KARI LAASONEN

BASIC RESEARCH UNIT, HELSINKI INSTITUTE FOR INFORMATION TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF HELSINKI

WORKSHOP ON CONTEXT-AWARENESS FOR PROACTIVE SYSTEMS 2005

Route Prediction form Cellular Data

1. Introduction2. Problem Description3. Prediction Algorithm

3.1 Route Composition 3.2 Route Similarity 3.3 Making Predictions

4 Evaluation4 EvaluationComments

Outline2

Location awareness plays a large role in ubiquitous computing.

Several applications relied on knowing or predicting the location of the user. Not merely to a known location, but to accurately

predict human movement Early-reminder system Traffic planning

We present an algorithm for predicting movement from cell-based location data. to learn places that are personally important to that

user, To predict the place the user is moving to.

1. Introduction-13

Existing approaches to learning important locations and predicting routes rely on GPS data such as [4,2] GPS can be problematic in urban areas Privacy.

The contribution of the present paper an enhanced algorithm for predicting routes. The algorithm analyzes whole paths using string

processing techniques, instead of relying on the short path fragments of the earlier paper.

1. Introduction-24

2. Harrington, A., Cahill, V.: Route Proling--Putting Context to Work. In: 2004 ACM Symposium on Applied Computing SAC'04, ACM Press(2004) 1567-15734. Marmasse, N., Schmandt, C.: A User-centered Location Model. Personal and Ubiquitous Computing 6 (2002) 318-321

Problem A GSM phone communicates with a base station.

over the air several base stations signal reaches the phone.

Select the station which has the strongest signal How about the signal strengths are equal?

A cell is the area covered by a single base station we say the phone is in cell

the phone is in the area of the corresponding base station.

overlapping each other A physical location does not one-to-one to cells

2 Problem Description2.1 Locations and Bases

5

6

We can visualize the data by making a graph, the vertices are the observed cells, edge (ci, cj ) is a transition from ci to cj .

This graph shows both daily commute from home (“Vuosaari”) to work from home to downtown Helsinki. does not include transitions in the opposite direction.

A location is either a cell cluster or a single cell. A location is promoted to a base the time spent there as a portion of the total time the

software run goes above a certain threshold.

Locations we can reliably detect the user entering and leaving

them. are important to the user are known as bases.

Fig. 17

the most important consequence of using cell-based location data is that lack the physical topology of the cell network.

includes the correspondence between cells and physical locations, and also all indications of direction.

cell sequence: ABA? the user visited B and came back. Or cell A was just briefly shadowed by B.

Looking at the immediate context is all but useless.

2.2 Route Prediction8

The first is to examine the local context of recent cells [3]. Suppose in cell c and the have been h1,

h2… prepare strings hkhk-1 …h1c, variable k. matched against a database of stored

fragments. Based on the matches found, and the

bases reached from c, we get probabilities for the next base.

Two basic approaches to the problem-19

3. Laasonen, K., Raento, M., Toivonen, H.: Adaptive On-device Location Recognition. In Pervasive Computing: Second International Conference, LNCS 3001, Springer Verlag (2004), 287{304

The second approach, in this paper entire routes between two bases. To learn all different physical routes as strings of cell

identifiers. Whenever the user completes a route r between

bases a and b if an existing route between a and b is similar to r.

the two routes are merged together. To make a prediction

the user has left base a, we have a set of possible routes and their destinations b.

We now use a recent history h of cells and find the route that exhibits the largest similarity to h.

Two basic approaches to the problem-210

11

route clustering to the data of Fig. 1. the two most frequently traveled are shown the routes actually traveled in the physical world.

There are three phases in the algorithm. Phase 1, the user leaves a base enters a cell

c, prepares for a new route prediction task.

Phase 2, at each cell transition, we make a prediction, which is a set of pairs (b, p), b is a possible future base p the probability of the user going

Phase 3, when the user arrives at a base, the entire route a, c1,…, cn, b* is used to make better subsequent predictions.

3. Prediction Algorithm12

For each pair of bases a and b we maintain a set of routes Rab.

When the user arrives at base b a new route t = ac1 … cnb is added to the database If the maximum similarity of t against all

occurs with some r = rmax and is greater than a threshold value. Then t is merged with route rmax.

falls below threshold value for all existing routes, add t to Rab, the set of (distinct) routes between a and b.

3.1. Route Composition13

the paths as strings of cell identifiers or “letters.” give each letter in both strings a position (value), [0; 1] the initial value assign to ith letter is v(xi) = (i -1)/(n -1).

For example,

The merged string is thus “tw(ir)les", i and r share the same position. some cells do not necessarily have fixed order to them.

the average value v(t) = 0; v(w) = 1/6 ; v(i) = v(r) = 1/3 ; v(l) = 2/3 ; v(e) =

5/6; v(s) = 1:

To handle cyclic paths14

The similarity function, sim(r, t)r is a composite route between two bases. t be a complete path.

Jaccard measure J = nrt/(nr + nt - nrt), nr and nt are the number of elements in r

and t, nrt is the number that is in both. symmetric, but ignores direction, so a

string is equivalent to its reverse.

3.2. Route Similarity15

Inclusion similarity, I is similar to J but asymmetric

let I(r, t) = T/|t|, T is the number of elements in t that are

found, in-order, in r. For example,

I(abcdef; acbdg) = 3/5; letters `a' and `c' are in order, but `b' and `c' have been exchanged.

Inclusion similarity16

Prediction By the most recent h = ck-m … ck , not all c1 … ck

detect faster and more efficient.

Route matching has produced a set S of possible reachable bases when starting from base a. Making a prediction entails computing for each candidate

base b S the similarity

the largest similarity of the cell history against all routes leading to b

equal similarities, choose by additional context variables. time of day, weekday and cell frequency

3.3. Making Predictions17

The data was collected for six months in 2003 With the Context Phone software on a Nokia 7650

phone. three volunteer users

both at work and at leisure.

The baseline algorithm is the fragment-based method [3], which was tested with several window sizes k .

The resulting prediction was then compared to the actual base.

4. Evaluation18

3. Laasonen, K., Raento, M., Toivonen, H.: Adaptive On-device Location Recognition. In Pervasive Computing: Second International Conference, LNCS 3001, Springer Verlag (2004), 287{304

A prediction is correct matches the actual next base and larger than the

threshold value = 0.3.

A low correct prediction is correct, but probability is less than the threshold, or the second-best prediction is correct with nearly

equal probability (e.g., p1 = 0:55 and p2 = 0:44), or the fork point was predicted correctly.

A low fail prediction was wrong, but the probability was also low.

A fail -type prediction wrong, or no prediction at all.

Fig. 319

20

The F2 and F4 are the fragment method with a window size of 2 and 4, respectively.

The C denotes the route prediction using the normal context database,

which maintains a time distribution for all intermediate route cells;

The reduce model C’ has a time distribution only for the starting times.

Fig. 421

Fig. 4 Comparison of the memory consumption of the algorithms.

Accuracy models C and C’ are

very similar the latter uses much

less memory

But even model C consumes less memory than any fragment-based method.

Route predictions are based on approximate string matching techniques.

在當時用GSM系統進行研究，就我們的研究可以用WiMAX進行研究

用 base station 來進行路徑的紀錄及預測，在準確度上是否會不足

這篇 paper的做法是需要有歷史紀錄的支持才能進行預測，對不曾前往的地點則無法預測。

Comments22

Algorithm23

3. Laasonen, K., Raento, M., Toivonen, H.: Adaptive On-device Location Recognition. In Pervasive Computing: Second International Conference, LNCS 3001, Springer Verlag (2004), 287{304