Transportation system monitoring methodby usingprobe vehicles that observe other vehicles

TSUTokyoTech

Transport Studies Unit

Toru Seo
Doctoral Course Student,
Asakura Lab., Transport Studies Unit,
Tokyo Institute of Technology

The 2nd International BinN Research Seminar
＠The University of Tokyo
20140928

Introduction

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Transportation system monitoring
Transportation system

A system where travelers are traveling– Automotive road network– Pedestrian space– CityMonitoring

Acquiring information on a transportation system’s dynamics
 State– Flow– Speed

 Behavior– Macroscopic behavior of travelers• System model– Microscopic behavior of a traveler• Destination/route choice (strategic)• Interaction between travelers (tactical, operational)

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Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Methods for monitoring
Eulerian observation
Observe a system’s dynamics from fixed points inthe system
 Traffic detectors
 Cameras
 Ticket transaction data
Lagrangian observation (probe)
Observe a system’s dynamics from floating points
that move along with travelers
 GPS4

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

5Eulerian observation
 Rich information at thesensor’s installed points can beacquiredTime Wide-ranging observation isdifficult due to the cost of theobservation

SpaceObservation
PointObservation
pointSpace
traffic detector

fixed camera

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Lagrangian observation (probe)6 Information over wide-ranging
space can be acquiredTime Passive observation can acquire
information cost-efficientlySpace Volume-related information
can not be estimated from thesesampled trajectories

SpaceGPS-equippedprobe vehicle

BCALs(Hato 2010)

smartphoneProbe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Summary of current monitoring methods
Eulerian observation
 Can acquire– Volume-related info. (flow, density)– Quality-related info. (speed, reliability)

 Can not acquire– Wide-ranging info. (for time and space)

Lagrangian observation (probe) Can acquire– Quality-related info.
– Wide-ranging info.

 Can not acquire– Volume-related info.

Problem: How we can acquire volume-related
info over a wide range?

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Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

A solution

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New Lagrangian observation:
Probe travelers that observe othertravelersTime Volume-related information
can be estimated, since local
density is available

Space Information over wide-ranging
space can be acquired
Space

Can be efficient in traffic flow
monitoring in the near futureProbe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)Spacing measurement technologies
 Technologies of recognizing surrounding environment of avehicle from an on-vehicle equipment were developed– Radar, Laser scanner, Monoeye/stereo camera– Other vehicles, road alignment

Movie source: Stein et al. (2005)

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Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)Spacing measurement technologies

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Advanced driver assistance systems (ADAS)– record driving– warn the driver– semi-automation (ACC)– full-automation (autonomous car)

ADAS Vehicle-to-vehicle distance (≃spacing) must be measured in order toachieve traffic safety
 ADAS-equipped probe vehicle data can be utilized for estimation of
the volume-related variables, since spacing is inverse of local density

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Supposed future traffic systemtraffic control center (public/private organizations)

utilizebenefitequip

Traffic state(Flow, Density, Speed)

Formulation

Validate with aproof-of-concept
field experiment

traffic system 道路

utilize

ADAS

Spacingmeasurement
technologies

estimate

Data basePublic policies(traffic control, transp. planning)

Commercial services(info. provision)

benefitentire social

 Some drivers will gain benefit from ADAS by equipping the SMT
 Entire social will gain benefit from policies and services based on
the collected probe vehicle data11

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Motivations and Objective

12Motivations
 Transportation managements will be significantly improved if
it is monitored by using Lagrangian observation only
– arterial roads, developing courtiers Current probe vehicles can not acquire volume-related
information
 Spacing measurement technologies were practicallyimplemented; and have potential to spread to the world inorder to enable ADAS
Objective To develop and validate a methodology of estimating traffic
state using probe vehicles with spacing measurement
equipmentTraffic State
Estimation method

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

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Supposed situations
Target road
 One-wayIn The schematics are known

Traffic state in this area is estimatedout

Probe vehicles
 randomly distributed in the traffic at a certain penetration
rate
Spacing measure its position and spacing
 no measurement errors their characteristics and driving behavior are the same as
the rest of traffic
Target of estimation
 Traffic state (flow, density, speed) with a certain time spaceresolution

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Estimation method15

 Traffic flow represented as a time-space
diagramspace xProbe
time-space
region AProbe– vertical axis: space– horizontal axis: time
– curves: vehicle trajectories

 The probe vehicle acquire its own
trajectory and its leading vehicle’s one
 Traffic state in any closed region A canbe estimated from the probe vehicles’

time t– distance traveled– time traveled– area of region between the probe and its
leading vehicle
based on Edie’s generalized definition(1963)

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)Characteristics of the method

16 The method can estimate traffic state including thevolume-related variables from Lagrangian observation
data only The method can estimate traffic state with an arbitrary
time space resolution– 1 min-100 m traffic state– hourly traffic volume of a link
– macroscopic fundamental diagram The method relies on few exogenous assumptions: Dataoriented approach– It can be utilized for estimating behaviors in system (BinN?)

Validation witha Field Experiment

Probe vehicles that observe other vehicles

Field experiment at Tokyo

Toru Seo (Tokyo Tech)18

 Date/time: Sep. 24, 2013 (Fri.), 15:00 – 16:00
 Location: Cruising lane, Inner Circular Route (counterclockwise),Tokyo, Japan
 Number of probe vehicles: 20 (=3.5% penetration rate) Measurement devices: GPS logger and Mono-eye camera

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Inner Circular Route19

 Total section length: 14.2km– The survey area is cruising lane of 11km
length section excluding tunnels

 Most of the section has two lanes and50km/h speed limit
 It has complex traffic flow characteristic– curves, elevations, merging/diverging
sections A lot of detectors are installed. Reliableground truth data is available– time reso.: 1 min– space reso.: roughly 250m and per laneProbe vehicles that observe other vehicles

Actual traffic state
Density as a time-space diagram
 plot color: density
 vertical axis: space horizontal axis: time
Takebashi JCTEdobashi JCT

Hamazakibashi JCT

Toru Seo (Tokyo Tech)

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Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Probe vehicles

 20 standard sized passenger vehicles driven by non-professional drivers
were employed as probe vehicles– 44 laps were performed during 1 hour
– It corresponds to 3.5% probe vehicle penetration rate They measured their position and spacing with 15 s interval The position was measured by the GPS logger
 The spacing was measured by analyzing images taken by the camera– width of the leading vehicle in the images– actual width of the leading vehicle– field of view of the camera

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Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

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Estimation results

Detector data

Estimation result fromthe probe vehicle data

 Density as time-space diagrams
 penetration rate 3.5%, time resolution 5min, spaceresolution with 500m
 Dynamical features of the traffic flow were reproduced– free, congestion, queue extension

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

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Estimation results
 Error indices of various estimation scenarios– root mean square percentage error (RMSPE)
penetration rate
(probe per hour)

estimation
targeterror（RMSPE）

3.5% (42veh)

5min flow

14%0.2% ( 2veh)

1hour flow

16%

 High resolution information can be acquired where enoughnumber of probe vehicles exist– highway traffic managements

 Lower resolution information can be precisely acquired even
if the penetration rate is low– transportation planning

Conclusion

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

Achievements

25 We developed a traffic state estimation
method that utilize using probe vehicles withspacing measurement equipment
 We validated the method under an actual
traffic condition As result, the characteristics and performanceof the method were clarified

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

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Future plan
Transportation system

A system where travelers are traveling– Automotive road network– Pedestrian space– CityCurrent topics

Monitoring

Next next topics?

Next topics

Acquiring information on a transportation system’s dynamics
 State– Flow– Speed

 Behavior– Macroscopic behavior of travelers• System model– Microscopic behavior of a traveler• Destination/route choice (strategic)• Interaction between travelers (tactical, operational)

Probe vehicles that observe other vehicles

Toru Seo (Tokyo Tech)

References

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Some images were taken form external sources