As congestion levels increase in cities, it is important to analyze people’s choices of different services provided by transportation network companies (TNCs). Using machine learning techniques in conjunction with large TNC data, this paper focuses on uncovering complex relationships underlying ridesplitting market share. A real-world dataset provided by TNCs in Chicago is used in analyzing ridesourcing trips from November 2018 to December 2019 to understand trends in the city. Aggregated origin–destination trip-level characteristics, such as mean cost, mean time, and travel time reliability, are extracted and combined with origin–destination community-level characteristics. Three tree-based algorithms are then utilized to model the market share of ridesplitting trips. The most significant factors are extracted as well as their marginal effect on ridesplitting behavior, using partial dependency plots for interpretation of the machine learning model results. The results suggest that, overall, community-level factors are as or more important than trip-level characteristics. Additionally, the percentage of White people highly affects ridesplitting market share as well as the percentage of bachelor’s degree holders and households with two people residing in them. Travel time reliability and cost variability are also deemed more important than travel time and cost savings. Finally, the potential impact of taxes, crimes, cultural differences, and comfort is discussed in driving the market share, and suggestions are presented for future research and data collection attempts.
The flexible nature of on-demand ride services provided by transportation network companies (TNC) has resulted in unique supply-side challenges as the industry deals with the COVID-19 pandemic. Early during the pandemic, there was a 70% decrease in the number of drivers accepting trips on TNC platforms, as individual drivers chose to reduce their risk of viral infection and abide by social distancing recommendations. Given the two-sided market nature of TNCs, the decrease was also the effect of reduced rider demand creating a less desirable driver experience. This paper characterizes and quantifies this change in supply as it relates to driver residency, tenure, attrition, and the number of trips provided. The distribution of drivers accepting trips shifted slightly toward the lower income and higher minority areas of Chicago. Using survival analysis methods, we find that retention among drivers who started in the early months of the pandemic was significantly lower than in reference years, after six months of driving. The results of the negative binomial regression show that drivers on a single TNC platform provided 20% less trips than drivers on multiple platforms. This difference increases to 30% during the pandemic. Additionally, new drivers joined multiple apps during COVID-19, likely to serve more trips and secure higher income. The results of this paper can be used to understand and target driver retention to accelerate the recovery of the TNC industry.
This paper studies the tax intervention applied to transportation network company (TNC) trips starting on January 6, 2020 in the City of Chicago. An interrupted time series (ITS) with an autoregressive integrated moving average (ARIMA) methodology is employed to infer the causal impact of the intervention on the percentage of shared trips and the counts of shared and private trips. Analysis is conducted at a community area level, either as pickup or drop-off. The results show a significant but small increase in the share of shared trips as well as the count of shared trips, specifically on weekends because of the intervention. Private trips, on the other hand, are found to have decreased on the weekdays, but potentially increased on the weekends. A Bayesian hierarchical model is then employed to combine information across community areas, examine a posteriori if there are significant spatial differences, and estimate the common treatment effect. The analysis suggests minimal spatial differences across community areas. The common treatment effect on weekdays ($1.75 tax difference) is a 3.78 percentage point increase in the share of shared trips, a 27% increase in the count of shared trips, and a 12% decrease in the count of private trips (at an approximate base of 10% market share of shared trips). Thus, the intervention likely shifted demand toward pooled rides, reducing congestion caused by TNCs. However, there is little evidence that this shift is sufficient to offset or reverse the systematic trend of declining use of shared rides.
Large-scale planned special events (PSEs) can pose unique transportation and logistics challenges. Data collection and simulation are important tools to address these challenges, although they are often difficult because of event size and complexity. This paper discusses methods to address the challenge of multimodal simulation at large PSEs through the context of AirVenture, a large week-long airshow organized by the Experimental Aircraft Association in Oshkosh, Wisconsin. Sampling and data collection techniques are discussed for a variety of modal processes like private vehicles, pedestrians, and shuttles, and for different situations like vehicle arrivals and departures, pedestrian queues, and shuttle systems. A flexible simulation framework for integrating these three modes and numerous activities is developed as a network of heterogeneous queues and queue-dependent choices. The simulation tested a variety of proposed policy changes around the site, including rerouting shuttle lines, and adjusting the system of vehicle arrivals to the site. Results of this study demonstrate the effectiveness and flexibility of the data collection and simulation methodologies. The techniques developed in this work can be used to improve planning and transportation systems at many other forms of PSE.
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