IBM Research – Tokyo

Large-scale agent-based socialsimulation for traffic and pedestrian
in a city
IBM Research AI
Research Staff Member
Hideyuki Mizuta© 2017 International Business Machines Corporation1

IBM Research – TokyoAgenda§ Introduction of agent-based social simulation§ Large-scale agent simulation framework§ IBM Mega Traffic Simulator§ Pedestrian Simulation for shopping mall and city

© 2017 IBM Corporation2

IBM Research – Tokyo

Introduction of Agent-based Simulation
New Paradigm for Social Science and Computing§ Background– Emerging of complex dynamic social behavior• Market crisis• Global environment• Social interaction– Beyond the limit of traditional social science with IT and Physics• Single rational agent
Heterogeneous agents with bounded rationality• Static equilibrium
Dynamic interaction among agents and environment§ Objectives– Understand and reproduce complex social behavior– Provide effective design and decision support tools for city planner§ Examples:– Artificial Market– Traffic Simulation– Evacuation Simulation© 2017 IBM Corporation

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Interacting Agents in Complex Adaptive System§To investigate the complex behavior of social system, we consider
heterogeneous agents that determine the action based on theinteractions with other agents and environment§Dynamic interaction and feedback among microscopic agents andmacroscopic environment generate the micro-macro loop

AgentsBehavior
Model© 2017 IBM CorporationDecision

HeterogeneouspreferenceEnvironment(Complex Adaptive System)

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X10-based Agent Simulation on Distributed
Infrastructure (XASDI)§ XASDI is large-scale agent-based social simulation framework with
§ Enormous number (billions) of agents to represent citizens in cities or countries§ Highly parallel and distributed simulation with X10 platform for post-Peta Scale machines§ Easy-to-use API with Java that is familiar to application programmer of social simulations§ XASDI is published as an open source software under the Eclipse Public License(EPL) https://github.com/x10-lang/xasdi
Social Simulation (Traffic, Evacuation, etc)

Application
Simulation Framework
X10-based Agent Simulation on Distributed Infrastructure(XASDI)

X10 / Java© 2017 IBM Corporation

CPUCPU

Large-scale agent environment
Programming with X10 and JavaCPU

Distributed Infrastructure (HPC, Cloud)5IBM Research – Tokyo

Traffic and Pedestrian in a city
§ It is important to estimate various human activities in a city for governmentand business to establish smarter cities with high value and low pain
– Increase citizen satisfaction (service level) and economic activities– Reduce congestion (traffic jam) and risk due to disaster§ Human activities with enormous elements and interactions are toocomplicated for simple analysis or equations§ We developed traffic and pedestrian simulator in a city to observe individual
behavior and support decision makers with what-if analysis© 2017 IBM Corporation6

IBM Research – Tokyo

IBM Mega Traffic Simulator (Megaffic)
Map Data

Analysis andDataPreparation•Link Cost Prediction

RoadTransition
cost

IBM Mega Traffic
Simulator

Visualization

DriverBehavior
Model•Driver Behavior Modeling

Probe Car
Data
Traffic
Demands
Data

GIS pointdata•Traffic Demands
Estimation•Geo coding

TripDefinition•OD Table generation

RoadNetworkDefinition

Simulation Base
X10-based Agent Simulation
on Distributed Infrastructure(XASDI)
Scalable simulation engine for supporting
parallel execution environment

This project is funded by the PREDICT project of Ministry of Internal Affairs and Communications and CREST project of Japan Science and Technology Agency© 2017 IBM Corporation7IBM Research – Tokyo

Modules of Traffic Simulator§ Megaffic is developed on the X10-based framework XASDI that can be
executed on distributed HPC environment§ Road network consists of cross points (CPs) and road links§ In Megaffic, CPs execute each time step (1 sec) of traffic simulation in parallel§ Vehicle on road is defined as an agent that has driver model including speedchanging module and lane-changing module
Mega Traffic Simulator (Java)
Driver Module

Speed/Lane Module

Network Module

CrossPoint Module

X10-based Agent Simulation on
Distributed Infrastructure (XASDI)

CP1CP3CP4CP2CP2CP5X10 on HPC, Cloud8© 2017 IBM Corporation

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IBM Research – TokyoDemonstration Experiments of Traffic Flow atHiroshima City• We performed demonstration experiments withHiroshima city
• Simulation area is whole Hiroshima city
– 40,619 cross points and 121,543 roads• Simulation Screenshot© OpenStreetMap contributors

The map images and colored streets are based upon OpenStreetMap
data (©OpenStreetMap contributors) and licensed under CC-BY-SA 2.0(http://creativecommons.org/licenses/by-sa/2.0/) or ODbL
(http://opendatacommons.org/licenses/odbl/)© 2017 IBM Corporation

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What-If Simulations at Hiroshima City
Traffic changes caused by traffic restriction with two scenarios
Gion Shin-Do has both 3 lanes for north and south. 3 lanes fornorth are closed.
x2

1. Use remaining 3 lanes only for south

x1

2. Divide remaining lanes into 1 lane for north and 1 lane for
south

1/2

x4
x2

© OpenStreetMap contributors, CC BY-SA

Traffic changes caused by theopening of new highway(Minami Road)x1

The map images and colored streets are based
upon OpenStreetMap data (©OpenStreetMap
contributors) and licensed under CC-BY-SA 2.0or ODbL
© 2017 IBM Corporation© OpenStreetMap contributors, CC BY-SA10

IBM Research – Tokyo

Validation with Tokyo metropolitan traffic flow
Road network extracted
from Open Street Map
891,335 Cross points
2,465,767 Roads
Comparison of logarithmic
traffic volume between
simulation results andobserved data
Correlation Coefficient: 0.7028

log© OpenStreetMap contributors, CC BY-SA© 2017 IBM Corporationlog

The map image is based upon OpenStreetMap
data (©OpenStreetMap contributors) andlicensed under CC-BY-SA 2.0 or ODbL

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IBM Research – Tokyo

Pedestrian Simulator in Shopping MallMarketing
Purchasing
Shop Location
Product distribution
Economic information
Zone
Network

Spatiotemporal
Walking© 2017 IBM Corporation

Position
Speed
MovementEnvironment

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Consumer’s decision and action§ At each timestep, consumer agents update attraction value for
products and zones, and perform following decision making and action

Reach destination

P(Dest)Selectdestination

Leave

Move/Walk

Shop
decisionP(Shop)Enter© 2017 IBM Corporation

Exit

P(Leave)WalkaroundPurchase
P(Buy|Shop)

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What-if simulation experiments in simple map
What-if simulation to exchange thelocation of two shops (small and large)in simple map with 5 shops
one shop has 20 products and others
have 10 products that make difference
in zone attraction
3600 consumer agents enter into theshopping mall during the 3600 secondsof simulation time
© 2017 IBM Corporation14

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Case 1: many products in the small shop
§ Total purchase is restricted: 58,960§ The small shop attracts many consumer and heavy congestion occurs© 2017 IBM Corporation

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Case 2: many products in the large shop
§ Total purchase increases up to 68,303§ The large shop attracts consumers and reduced congestion improve
the total revenue© 2017 IBM Corporation16

IBM Research – TokyoPedestrian Simulation in a city area
§ By extending the shopping mall simulation,
we generate general polygon map withroads and buildings extracted from OpenStreet Map§ We can utilize this extended pedestrian
simulator to consider the congestion
mitigation or evacuation planning in timesof major disaster© 2017 IBM Corporation

The map images and colored streets are based
upon OpenStreetMap data (©OpenStreetMap
contributors) and licensed under CC-BY-SA 2.0
or ODbL© OpenStreetMap contributors, CC BY-SA17

IBM Research – Tokyo

Pedestrian Flow at Sendai city

© 2017 IBM Corporation18

IBM Research – TokyoSummary§ We develop the agent-based simulation platform XASDI for largescale distributed social simulation§ XASDI is available at GitHub with EPL• https://github.com/x10-lang/xasdi§ We introduced two social simulations developed with XASDI– Mega Traffic Simulation– Pedestrian Simulation§ By utilizing these simulator, we can support complex decision makingfor evacuation planning in times of major disaster© 2017 IBM Corporation19