We’re diving into gig economy services with fresh insights from anonymized DriversSeat data. Volunteered by rideshare and delivery drivers, this data sheds light on crucial aspects of gig worker experiences and can answer many questions about how the availability of gig economy services have altered travel and consumer behavior. Further, the ‘directly from the source’ model allows us to develop and share insights that otherwise would not be possible due to gig economy companies lack of incentives to share.

Gig-economy services thrive because they offer convenience valued by both customers and drivers. Customers are willing to pay for the convenience, even as rates have increased, speaking to the “stickiness” of the service. Drivers are willing to participate due to a combination of flexibility, earnings, and ease of entry.

Exploratory analysis of massive datasets like these is critical to understanding the type of analysis and questions that can or cannot be evaluated with the data. Rather than performing traditional statistical analysis immediately, visually inspecting transportation data in multiple forms allows for a better understanding. By recognizing patterns or outcomes that support or refute previously held hypotheses, the inclusion of multiple datasets (including gig driver or the public input) can be informed by multiple perspectives and lead to better processes and recommendations.

Here’s what we found during initial exploration for data with trips occurring in the southern California region:

1. Travel convenience still trumps all.

During the pandemic, people were willing to pay slightly more for short-distance trips, a trend that continues even though most consider the pandemic over, as shown in the increase of trips taken in the graphs below. People continue to be willing to pay more for ride-hail services to avoid the hassle of tasks like finding parking, sitting in traffic behind the wheel, or running errands. The more people use these services, the more they seem willing to continue doing so, even for short trips or small errands. Possible factors influencing the decision that could be investigated are the amount of time at the destination (i.e. visiting friends, going to work, or picking up food) relative to the amount of time driving and parking.

2. Transit access redefines choices.

Those with transit access were less willing to pay more or spend more time ridesharing before and after the pandemic than during the pandemic, as shown in the graphs below. This may be due to changes in transit service levels, or the sense of personal health risks associated with public transportation. Those without transit access tended to take rideshare slightly more during the pandemic, with the amount of time willing to spend traveling remained roughly the same. This may be due to the sense of health risk during the pandemic increasing willingness to pay, but the tolerance for longer travel times did not change. The number of people taking the trip may also influence the decision since most transit operators have a per-person fare rather than a per-trip fare of most rideshare companies.

3. Taking a Step Back.

As we navigate the ever-changing landscape of gig economy services, the potential applications of this unique DriversSeat data are vast. It’s no longer just for the benefit of gig companies. Transit agencies, city planners, those involved in Vehicle Miles Traveled (VMT) mitigation, as well as individuals focused on revenue and curb space management, all stand to benefit from the transparent and varied perspectives in this data to make decisions.

This analysis also raises further questions about equity and societal impact. Consider, for instance, when someone orders on-demand groceries or food delivery, what areas and populations most benefit and are potentially most affected? How much are people willing to pay to avoid the inconveniences of driving or shopping in person? Can safety become an issue if ride hail drivers, seeking to earn adequate pay, engage in risky behaviors like speeding or disregarding driving rules to complete more jobs? How should street design accommodate ride hail and delivery services, and what potentially, does the space needed to do so come at the expense of?

Are you interested in exploring the practical applications of this data or talking more about tradeoffs, including the implications of these findings? Let’s start a conversation.