1 Introduction
Trucking
and taxi companies equip vehicles with history keeping devices in order to keep
track of the hours and mileage driven by their employees. Car mechanics can analyze automobile driving
histories to facilitate the maintenance of cars. These successful
histories for business professionals (Kiger et al., 1997; Sanquist et al. 1993)
led us to explore uses of
history information for commuters.
In this paper we focus on the design of a system to help drivers select the best route for their common commutes. Daily traffic congestion during rush hour in many big cities is a growing problem and drivers have to choose the most desirable start time and route. Every day their experience helps them plan their commute to and from work, as well as regular trips to airport, train station, shopping, friends, family, etc. However, most people rely on trial-and-error and have difficulty keeping track of the day-of-the-week or time-of-day variations among trips. Finally, people have different criteria for the best route: the fastest, the fewest stops, the best fuel savings, or lowest average speed.
Our positive experience with
history systems in learning environments (Plaisant et al., 1999) encouraged us
to apply them to other domains. Driving history records could also help novice
drivers review their driving technique and compare their speed and trajectory
with those of experienced drivers.
2 Description of the Interface
We define a commute as any
regularly traveled pair of origin-destination locations (e.g. home to work,
home to parents’ house, work to airport, etc.). We assume an onboard navigation system with a date-time
clock. Each time the car travels one of
the previously specified commutes, the system records the history of that trip
and updates the summary information. A privacy control would disable history
logging or delete logs. Once a sufficient number of trips have taken place, the
user can review the summary data on the car display or their home computer to
spot patterns for travel time, fuel consumption, stop frequency, total stopped
time, distance, and maximum speed. As
the driver takes alternative routes for a given commute, the system keeps track
of the data for each route separately.
We designed the
prototype display for a touchscreen monitor with 640 x 480 pixels, a likely resolution
for the next generation of car instrument panels. It has a graph (Fig. 1), map (Fig. 2),
table, and change criteria display. A simple tab system allows users to change
displays with one touch. Each display allows users to select the commute and
the day/hours for the trip. The default
initial display is a graph showing all the recorded data necessary to choose a
route for a given departure time range.
The users can see the best route at the top of the list, as well as the
fastest route, lowest fuel consumption route, etc.
Figure 1: Comparison
of routes for a commute. For each parameter, the indicators
are stacked to facilitate comparison.
The best route in each column is highlighted with a brighter border.
When users specify new commutes to be recorded they
are asked to choose or type names for the end points of the commute (e.g. work,
airport etc.). Since the location is
tracked by GPS (Global Positioning System) users will most likely recognize the
shapes of the routes on the map display (Fig. 2) but of course users can enter
the names of the routes themselves or confirm names picked automatically from
GISs (Geographical Information Systems).
Figure 2: Map showing
the three alternate routes
A third display shows the numerical values in a
table. These numerical values include average, minimum and maximum values of
the trips recorded till then. A click on an average value brings a table with
the detail log.
3 Discussion
Since
comparing repetitive events over weeks or months is difficult for most people,
this system could help users in optimizing their daily commutes. Once a
sufficient number of trips have been logged, users will have the data to make
thoughtful comparisons and better decisions.
They can choose to save time, reduce frustrating waits, conserve fuel,
or reduce mileage.
Potentially, a service could accept data from
multiple drivers and produce a database for all users to share, while
preserving privacy. New ITSs (Intelligent Transportation Systems) can supply
information from outside of the car, and accidents or road work information
could be used to update the route comparison.
Our prototype provides the information to choose the best route given a
known start time window, but providing the best route and start time for a
given commute might be possible for users with flexible work hours.
References
Plaisant, C.,
Rose, A., Rubloff, G., Salter, R., Shneiderman, B. (1999). The design of
history mechanisms and their use in collaborative educational simulations Proc.
of the Computer Support for Collaborative Learning, CSCL' 99, Palo Alto,
CA, p. 348-359.
Kiger,
S., Rockwell, T., Tijerina, L. (1995). Developing baseline data on heavy
vehicle driver visual workload. SURFACE TRANSPORTATION: Heavy Vehicle Driver
Workload Assessment [Symposium] Proc. of the Human Factors and Ergonomics
Society 39th Annual Meeting 1995 v.2, p. 1112-1116.
Sanquist,
T., Lee, J. (1993) Voyage planning and track keeping with Paper and electronic
charts: A case study of maritime navigation tasks GENERAL SESSIONS: Surface Transportation:
Proc. of the Human Factors and Ergonomics Society 37th Annual Meeting 1993,
v.1 p. 564-568.
*
Makoto Konishi is a research visitor from Toyota Motor Corporation.