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User-centered travel behavior intervention technology to empower commuters to switch car trips to transit and active travel

Adaptive Artificial Intelligence as the foundation for personalized interventions to make transit & active travel accessible and attractive

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Why are we doing this?

Travel behavior change interventions have had no demonstrable significant and sustained reductions in car use (and associated increase in transit use and active travel). This is primarily due to their intrinsic inability to provide truly personalized and realistic recommendations for change.

Why commuters?

The 2017 American Community Survey shows that around 65% of trips to/from work in Pittsburgh are by car. Even though this is significantly better than the national average of around 87%, there is still a marked dependency on the car for commuter travel.

Why should employers care?

Physical inactivity has been linked with lower levels of productivity at work and higher levels of absenteeism. Strong evidence shows that good employee health and well-being boosts organizational health. There are numerous reasons why promoting worker health and well-being makes good business sense. Put simply: healthy workers = healthy organizations = healthy business performance.

Recent studies show that an increase in physical activity of over one hour per week (which can be easily achieved through a switch from car trips to walking, cycling or transit), would be expected to lead to a measurable reduction in levels of absenteeism and a measurable increase in productivity. This is of clear commercial benefit to employees and supports the business case for investing in programs to promote travel behavior change for commuting (and other) trips. The greatest benefits in reduced absenteeism were achieved by encouraging employees who are currently very inactive to take up between one to two hours of physical activity each week – a level of activity readily achievable through changed travel behavior. Importantly, the evidence review highlighted active travel as being potentially more cost effective than other approaches to increasing employee physical activity levels, especially among those less amenable to organized workplace activities. This supports other behavioral research that demonstrates that 15-20% of car drivers in the United States wish to reduce their car use, and that health, weight management and exercise are often primary motivators.

Our Solution

Our team will deploy a user-centered smartphone-based travel behavior change intervention program that will empower commuters to use transit, bike and walk by providing personalized recommendations that are commensurate with their individual capabilities. The primary focus of our program is to transfer trips: (1) from motor vehicles to any other travel mode or combination of modes; and (2) from transit to walking and cycling. 

The platform includes two primary components: (1) activity detection system; and (2) travel behavior change platform. The integration of these two components will enable our team to provide commuters with fully personalized recommendations for change that are commensurate with their individual capabilities and embedded into their daily routines. The importance of providing realistic recommendations (in contrast to the vague and generic nature of information provided by all exiting travel planning tools and fitness devices) is that it will truly empower people to shift trips (or portions of them) from motor vehicles to sustainable modes.

Our team has already developed, in collaboration with a leading data science company, an adaptive Artificial Intelligence world-class activity detection system that uses information collected from smartphone sensors (e.g., accelerometer, magnetometer, gyroscope and GPS) to determine when/where/how/why people travel. More specifically, our system determines (to a high level of precision): (a) origins and destinations of all trips (e.g., home and school, home and work, or work and shopping); (b) routes traveled; (c) real door-to-door travel time for all trips; and (d) travel mode(s) used (i.e., whether people walked, rode a bicycle, traveled on bus, tram or train, or drove, or used a combination of two or more modes). This fine-grain and person-level information enables the identification of true/revealed travel patterns for each individual – taking the guesswork out of travel behavior change efforts.

Only by knowing (to a high level of precision) what people do today, we can understand (and begin to influence) what people may be able and willing to do tomorrow. This is not trivial, as lack of time has been demonstrated to be one of the primary deterrents to exercise across all sociodemographic groups. In recognition of this, health agencies in the US and around the world explicitly aim to promote transit and active travel. More specifically, since people struggle to find the time to exercise, transit use and active travel have been identified as efficient means of incorporating physical activity into our daily lives. If we continue to ‘ignore’ daily travel as a potential primary component of physical activity, we will miss out on an enormous untapped opportunity that can help all people (and not just those who are already healthy/fit or health/fitness conscious). Put simply, most people travel each day while most people do not meet physical activity guidelines (around 80% of adults do not meet the government’s guidelines according to the Centers for Disease Control and Prevention). In Pennsylvania, close to one quarter of the adult population does not engage in any physical activity. Our system directly addresses this critical need by explicitly identifying opportunities to embed walking and cycling into daily trips. In doing so, we can now tell people how to achieve their goals as part of their daily life, instead of simply prompting them to achieve them.

In collaboration with the Motion Analysis Laboratory of the Harvard University Medical School, we have developed algorithms (through simulations and inference) that determine personalized/desirable/safe physical activity thresholds (i.e., maximum number of daily steps, minutes and kilometers per day and per individual event) based on people’s age, gender, weight, height and health condition. This is the result of simulations for thousands of patients over many years.

By combining the physical activity thresholds (what individuals can actually achieve in terms of walking) with information from the activity detection system (what individuals actually do on a routine basis), we identify those trips (for each individual person) that are realistic candidates for ‘change’ (based on trip characteristics, individual capability and time constraints). These trips are those for which it is feasible to: (a) switch from car (private, taxi, Uber/Lyft or shared mobility) to transit, bike or walking; or (b) switch from transit to walking. Only trips for which a shift results in time gain or minimal additional travel time are considered. For example, long trips with no feasible and efficient transit options are ‘ignored’, whereas short trips that are eminently walkable (or for which viable time-competitive alternatives are available) are ‘considered’. Importantly, since our system identifies the true origins and destinations of trips, as well as the subcomponents of trips, it also identifies opportunities for physical activity by switching portions of longer trips (e.g., walk the last segment of a trip to work or alight the tram a stop earlier and walk the ‘rest of the way’).

Our team has also developed a user-centered behavior change platform that includes gamification and peer influence elements, as well as a ‘virtual bank of positive travel behavior change transactions’ to provide support to financially disadvantaged members of the community (to be identified in collaboration with the City of Pittsburgh). More specifically, each time a person switches from driving to transit or from transit to walking, a credit is added to the virtual bank. Credits are accrued over time and used to provide access to transport services for financially disadvantaged people. We will do this by offering free transit passes, sponsored by the Challenge funds, to financially disadvantaged people as a ‘reward’ for participants using the platform and changing their travel behavior. This feature will encourage behavior change among some participants (motivated by altruistic goals).

We will undertake a free-living conditions pilot study with 15-20 employees from Metz Lewis Brodman Must O’Keefe, a Pittsburgh-based law firm. We will evaluate the system’s capability to promote transit use and active travel for commuting (and other trips). Upon recruitment of the study participants, we will provide each person with a smartphone with the app installed. Our system will use the algorithms in the activity detection system and the signal from smartphone sensors to: (1) detect the participant’s travel mode; and (2) display in real-time the real location of the participant and of a ‘virtual person’ travelling on a realistic alternative travel mode (e.g., if the participant is travelling by motor vehicle, the ‘virtual person’ is travelling on transit or walking). The two locations will be displayed on a screen with two parallel lines showing the start and end of each journey (e.g., home and shops). At the end of the trip, the app will display ‘race statistics’, including total door-to-door travel time, time waiting (e.g., congestion and searching for parking), time in motion, steps walked, minutes of physical activity, calorie expenditure, emissions and energy consumption estimates, and cost (e.g., estimate of gasoline consumption or cost of transit ticket).

The participants will be given prompts prior to initiating trips, reminding them of the outcomes of their travel choices (in terms of time and other parameters). Participants will be ‘tracked’ using smartphone sensors and the activity detection system to determine the mode of travel used. The app will include gamification and peer influence elements, enabling participants to anonymously connect to the ‘community of participants’ and compete with themselves and each other (e.g., gain points for achieving personal goals or ‘out-competing’ other participants). In addition, our system will include the ‘virtual bank of positive transactions’ to encourage positive change motivated by altruistic goals.

The behavior change program will include the following stages: 1) data collection to establish current travel patterns, including mode of travel (e.g., car or walk), route, parking location (at destination), average walking/cycling speed and true door-to-door time (i.e., home to entrance of office building) when travelling by motor vehicle, transit and when walking or cycling. 2) test the potential to influence behavior change simply by providing information about outcomes achieved by switching to transit, walking or cycling. 3) test the potential to influence behavior change through a combination of information and peer influence/gamification. 4) test the potential to influence behavior change by including altruistic incentives via the ‘virtual bank of positive transactions’.

Why our solution will work?

Our team recently undertook three validation trials to determine the potential of personalized information to influence travel choices. In short, our validation trials were a resounding success. We now need to transfer this knowledge to key target beneficiaries and scale the delivery of our innovative system.

The three pilot studies included over 120 participants in Melbourne (Australia) – two studies focused on shifting trips from motor vehicles to transit for commuting trips, and the other on shifting trips from transit to walking for the last segment of commuting trips. Participants were provided with a smartphone-based platform that displayed in real-time their location (on their routine mode of travel) and the location of a person undertaking the same trip on an alternative mode. The platform presented race statistics at the end of each journey, providing comparisons in terms of total door-to-door travel time (including the traditionally forgotten parking element), minutes ‘stuck in traffic’, minute of physical activity, calorie expenditure, emissions and cost. Participants were given prompts before initiating their journey ‘reminding’ them of the outcome of their travel decision. We tracked participants over a period of four weeks and achieved unprecedented levels of behavior change – 45%, 54% and 68% in the respective pilots. By comparison, multi-million-dollar and multi-year travel behavior change programs are considered successful when they achieve levels of change approaching 2-3%. Importantly, the change we achieved was revealed through the technology and not simply stated by users (as is the case for most trials to date).

Our team also undertook tests for short-, medium- and long-distance trips in inner Melbourne and middle suburban areas to determine the true door-to-door time of trips by motor vehicle (including the parking and walking components), on transit (including the walking segments at the beginning and end of the journey), by bike and on foot. The study revealed that there are a large proportion of trips (of a wide range of distances) for which transit is highly time competitive with the car. In addition, there are a large proportion of trips (up to around 3 miles in length) for which walking and cycling are highly competitive with all motorized modes. The travel time estimated using existing tools overestimated the walking, cycling and transit journey times (including the walking components) and underestimated the time when traveling by car (and completely ignored the parking and walking components at the end of the trip). This study demonstrates the need for providing real door-to-door time information (such as the one generated automatically by our system) to enable people to make informed decisions about their travel choices. This information alone has the potential to immediately make walking, cycling and transit more attractive and appealing than they appear to be using current tools.  

How does our solution address the opportunity area?

Our system, focused on promoting travel behavior change, is deliberately designed to make transit and active travel attractive, accessible and enjoyable options for commuters. The primary outcome will be a system that provides real-time travel recommendations for people to use transit and walk/bike more, while enabling them to (in all probability) arrive at their destinations even earlier (or at worst within a few minutes later than their current travel mode of choice - driving).

Our system will promote an increase in physical activity for those people who change their behavior from driving to any other mode. Research shows that transit is an important contributor to daily physical activity through stop access and egress time; in fact, 29% of transit users meet existing physical activity recommendations solely from walking to and from transit. This change will provide a tangible health benefit, while promoting a reduction in congestion and emissions associated with motor vehicle travel.

Pilot Program

We propose to work closely with the City of Pittsburgh and the City of Tomorrow team to develop an appropriate pilot program as follows:

  • Recruitment of program participants from Metz Lewis Brodman Must O’Keefe, a Pittsburgh-based law firm
  • Funds to be allocated towards travel behavior change platform licensing costs (at a discounted rate), participant recruitment, outreach and credits for the virtual bank of positive transactions (to benefit disadvantaged members of the community – potential beneficiaries to be selected with the City of Pittsburgh)
  • Pilot period of three to six months

We have a strategic alliance with a Pennsylvania-based transportation engineering firm that will support the program as appropriate.

The platform licensing for the City of Pittsburgh includes the following:

  • A world-class travel behavior change intervention program that provides fully personalized active travel and physical activity recommendations for commuters that are commensurate with their individual capabilities and embedded into their daily travel routines.
  • An adaptive Artificial Intelligence activity detection system
  • A physical activity threshold estimator
  • A peer influence, gamification and altruistic platform
  • Cloud-based architecture and easy integration with third-party travel planning tools and health insurance incentive program apps

Describe who will use your solution (1,000 characters)

The target beneficiaries of this initiative are employees from Metz Lewis Brodman Must O’Keefe, a Pittsburgh-based law firm. Physical inactivity is linked with higher levels of absenteeism and lower levels of productivity at work. As such, employers everywhere are seeking ways to promote healthier lifestyles for their employees. For this particular Challenge, we will recruit 15-20 employees from Metz Lewis Brodman Must O’Keefe. The inclusion criteria will be employees who: 1) self-report as healthy enough to walk or ride a bicycle for at least 30 minutes; 2) live within 2 miles of a transit stop for a service that can bring them to work; and 3) generally travel by car for commuting and a large proportion of other trips (self-reported and to be validated with our activity detection system). Importantly, the framework/system to be piloted as part of this Challenge will be widely applicable and replicable with commuters across Pittsburgh, Pennsylvania, the US and internationally.

Describe your solution's stage of development

  • Prototype - you have built a prototype and tested it with potential users

Tell us about your team or organization (500 characters)

Our team includes leaders in data science, AI, technology/software development, dynamic optimization and medicine. For the Challenge, our team is: movendo (an Australian consultancy), Harvard University’s Motion Analysis Laboratory and Metz Lewis Brodman Must O’Keefe, a Pittsburgh-based law firm. The Challenge will bring together organizations from distant places with a common goal – to promote sustainable and healthier outcomes. Without the Challenge, this partnership would not be possible.

Size of your team or organization

  • 2-10

Funding Request

  • $100,000

Describe how you would pilot your idea (1000 characters)

Challenge funds will be used to help cover the costs of running the proposed pilot, including travel behavior change platform licensing costs (at a discounted rate), participant recruitment and outreach. Challenge resources will also be used to partially fund the ‘virtual bank of positive travel behavior change transactions’ to benefit disadvantaged community members (to be selected with the City of Pittsburgh). Importantly, the technology components are fully developed and ready to be deployed (after minor necessary adjustments to meet local conditions) – no Challenge funds will be used for technology development. A successful pilot will result in significant commercialization opportunities. The two primary channels will be: 1) technology licensing and/or intervention programs for the City of Pittsburgh after the pilot has been completed; and 2) technology licensing for corporations and health insurance/wellness organizations to promote healthier employee outcomes.

Describe how you would measure the success of your pilot (1000 characters)

During the 3-6-month pilot, we will measure vehicle-miles-traveled (VMT), proportion of trips by travel mode and distance/steps/time walked will be the primary metrics. Our activity detection system automatically measures all three, enabling us to readily determine the pilot’s success. VMT changes will be combined with data about the vehicles used by each participant to estimate reductions in emissions/fuel consumption. VMT changes will also be used to estimate personalized reductions in road crash exposure. Increases in walking/cycling will be used to estimate personalized physical activity statistics (i.e., calorie expenditure and reductions in cardiovascular risks – using CDC data). Individual benefits will be aggregated to determine the pilot’s impact and estimate potential city-wide impacts. Surveys will be undertaken during/after the study to determine factors that motivate and/or inhibit travel behavior change, critical to expand the reach and replicability of the initiative.

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Hi Jose! Welcome back and thanks for posting before the deadline - Katie - Facilitator