TrafficView: A Scalable Traffic Monitoring System Tamer Nadeem, Sasan Dashtinezhad, Chunyuan Liao, Liviu Iftode* Department of Computer Science University of Maryland, College Park *Now with Rutgers University

2 TrafficView Enable drivers to see vehicles in front of their cars, farther than they can see, in real-time Use vehicle-to-vehicle ad hoc networks

3 How TrafficView Works Each vehicle has an embedded system –GPS receiver (location, speed, time) –Short-range wireless NIC –On-Board Diagnostics interface (optional) Receive data from remote vehicle Non-validated dataset Validate Validated dataset Local data Display Broadcast data

4 Need for Data Aggregation Ad hoc networks of vehicles are dynamic Data propagation must be simple Send all data in one packet (up to MTU) Use data aggregation to put as much information as possible in one packet

5 How Far Can You See? Problem –How to aggregate data to see vehicles as far as possible with “acceptable” accuracy loss Natural Solution –Aggregate data for vehicles that are close to each other –Perform more aggregation as distance increases

6 Outline Motivation and Problem Definition Data Representation Aggregation Algorithms Evaluation Conclusions and Future Work

7 Data Representation Vehicles store records: –Vehicle ID (ID), position (POS), speed (SPD), broadcast time (BT) Broadcast time: the time at which the originating vehicle sent out the record An aggregated record contains more than one ID

8 Aggregated Records Having n records Calculate the aggregated record’s fields: POS and SPD are weighted averages.

9 Aggregation Algorithms Ratio-based Cost-based

10 Ratio-based Aggregation Current Vehicle Aggregation ratio: inverse of the number of records that would be aggregated in one record Portion value: amount of the remaining space in the broadcast message 3. In each region, each two consecutive records that are closer than the merge threshold, are merged 1. Calculate region boundaries 2. Calculate merge thresholds

11 Cost-based Algorithm The Ratio-based algorithm selects the records to be aggregated blindly! Assign a cost to merging two records, select records corresponding to lowest cost Cost function: –High cost to close vehicles –Minimize error due to merging records –Minimize number of cars in merged records

12 Information Aging Problem –Vehicles move and change speed –Records can be out-of-date –Received information might be invalid Solution –Delete record if no information about that vehicle is received in a while –Compute expected delay for each record received –Store record only if |actual delay – expected delay| < threshold

13 Evaluation Metrics Road Scenarios Simulation Results

14 Metrics Visibility –Average distance ahead about which a vehicle has information Accuracy –Average position error introduced due to aggregation Knowledge Percentage –Average percentage of vehicles in each region ahead about which a vehicle has information

15 Evenly distributed entries and exits Random constant speed during time intervals Changing lanes randomly Traffic Model

16 Simulations NS-2 simulations –802.11b with 11Mbps bandwidth –transmission range of 250m –MTU = 2312 bytes 15,000m road, 4 lanes 300s duration of simulation Algorithms: –Ratio-based, Cost-based, Non-aggregation, and Brute-force Cost- based Selected parameters using preliminary simulations

17 Scenarios Name# of nodesAvg. speed (m/s)Avg. gap (m) Rush-hour City High-density highway Low-density highway

18 Effect of Road Parameters on Visibility (1) Ratio-based Aggregation Cost-based Aggregation

19 Effect of Road Parameters on Visibility (2) Non-aggregation Brute-force Cost-based

20 Visibility (High-density Highway)

21 Accuracy (High-density Highway)

22 Knowledge Percentage (High-density Highway)

23 What We Learned Intuitively, cost-based algorithm appeared to be a better choice Cost-based algorithm is only marginally better for relatively closer distances Ratio-based algorithm is better for farther away distances and is more flexible

24 Conclusions TrafficeView provides drivers with real- time view of vehicles in front of their cars Designed and evaluated two aggregation algorithms using realistic road scenarios Ratio-based algorithm is a good algorithm –Good visibility and small position error

25 Future Work Working on prototype implementation Linear programming model to automatically calculate the aggregation parameters Privacy and Trust

26 Thank You!