Machine Learning Approach to
Report Prioritization with an
Application to Travel Time
Dissemination
Piotr Szczurek
Bo Xu
Jie Lin
Ouri Wolfson
Agenda
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Background
Model and Problem Definition
Machine Learning Approach
Application – Travel Time Dissemination
Results
Conclusion
Background
• Technology in vehicles
– Computers
– GPS
– Communication devices (802.11p, C2C)
• Sensing of environment
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Video cameras
GPS
Temperature
Automobile status: break sensors,
accelerometers
Background
• Dissemination of information
– Limited by connectivity and bandwidth
• Store-and-forward communication
– Information is stored in a local database of limited
size
– Addresses connectivity issues
• Prioritization
– Not all information may be communicated
– Not all information may be stored
– Need to select most useful information to be kept
and communicated
Model and Problem Definition
• System:
– Set of mobile nodes: physical entity capable of data
computation, storage, and short range wireless communication
– Nodes observe environment through sensing device (e.g. GPS)
• Reports:
– Data derived from the sensing device
– Fixed set of attributes and their values.
• (all reports have fixed size)
– Created over time by nodes
– Examples:
• Speed report (average speed, timestamp, vehicle id)
• Parking space report (parking meter id, availability)
– Once created, stored in report database
Model and Problem Definition
• Report database
– Local database maintained by each node
– Limited in size
• Communication
– Reports stored in the database are communicated
over time to a subset of other nodes in the network
– Broadcast communication: reports are sent to all
nodes within transmission range
– Communication protocol: decides when and how
many reports to broadcast
– Remaining question: which reports should be
broadcast?
Model and Problem Definition
• Relevance value
– Utility a report holds when it would be sent to other nodes,
given the sending node’s current characteristics and the
attribute values of the report
– Highly application specific, difficult to specify
– Value of a report can change over time
– Can be a range of values (0..1) or Boolean (0 or 1)
– Example: parking space availability (0 for occupied, 1 for
not occupied)
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What to broadcast and keep in report database?
– Find relevance value for each report and keep (or
broadcast) the highest valued reports
• Problem: finding the relevance value of a report
Machine Learning Approach
• Idea: use received reports as input to a machine learning
process
• Assumptions:
– Nodes can judge the relevance of a report after it is received
– Relevance value is based on a goal common to all nodes
• Method description:
– Define a goal on which the relevance value is based
– Relevance value of a report is then defined based on how close
the report achieves the goal
– For every incoming report, use report’s attributes and sender’s
characteristics as input values. Use relevance value as output.
This creates a training example.
– Use a supervised machine learning algorithm to find a model for
mapping inputs to outputs.
– Use learned models to find the relevance value of a report
Machine Learning Approach
• Two ways of learning:
– Online: models are updated while training
– Offline:
• First, collect training examples
• Second, use learned model
• Offline learning
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Advantage: Nodes do not incur overhead of learning
Disadvantage: model is not adaptable
Can also be used to bootstrap online learning
Used for finding useful attributes
• Research questions:
– Can the relevance value be learned?
– What advantage does the learned model offer?
Application – Travel Time
Dissemination
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Assume every vehicle in system carries GPS, on-board computer with
communication capabilities (e.g. 802.11b)
Each vehicle has a known destination to which it travels along the shortest
path
Vehicles measure travel times on road segments as they traverse them
Travel times are encapsulated by reports. Each report contains:
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Report ID
Road segment ID
Travel time
Time of measurement
Reports are stored in reports database of a limited size (200 reports)
Reports database is a list of all received or generated reports. List is ranked
by ranking function. If database size is exceeded, lowest ranked report is
discarded.
Example of ranking function: r=1/ageOfReport
Application – Travel Time
Dissemination
• Reports are disseminated over VANET
• Incoming and newly generated reports are used to
update a digital map
• Digital map contains:
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Road segment identifier
Coordinates of the segment endpoints
Road type
Travel time estimate (average of all reports for latest time
interval; initially free-flow)
– List of reports used for the estimate
– Time period number (indicates 5-minute interval; initially 1)
Application – Travel Time
Dissemination
• Travel time updates
– Executed at end of each 5-minute interval
– All reports generated or received within that interval are used
– For each road segment in digital map:
• Reports for the most current period are identified. All others are
discarded.
• Report period number is then compared with that in digital map:
>: Time period is updated and all reports are inserted in list. Travel time estimate is
average of all inserted reports.
<: All reports are discarded
=: All reports are inserted in list; duplicates are discarded. Travel time estimate is
average of all inserted reports.
• After each update, vehicles recalculate the shortest path to
their destination
Application – Travel Time
Dissemination
• Communication
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Based on TrafficInfo algorithm
Combination flooding/periodic broadcasting
Flooding for freshly created reports
Periodic broadcasting of subset of reports from report
database. Subset is chosen based on ranking function.
Highest K ranked reports are chosen.
– Size of subset (K) is determined by Good Citizen Formula.
• Based on transmission range, node density, and last broadcast
time
– Broadcast period is determined based on transmission
range and vehicle velocity
Application – Travel Time
Dissemination
• Example:
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Vehicle A just traversed road segment 123 at time of 1:04pm
(time period 2). The recorded travel time was 10 minutes.
Vehicle A creates a report with ID 1, using the measured travel
time. Report contains:
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Report ID (1)
Road segment ID (123)
Travel time (10 minutes)
Time of measurement (1:04pm)
Vehicle A updates its digital map at 1:05pm. Currently, it holds
no reports for segment 1. The following changes are applied
for road segment 123:
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Travel time estimate = 10 minutes
List of reports: [report 1]
Time period = 2
Application – Travel Time
Dissemination
• Example (continued):
4. Vehicle A broadcasts report 1 at 1:06pm (in
time period 3).
5. Vehicle B receives the report.
6. Vehicle B updates its digital map at 1:10pm. It
currently has one report (report 2) for segment
123, with travel time of 11 minutes, for time
period 2. The following changes are applied for
road segment 123:
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Travel time estimate = 10.5 minutes
List of reports: [report 1], [report 2]
Application – Travel Time
Dissemination
• Learning the ranking function (offline)
– Goal of application: vehicles choose the best (shortest)
paths to their destinations
– Relevance of a report: report is good when it changes the
shortest path
• 0 if report does not change path
• 1 if report changes shortest path
– Attributes:
• Age of report in time periods
• Distance to road segment in terms of free-flow travel time form
vehicle’s current position to the road segment contained in
report
• Road type: either highway or city street
Application – Travel Time
Dissemination
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Learning (continued)
– Learning examples created artificially by emulating different scenarios
– 25 learning epochs:
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Each epoch had vehicles placed randomly on a road network (region of Chicago)
Random destination for each vehicle
All vehicles have digital map with one report containing free-flow travel time and random
period number between 0 and 100
Random segment is chosen from the road network. Its travel time is chosen from a uniform
distribution between 0 and free-flow travel time
101 reports are created for each vehicle with ages 0..100
Each report is about the chosen road segment and contains the assigned travel time
Every vehicle applies the 101 reports independently. After each is applied it is checked
whether the shortest path would change.
If report would change path, a positive training example is created; otherwise a negative
training example is created
– Two road networks were used (from different regions of Chicago). On smaller region,
100 vehicles were used; 250 was used for the larger region.
Application – Travel Time
Dissemination
• Learning (continued)
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Weka learning toolkit was used for learning
Negative examples were downsampled to match positives
7677 positive and 7677 negative examples
5 classifiers were tested:
• Naïve Bayesian (NaiveBayes Weka implementation)
• Logistic Regression (using Logistic Weka implementation)
• Support Vector Machines (using SMO Weka implementation, w/
buildLogisticModels enabled)
• Artificial Neural Network (using Multilayer Perceptron Weka
implementation)
• Decision Tree (using J48 Weka implementation)
Results
• 10-fold cross validation
• All algorithms, with exception
of decision trees, performed
similarly with an accuracy of
approximately 83-84%
• The decision trees had the
best accuracy of 96.22%
– But unusable model: complex
tree with most leaf nodes being
homogeneous
• Logistic regression model most
understandable:
U = -0.0322*age - 0.02*distance +
0.3885*[road=highway] –
0.3885*[road=city street] +
4.9053
Results
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3 logistic regression models were derived:
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Using region 1 examples
Using region 2 examples
Using examples from both regions
No major difference in coefficients for all models, except for road type
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This shows that the relevance values are dependent on the makeup of road
network
Usefulness of derived models in report prioritization
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Tested SWANS/STRAW simulator
100 vehicles were randomly placed in region 1. Each travelled to random
destinations for 1 hour.
Majority of highway segments had reduced speed limits (simulated accident
scenario)
Number of broadcasted reports limited to 10
Two evaluation metrics were used:
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Average Trip Time: average time to reach destination
Total Path Travel Time Difference: calculated by taking the absolute value of the difference
between travel time along the shortest path given vehicle’s current knowledge and full
knowledge
Compared to common heuristics (1/(age+distance) used by TrafficInfo)
Results
Results
Conclusion
• Proposed a machine learning approach to report
prioritization for use in peer-to-peer environments
• Uses incoming reports in order to provide input to
supervised machine learning algorithms
• Learned model can then be used by all nodes in
order to rank the reports to be disseminated
• Accurate prediction is feasible
• Learned model outperformed heuristics in terms of
disseminating the information most likely to affect
the vehicle’s path