leveraging ubiquitous computing for empathetic routing With the increase in automated driving
assisting systems, the vehicles in cabin space is gradually transforming into a mobile built
environment, in which the drivers and passengers spend less time focusing on driving and more time
on other activities. The changes in scenery, traffic patterns, noise level, and even road
type may significantly impact driver's state, comfort, and behavior. For instance, research
suggests that road features, road type, traffic condition, and driver's familiarity with the road
can positively and negatively impact drivers' emotional state, and behaviors. This suggests
route options could significantly influence the driver and passengers' emotional states
prior to reaching their destination. However, the majority of routing services provide options only
based on identifying the fastest route or shortest distance to the final destination.
In this research, we propose a framework for humanizing routing services. Through our proposed
empathetic routing framework we categorize different routes and locations in a city
based on how they affect drivers' stress levels. The empathetic routing framework consists of four
sections. In section A we collect data using conventional wearable devices, which includes
driver's biomarkers, location, hand movements, and other behavioral or environmental data. We
send these timestamped drivers' behavioral and physiological data, as well as the GPS locations,
to a computing unit. In the computing unit we perform pre-processing such as standardizing
heart rate and calculating heart rate variability features, which has been shown to be correlated
with the stress levels. We then match heart rate and GPS locations based on timestamp
and we find the associating infrastructural elements to each GPS point using a mapping API.
From that, we create a database of different infrastructures that could be associated to high
heart rate concentrations. Once a user requests routing, we first assess if we have enough
data from that user. If we do, we perform route selection based on users' personal inputs or users
heart rate data from the past. If not we use the created database and we basically provide routing
based on the aggregated data from all users to avoid high heart rate concentration areas.
For instance, we collected data from 21 participants in the city of Charlottesville
and you're looking at the standardized heart rate throughout the city of Charlottesville
in different days and months of the year.
Based on the GPS locations we also provide a heat map of heart rate in the city of Charlottesville,
where we can see different locations that are associated with higher
or lower stress levels. We then use a case study to provide the application of Empathetic Routing
for ranking three routes between points a and b in the city of Charlottesville. We have three
candidate routes and we compare these three routes based on historical heart rate data.
Route 1 passes mostly through a highway. it has a relatively higher speed limit, more noise,
and fewer intersections. On this route, the driver has to drive through three busy intersections,
two ramps, and two mergings on or off highways.
Route 2 passes through a residential neighborhood that has a relatively lower speed limit, a lower
noise level, and a higher number of intersections. On route 2 the driver passes through one ramp,
two busy intersections, and one fairly tight road segment, which has a higher traffic density.
Route 3 passes through the main streets of the city. It has a relatively average speed limit
and medium noise level. On route 3, the driver passes through lower traffic density areas and
four intersections. To choose between these three routes we perform a Welch t-test. As you can see
the historical HR data is used for the comparison and the Welch t-test shows that route number 3
has the least HR standardized value, which means that it is the less stressful for the drivers.
Although in this study we have only demonstrated the application of driver's heart rate,
multiple other measures such as driver's hand movements, or light and noise in the
environment provided by the smart wearable can also be considered. The outcome of the
presented framework can be used by city planners to provide a more holistic overview
of the effect of different areas of the city on the drivers or choosing different design options
to potentially decrease driver's stress level. Additionally, in our study we have not considered
the differences between the hours of the day, different genders and driving experiences while
choosing routes. Contact us if you are interested in our research, uvabrainlab.com