Commuting is more than commuting.
In my 2009 Adapt-A-Ride project, I interviewed 30 users around Pittsburgh and surveyed 240 people nationwide, and discovered the richness of the ride sharing challenge. Commuting is a time for reflection, learning, organizing, and sharing. Participants touched on the themes of ‘me-time’, ‘family-time’ and ‘flexibility’ describing their preferences.
My concept design takes on the ‘flexibility’ theme as its principle. It suggests ways in which people can find time for rich experiences during their commuting — fulfilling their experiential needs — while also finding commutes that fulfill their functional needs around cost, timing, and convenience. Casual ride sharing emerged as a promising sweet spot for future efforts addressing alternative transportation methods.
The initial goal of my research was to understand the dynamics of commuting and unveil both individual and contextual aspects affecting individuals’ commute choices.
We deployed an experience design framework, called modes of transitions (MOT), and framed commuting as a transition problem. Modes of Transitions is a practical framework for understanding issues that arise around transitions and translating them into product ideas. A transition can be defined as the activity of changing from one mode to another and happens through daily and long-term interactions.
Commuting involves a spatial transition, traveling from A to B. It also involves role transitions of switching between family and work roles, and adapting from solo driving into a ridesharing role. While bridging different daily contexts and life stages, transitions carry threats to a person’s well-being: they can destabilize people’s routines, discord their role enactments (home, work), drain the meaning away from people’s rituals, and threaten people’s identity.
In the understanding phase, we carried out semi-structured interviews with thirty commuters in a university campus setting, including solo drivers, carpoolers, and bus riders, followed up with a nationwide survey. Our main findings were that convenience, cost, commute time, and personal preferences motivate commuting choices. Participants characterized their best commute times as when they are experiencing “me-time,” “traffic-free time,” or “ritual time”; and their worst experiences when there is a traffic-jam or a socially awkward situation. We followed up interviews with an online survey on commuting choice and collected responses from 240 participants across the United States. We found that our previously observed motivations remained significant in the larger population. However, we also observed that individuals who most valued convenience and flexibility tended to be least motivated by cost.
In the translation phase, we generated 13 ride sharing service concepts and assess them in a series of speed dating sessions. Based on user feedback, we refined the most popular concepts and developed a concept service — Adapt-A-Ride, and evaluated its interfaces as a paper prototype in a laboratory study.
We developed a service design concept addressing flexibility, incentives, and dynamic information feed. Adapt-A-Ride is a service design that helps people to coordinate casual ridesharing and provides an incentive program for them to engage in habitual change. It provides an online social network platform built on a website, a mobile device to match drivers and riders, and a reward card for collecting incentive points. For this phase of study, we decided to focus on the idea of an online social network using the website component.
In our interview and survey results, we identified five profile features people care about in their ride mate preferences: age group, gender, affiliations, social networks (Facebook, IM, Twitter, etc.), and interest groups. We designed a profile page, where people can input their preferences for their ride mates.
To explore the users’ flexibility in depth, we designed flexibility controls on time, location, and ride mate (Fig 2). People can input their time and location flexibility both for departure and arrival. They can also indicate whom they want to ride with: friends, friends of friends, affiliates, interest groups and service members (strangers).
We envision an algorithm that can respond to individuals’ flexibility preferences with dynamic scenarios, providing people flexibility choices for time, location, ride mate, and incentives. Figure 2 shows the template we used in our study and displays rideshare choices.
Each rideshare option provides dynamic information on whether the person is taking or giving a ride, ride-mate candidate, time and location proximity and the cost of the ride.
- Ride 1 is a ride in which the participant would give a ride to a stranger and who would share costs, however the driver would arrive later than their specified arrival time.
- Ride 2 is a ride in which the participant takes a ride from a friend of a friend but needs to meet the other person at a certain distance from their starting point.
- Ride 3 is a ride in which the driver participant gives a ride to a person from her interest group but waiting for a certain time at the starting point.
- Ride 4 is a ride in which the rider participant takes a ride from a stranger driver but will be dropped of at certain distance from the arrival point.
- Ride 5 is a ride in which the driver is using his/her own car.
- Ride 6 is a ride in which the participant takes a taxi.
Based on our speed-dating study, we identified the relevance of incentive programs for ridesharing and decided to explore its effect along the other flexibility dimensions. We follow an approach similar to Nuride: an individual can get discounts from local stores by using alternative ways of commuting such as ridesharing, bike, or public transportation. However, we made the means the program open to probe participants’ preferences.