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TRANSCRIPT
Agenda
• Activity-Based Travel Demand Modeling from Cellular Data–Introduction–Activity Pattern Recognition from Cellular Data–Construction of Ground Truth Activities–Experimental Results–Conclusion and Future Works
• Agent-Based Modeling of Traveler Behavior and System Operations with BEAM–Goals–Approach–Preliminary Results
1Presenter: Colin Sheppard
Agent-Based Modeling of Traveler Behavior and System Operations with BEAM
2Presenter: Colin Sheppard
BEAM: The Framework for Behavior, Energy, Autonomy, and Mobility
Research Goals
• Holistically understand and analyze transportation mega-trends:–Mobility Services–Autonomy–Electrification
• Answer a variety of research questions centered around emerging mobility through an energy and services lens:–What will be the energy and mobility impacts of X?
• Create a simulation engine capable of:–Easy to use–Capturing all modes of travel–Modular and open for linkage with other modes (e.g. vehicle energy /
controls)
3Presenter: Colin Sheppard
Approach: Why Agent-Based Modeling?
• Travel behavior occurs at the scale of individuals• Travelers need a complete set of alternatives to choose
from with accurate estimates of cost and travel time• Choices impact the whole system through externalities• Interactive effects of choices are complex• Resource competition is important, supplies are limited
4Presenter: Colin Sheppard
BEAM Key Features
• Demand (governed by behaviors):–Mode Choice–Route Choice–Rerouting–Park Choice–Refuel Choice
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• Resource Markets:–Road Capacity–Vehicle Capacity–TNCs–Parking–Refueling Access
• Supply:–Driving–Transit (any GTFS)–Walk–TNC (automated, humans, optimized)–Bike–Parking–Refueling Infrastructure
Cost
& T
ime
Presenter: Colin Sheppard
BEAM Extends MATSim
• Agent-based meso-scale simulation
• Highly extensible including:–Multimodal, –Alt. Fuels, –TNCs, –Dynamic Pricing, –Etc.
• Utility maximization through scoring and replanning
6Presenter: Colin Sheppard
BEAM Extends MATSim
• BEAM re-envisions the MATSimMobility Simulation
• Makes use of concurrent programming paradigm (actor model of computation)
7Presenter: Colin Sheppard
BEAM Architecture
–Core components decoupled: AgentSim, PhysSim, Router–Each component designed for flexibility & distribution–AgentSim written in Scala leverages advanced programming patterns
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AgentSim: Actor System
• Adopted the actor model of computation: message-passing, asynchronous, approach to concurrent programming
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• BEAM Scheduler relaxes strict chronology in model execution, enabling massively distributed agent computations
• Akka actor system manages multi-plexing, threading, and cluster deployment
Master Plan
10Presenter: Colin Sheppard
Day in the life of an traveler in BEAM
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• Trip planner enumerates and quantifies alternative attributes• Choice model evaluates alternatives and samples from resulting distribution
Mode Choice Process
R5 by Conveyal
Presenter: Colin Sheppard
Behavioral Modeling in BEAM
12Presenter: Colin Sheppard
Behavioral Modeling in BEAM
13Presenter: Colin Sheppard
TNC Driver Behavior
14Presenter: Colin Sheppard
Latent Class Mode Choice Model
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• Two-stage model (both multinomial logit):–Class Membership–Mode Choice
• Modality style a function of consumer surplus, which summarizes system level of service–E.g. highway congestion
influences both modality style and probability of choosing “drive alone” as mode
• Distinct models for mandatory (work, school, etc.) and non-mandatory tours
Adapted from Vij et al. (2017)
Presenter: Colin Sheppard
Latent Class Mode Choice Model
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• Example modality styles:–Complete Car Dependents–Partial Car Dependents–Car Preferring Multimodals–Car Resisting Multimodals–Car Independents
• Distribution of modality styles an emergent modeling outcome which facilitates insights and analysis
Source: Vij et al. (2017)
Presenter: Colin Sheppard
Preliminary Results
• Bay Area Scenario– 5% Sample (~400k persons, 340k
cars)– Full Transit (27 agencies, 828 routes)– TNC Fleet (20,000 - also referred to
as Ride Haling) • Sensitivities Explored:
– Transit Price– Transit Capacity– TNC Price– TNC Number– Bridge Toll Price– Value of Time
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• Caveats / Disclaimers– Work in progress– Choice model not fully calibrated,
therefore modal splits not yet realistic– TNC operations are still simplistic– Congestion feedback effects still not
captured– Transit is underutilized
Presenter: Colin Sheppard
SF Bay Daily Energy Consumption by Mode
18Presenter: Colin Sheppard
Energy Consumption by Mode, County, Hour
19Presenter: Colin Sheppard
Energy Consumption Per Passenger Mile
20Presenter: Colin Sheppard
Modal Splits are Sensitive to Pricing
21Presenter: Colin Sheppard
Service Availability Also Impacts Modal Splits
22Presenter: Colin Sheppard
Energy Consumption can Be Analyzed in Detail Spatially/Temporally/by Mode/ etc.
23Presenter: Colin Sheppard
Learn More at TRB
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