1. A Method for Modeling of Pedestrian Flow in the Obstacle Space using Laser Range Scanners Yoshitaka NAKAMURA †, Yusuke WADA ‡, Teruo HIGASHINO ‡ & Osamu TAKAHASHI † † Future University Hakodate, JAPAN ‡ Osaka University, JAPAN

2. Background Development of sensing technologies – Various phenomena become able to be measured as digital data – Some services based on this measuring data can be provided Pedestrian flow attracts attention – Movement of the pedestrians For orientation of the services based on mobility pattern of pedestrians For urban planning For control of pedestrian for refuge instructions 2011/9/192IWIN2011

3. Purpose of Research To generate pedestrian flow model with high accuracy and law cost – Cost of measuring sensor Price of sensor is as low as possible Number of sensor is as little as possible – Cost of data processing Information to use is as little as possible 2011/9/193IWIN2011

4. Related Work Pedestrian flow detection – Cameras By recognizing the images of pedestrians Disadvantage: Privacy, Setting cost, Angle of view – RFID tag By tracking with RFID of each pedestrian Disadvantage: Cost of RFIDs – Counting number of pedestrians By counting the passage number of pedestrians in each gateway Disadvantage: Affected by occlusions 2011/9/194IWIN2011

5. Laser Range Scanners(LRS) Measures the distance to an object from LRS Advantage – Fast scanning of wide area – Little probability to infringe pedestrian’s privacy – Small cost of calculation – (Simple tracking by the difference of data is possible) Disadvantage – Easy to lose target objects by obstacles – Difficult to measure all pedestrians completely 2011/9/19IWIN20115

6. Measuring Data of LRS Measurement time Position coordinate of pedestrian ID of pedestrian assigned by simple tracking of UTM-30LX Simple tracking of UTM-30LX Judge the same pedstrian from the difference of measured data and assign ID to the pedestrian If tracking is succeed, pedestrian’s movement history can be found by ID ID becomes extinct in the place where tracking of the pedestrian failed Even if the same pedestrian is found again, the other ID is assigned 2011/9/19IWIN20116

7. Precedent Experiment(1/2) Conducted in “Whity Umeda” – By synchronizing 4 LRSs(HOKUYO UTM-30LX) – Measure the height of pedestrians’ waist[17] 2011/9/19IWIN20117 [17] Kawata, Ohya, Yuta, Santosh and Mori, “Development of ultra small lightweight optical range sensor system,” in Proc. of IROS2005

8. Precedent Experiment(2/2) Beige area is the movable area of pedestrians Orange columns are LRSs Pink lines are the measuring laser of LRSs 2011/9/19IWIN20118 26m 33m

9. Performance of Tracking Simple tracking of UTM-30LX could continue only for a short time – 30%~40% of IDs’ life times are only 1 second Pedestrians are hidden behind obstacles such as pillars Pedestrians are also hidden behind other pedestrians Some pedestrians are staying near LRS and become large obstacles 2011/9/19IWIN20119 =>The other approaches are needed for pedestrian flow generation

10. Approach Pedestrian flow is often used in – Trajectory analysis of customers in commercial facilities – Pedestrian flow analysis for refuge instruction, etc. In such case, tendencies of the pedestrians’ movement are more important than actual behaviors of pedestrians – Accurate tracking is impossible 2011/9/19IWIN201110 Pay attention to the change of population density in the partial domain of the measurement area

11. Proposed Method Generate the pedestrian flow model from the population density – Divide measurement area into some square domains(cells) – Calculate population density of each domain – Generate flow model based on the change and distribution of population density 2011/9/19IWIN201111

12. Assumptions 1.Pedestrians move to the only adjacent cell (in all directions) from the cell where himself/herself is now 2.Pedestrians move from the entrance to the exit without making a detour under Assumption 1 3.Pedestrian is measured only once in each cell where he/she passes 2011/9/19IWIN201112

13. Overview of Method 1.Dived the measurement area into some unit cells 2.Select the gateway cells 3.Suppose the route candidate between each 2 gateways 4.Calculate the population density of each cell 5.Calculate the number of sojourners 6.Estimate the route where the pedestrian passed and its traffic 7.Determinate the direction of the flow 2011/9/19IWIN201113

14. Supposition of the Route Candidate Suppose the route candidate according to Assumptions 1 & 2 between each two different gateway cells. 2011/9/19IWIN201114

15. Calculation of the Population Density Count it up how many pedestrians existed in each cell for constant period of time => Population Density Exclude pedestrians moving at a speed less than minimum speed as sojourners – Minimum speed = 20cm/s 2011/9/19IWIN201115 Sojourners 3

16. Estimation of the Route One route corresponds to one pedestrian Decide a route candidate passing a cell with high density as a route Update the density data to the data removed a decided route Repeat the process until the population of all cells become almost 0 2011/9/19IWIN201116

17. Determination of Direction Detect the speed and direction of pedestrians’ movement from tracking data of UTM-30LXs Count the direction ratio of pedestrians’ movement in each cell on route candidates Determine the direction ratio of route based on direction ratio of pedestrian’s movement 2011/9/19IWIN201117

18. Example of Generated Flow(1/2) Direction of arrow: Direction of the pedestrian flow Thickness of arrow: Quantity of the pedestrian flow Numbers in Circles: Average number of sojourners of same cell for each 1 minute 2011/9/19IWIN201118 4 1 3 sojourners sojourner sojourners Moring

19. Example of Generated Flow(2/2) Direction of arrow: Direction of the pedestrian flow Thickness of arrow: Quantity of the pedestrian flow Numbers in Circles: Average number of sojourners of same cell for each 1 minute 2011/9/19IWIN201119 Evening 2 sojourners 2 3 6

20. Performance Evaluation Evaluate the generated model using scenario data formed by MobiREAL based on the number of pedestrians in the gateway cells – Compare the pedestrian flow model generated by proposed method with the pedestrian’s movement of scenario data 2011/9/19IWIN201120

21. Scenario Data Set the origin and destination point of each node Generate a realistic movement model using MobiREAL 2011/9/19IWIN201121

22. Agreement Rate Compare the flow model generated from the scenario data by proposed method with the scenario data 2011/9/19IWIN201122 Agreement rate: Ratio of the cell which the route of generation model passed is the same as scenario data Average agreement rate of route in the whole scenario is about 80.79% Agreement rate: 67% Scenario dataFlow model

23. Discussion Accuracy of the measuring population density – This method takes the average value of several minutes the tendency of the pedestrian flow does not change in a short time the disappearance of the pedestrian by the obstacle is keeping for an instant – Actually, the disappearing pedestrians exist for a long time depending on the placement of the obstacles(pedestrians) It is necessary to consider about method to estimate density by disappearance probability or different method to calculate the density by flow quantity 2011/9/19IWIN201123

24. Conclusion Proposed a method for modeling pedestrian flow in the space such as underground shopping center – Using Laser Range Scanners – Using the change of population density Future work – Different calculation method of density corresponding to the real environment – Comparison with the data which completely measured the movement trace of each pedestrian 2011/9/1924IWIN2011