Technology for the Estimation ofPedestrians’ Trajectories⚫ Online Collection of WiFi/Bluetooth Packet Signal
Newly developed WiFi/Bluetooth packet signal sensor collects the randomized ID numbers ofmobile devices with timestamps and RSSIs (Receiving Signal Strength Index) and transmit
them via the internet. The operators who may interest in the people flow in a town, a park, anevent venue, etc. can easily set up the monitoring system with those sensors.

①~⑬ : WiFi/Bluetooth sensor location

WiFi/Bluetooth Packet Signal Sensor

WiFi/Bluetooth Sensor locations in Soleil Hills Park, Yokosuka-city⚫ Estimation of Pedestrian’s Trajectory
The RSSI of WiFi/Bluetooth Packet Signal is attenuated by the distance to a device. We havedeveloped the ID tracking methodology to estimate pedestrian’s trajectory in 10m orderresolution with a smaller number of sensors. In the estimation, the meth utilizes therelationship of distance-RSSI and the place transition probabilities embedded in a discretizedwalkable space. The estimation result is provided with the form of most likely time-space
trajectory. Those estimated trajectories will be used to the microscopic agent simulation.
Relationship between distance - RSSI 80%
観測端末1-1070%

観測端末2-20

観測端末1昼RSSI-3060%↓WiFi/BT sensor

観測端末1夜-40

観測端末2昼-50

観測端末2夜-6050%
40%

取得確率

030%

-70
20%
-80

↑Estimated trajectory
Large

10%

-90
-1000%

0

5

10

20

30

4050

距離[m]

↑
↑Likelihood↑Small
地図：地理院タイル https://maps.gsi.go.jp/development/ichiran.html

This research is supported by the Commissioned Research #JPJ012368C08201 of
the National Institute of Information and Communications Technology (NICT)i-Transport Lab. Co., Ltd 3-10-9F Kanda-ogawamachi Chiyoda-ku, Tokyo, 101-0052, Japan HP: https://www.i-transportlab.jp

Nowcast / Forecast Technologies for
Crowd Flow Prediction⚫ Crowd Flow Nowcast
The ‘Nowcast’ of crowd flow is so-called online simulation which continuously adjusts its inputs
and key parameters with real time observed data. The ‘Kashiwa-no-ha Crowd Flow Nowcast’ takes
in the real time pedestrian count data from the local management organization and reproduces‘today’s’ crowd flow in the area every minutes. The ‘Nowcast’ enables social feedback loop in
cyber-physical system and will contribute to maintain the quality of walkable space.
Time series of pedestrian counts (9th December 2023)

Park

Dots: measured volume
Lines: estimated volume

Chiba Uni.

Residentialtowers

La-La Port(Shopping mole)● AI video cam sensors

Pedestrian count sensors implemented by theUrban Development Centre of Kashiwa (UDCK)

Estimated pedestrian O-D flow with an online
particle filter being fed to the ‘Nowcast’ simulation

The ‘Kashiwa-no-ha Crowd Flow Nowcast’ agent simulation
reproducing, quantifying and visualizing ‘Today’s’ condition⚫ Crowd Flow Forecast
The framework of the ‘Nowcast’ can be applied to the ‘Forecast’ byreplacing the real-time input data with the predicted data. We arenow developing the ‘Enoshima Crowd Flow Forecast’ under thecollaboration with the Toyoda Lab (IIS, the University of Tokyo),which provides the hourly time series of 7-days-future population intarget area based on AI-learning model with SNS articles. The‘Forecast’ provides the future crowd conditions of next weekend tohelp the local authorities suffering with over-tourism to plan and toprepare the measures of congestion mitigation in advance.

ENOSHIMA CROWDFORECAST

Click here!

Predicted population
in Enoshima Islandon Monday 1st Jan. 2024

https://twitter.com/chome2xx/status/1654078271452229633

Extreme congestion can be seen inhigh season

Predicted future crowd condition at the main
corridor of Enoshima Island

Portal site of the ‘Enoshima Crowd Forecast’This research is supported by the Commissioned Research #JPJ012368C08201 ofthe National Institute of Information and Communications Technology (NICT)i-Transport Lab. Co., Ltd 3-10-9F Kanda-ogawamachi Chiyoda-ku, Tokyo, 101-0052, Japan HP: https://www.i-transportlab.jp