powered vehicles that are expected to operate in the forthcoming years by international transportation companies like Airbus, Uber, and Kitty Hawk. Since these flying taxis are emerging mode of transportation, it is necessary to provide recommendations for the initial design, implementation, and operation. This study proposes managerial insights for these upcoming services by analyzing online customer reviews and conducting an internal assessment of helicopter operations. Helicopters are similar to air taxis in regards to their operations, and therefore, customer reviews pertaining to the former can enable us to obtain insights into the strengths and weaknesses of the short-distance aviation service, in general. A four-stage sequential approach is used in this research, wherein the online reviews are mined in Stage 1, analyzed using the bigram and trigram models in Stage 2, 7S internal assessment is conducted for helicopter services in Stage 3, and managerial recommendations for air taxis are proposed in Stage 4. The insights obtained in this paper could assist any air taxi companies in providing better customer service when they venture into the market. Keywords: Air taxi; Emerging technology; Urban Air Mobility (UAM); Helicopter services; Online customer reviews; Text analytics;