Hassan Ali Khan
North Carolina State University, Ralegih, North Carolina, United States
Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through \textit{i)} graphical user interfaces and \textit{ii)} public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54\%, utilization of available vehicles increased by 6\%, and a 5$\times$ increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50\% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services' availability and surge pricing.
https://dl.acm.org/doi/abs/10.1145/3491102.3517464
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)