Transportation Decarbonization Pathways Assessment Through Metropolitan Area Simulation

Summary:

• Decarbonization of transportation

  • Mode choices

  • Simulation of Transport

  • System Level Energy Efficient Transport Research

    • Better vehicles

    • Smarter vehicles

    • Smarter roads

    • Smarter travelers

• End-to-end System Simulation: From individual vehicles to entire metro areas

  • Vehicle energy consumption, performance, cost

  • CAV vehicles control and energy use

  • Vehicle trip profile generation

  • Focus of the talk: 

    • POLARIS: https://vms.taps.anl.gov/tools/polaris/

    • Metro area transportation system

• Polaris: Agent-based transportation system

  • Everything is an agent: drivers, cars, streetlights, roads, buses

  • Initiated by FHWA (highway authority), further developed by DOE

  • Study topics:

    • Multi-modal travel

    • Vehicles and infrastructure interactions

    • Connectivity and automation

    • Urban science

  • Scales:

    • Metro-scale: Urbansim

    • Long-term choices: population, home/workplace, household vehicle choice, CAV tech choice

    • Mid-term choice: telecommute, activity generation and planning

    • Within-day: activities, routing, traffic, EV charging, transit use, freight/logistics

    • Can understand impacts of many policies

      • Smart mobility

      • Interactions between drivers and policies

      • Fine-grained impacts on individual household types and communities

  • Tools:

    • Network editor (roads, intersections, traffic control)

    • Simulator and interactive visualizer

    • Web-based analyzer/postprocessor

  • Inputs:

    • Traffic analysis zones

    • Location/parcel data

    • Household surveys

    • Census data

    • Vehicle distributions

    • Transportation behavior surveys

    • Transit network: connectivity, traffic control, agencies, traffic counts, taxi data, bike/car share, charging locations, parking inventory

  • Data is collected by collaborating with local partners: transportation agencies, universities, etc.

    • Data quality is limited

    • Small-scale errors are not critical

    • Large-scale model predictions (average flows/speeds on major roads) are validated against live data

  • Polaris is being applied to many major cities: Chicago, Seattle, Detroit, Washington, Los Angeles, Beijing, Sydney, Austin

• SmartMobility workflow

  • Understanding efficiency of different large-scale transportation policies

    • Transportation/logistics

    • Energy use

    • Traffic

  • Integrated network, demand and traffic assignment

  • Energy consumption is modeled per-vehicle, considering many different types of vehicles

  • Model of charging network, as well as queuing at chargers (key for transportation and impact on delivery schedules)

  • Impact of land use on transport and impact of travel time (bidirectional connection to UrbanSim)

• Performance: 

  • C++ implementation, integrated with efficient optimization algorithms (CPLEX, Gurobi, GLPK)

  • 4-6hr for million agents

  • Optimization:

    • Before run:

      • ·         Design of EV Charging network

      • ·         Design of transportation network (warehouse-factory-store links)

    • Mid-run: ecorouting algorithms (also developed ML-based energy consumption models to support this)

    • Optimization of transportation policies (many POLARIS runs of alternative policies)

• Use scenarios

  •  Transit optimization in Chicago

    • Bus Rapid Transit

    • Improved transit ridership by 11%

  • Transit optimization in Austin

    • Varied charges for transportation

    • Partial subsidy improved transit use from 4.5%-5%

    • Free transit would increase to 5.6%

  • Impact of connectivity on delays due to traffic accidents

    • Connected vehicles rerouted around accidents

    • Can mitigate 50% of delay

    • Best performance achieved at 50% penetration (don’t want everyone to reroute due to every accident)

    • 1500 detectors sufficient (Bloomington, IN)

  • Impact of Level 2 (driver assist)  vs Level 4 automated vehicles (fully automated) on travel time

    • People in partially automated act the same

    • People in fully automated vehicles drive more, which slows all vehicles down

  • Self-driving vs buses

    • Buses are very efficient but their average utilization is low

    • If self-driving vehicles are very cheap, then this produces many empty miles as persona vehicles are shuttled between household members

    • This uses up a lot of energy

    • Cars used as taxis are much more efficient, 

      • ·         e.g. municipal or private taxi fleet operators that are globally optimized

      • ·         Primary opportunity for self-driving cars to improve upon current system

  • CO2 Policies that mitigate land use impact of self-driving cars

  • Impact of increased travel costs on low income and exurban areas (e.g. road pricing)

  • Design of vehicle charging infrastructure (accounting for parking, depos, households, stores)