Agenda - pcb.its.dot.gov...Agenda • Activity-Based Travel Demand Modeling from Cellular Data –Introduction –Activity Pattern Recognition from Cellular Data –Construction of

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


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)


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


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


BEAM Extends MATSim

• BEAM re-envisions the MATSimMobility Simulation

• Makes use of concurrent programming paradigm (actor model of computation)


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


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


Behavioral Modeling in BEAM


Behavioral Modeling in BEAM


TNC Driver Behavior


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)


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)


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


SF Bay Daily Energy Consumption by Mode


Energy Consumption by Mode, County, Hour


Energy Consumption Per Passenger Mile


Modal Splits are Sensitive to Pricing


Service Availability Also Impacts Modal Splits


Energy Consumption can Be Analyzed in Detail Spatially/Temporally/by Mode/ etc.


Learn More at TRB

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Thank You!Mogeng Yin

[email protected]

Colin [email protected]

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