DOI: 10.6086/d1nh4n
title: BEAM input generation and low exposure routing model

description: The Low Exposure Routing (LER) modeling methodology has already been
 evaluated at the vehicle level in our previous work. In this study, we
 propose a novel framework to quantify the impact of LER at the
 transportation system level applying different technology penetration
 rates. Under the framework, we employ the truck origin-destination from
 regional transportation demand model, to generate truck trips in the City
 of Riverside. Then, the calibrated BEAM model, an agent-based simulation,
 simulates trips through reinforcement learning and dynamic daily planning
 technique to reach maximum utility at the transportation system level.
 Finally, it shows that with a 100% penetration rate of LER and a strict
 10% time increase threshold, the air pollutant exposure reduced up to 16%
 at city level with a slight trade-off of travel time.
description: In this project, we aim to tackle this challenge by developing an
 activity-based traffic simulation model for the City of Riverside, CA and
 evaluate the transportation system-level impacts of the truck low-exposure
 routing (LER) technology for mitigating the impacts of truck emissions on
 communities. Based on the existing passenger transportation model
 developed, truck demand and trips are generated using Southern California
 Association of Governments (SCAG) model. Based on the long-range regional
 transportation plans in SCAG, we extract zone-based truck trips
 origin-destination table; thus, we got daily passenger car and truck trips
 for Riverside City. Since zone-based origin-destination (OD) table offers
 no exact coordinates and time of a day, this study utilizes employment
 data provided by the Employment Development Department (EDD) to give
 deterministic truck trips coordinates and utilizes PeMs data to calibrate
 the exact time of travel. Those trips are used as input for BEAM, a
 framework for a series of research studies in sustainable transportation.
 We then apply activity-based traffic simulation and Low Exposure Routing
 to Heavy Duty-Diesel Truck (HDDT) trips in the city, showing that the
 communities will benefit from reduced exposure to air pollutants by
 adjusting HDDT routes. Finally, we evaluate LER at the transportation
 system level under different technology penetration rates. 
 (hhdt_od_10.csv) Zone-based Origin-Destination table
 (OD TABLE) from SCAG is necessary for generating three different truck
 trips: Heavy-Duty Diesel Truck Trips, Medium-Duty Diesel Truck Trips, and
 Light-Duty Diesel Truck Trips.
 (pems_output_i10e.xlsx) PeMs collects real-time traffic data
 from over 39,000 individual detectors along with the freeway system
 crossing major cities, monitoring traffic volume by categories, such as
 number of trucks on the road. Thus, we can estimate deterministic time of
 travel by reading PeMs data. Since PeMs data is one-hour interval format,
 we randomly distribute these trips into corresponding hour.  
 (truck_related_employment.csv) The Employment Development
 Department (EDP) record business companies in Riverside City, which we can
 use them for potential truck trip position generation. Thus, for the truck
 trips inside Riverside City, we assign coordinates of companies in the
 corresponding zone to the truck trips as their origin and destination
 positions. For those truck trips just crossing city, we assign the closest
 boundaries points coordinates to them.
 (riverside_new.xml) The network files for Riverside City is
 derived from OpenStreetMap.com, which is osm (OpenStreetMap) format file.
 The raw network file is opened in Java OpenStreetMap Editor and
 transformed into pbf file. After we place this pbf file in the R5 folder
 under BEAM repository, the initial simulation automatically generates
 network.dat and physsim.xml where all map-related data is
 stored. (households.csv, person.csv, plans.csv) The
 minimum requirements needed for conducting a BEAM run are households,
 persons, vehicle fleet, vehicle types, map data, and configuration. We
 assign virtual household and virtual driver for these truck
 trips. (linkAttributes_RIV.csv) To determine vehicle
 emission factors (in the unit of <i>gram/mile</i>), link-based
 traffic activities (e.g., average traffic speed) is needed  as an input.
 We get the linkspeed from BEAM Model Results and use them here.
 (facilityAttributes.csv, blockAttributes.csv) facility and
 residential block are prepared as the sensitive receptors.
description: LowExpRoutingMannual_v2.docx is the user manual for the Low
 exposure routing. The minimum requirements are the
 fields in the uploaded files.