# US 2023/0054616 A1 · City Scale Modeling and Evaluation of Dedicated Lanes for Connected and Autonomous Vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0054616 A1 - Document ID: US20230054616A1 - Status: Published application - Kind: A1 - Application number: 17/405,623 - Filed: 2021-08-18 - Published: 2023-02-23 - Earliest claimed: 2021-08-18 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Richard Twumasi-Boakye; Shadi Djavadian; James Fishelson; Andrea Broaddus; Yifan Chen; Melissa Ruhl; Archak Mittal; Xiaolin Cai - Web page: https://patentgrep.com/patent/US20230054616A1 ## CPC - G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083) - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract An agent-based model (ABM) is generated—the ABM comprises agents. The ABM includes a road network corresponding to a metropolitan geographical area of interest. The ABM can be executed to simulate behavior of the agents. The ABM includes a road network having designations of dedicated connected autonomous vehicle (CAV) lanes. The ABM further includes a CAV lane-choice behavior model that models CAV lane-choice behavior of drivers. The CAV lane-choice behavior model has parameters that can be varied to vary lane choice behavior modeled by the lane-choice behavior model. The lane-choice behavior model is applied to the ABM so that when the ABM is executed the agents behave at least in part according to the CAV lane-choice behavior model. ## Claims ### Claim 1 A method performed by a computing device, the method comprising: constructing an agent-based model (ABM) that has mesoscopic granularity, the ABM comprising agents, the constructing including incorporating a road network with granularity corresponding to a metropolitan geographical area of interest, wherein the ABM is executed to simulate behavior of the agents, wherein an execution of the ABM produces emergent outputs that correspond to collective effects of simulated individual behaviors of the agents; the constructing the ABM further comprising implementing a connected autonomous vehicle (CAV) lane-choice behavior model that models CAV lane choice behavior of drivers, the CAV lane-choice behavior model comprising parameters that can be varied to vary lane choice behavior modeled by the lane-choice behavior model; applying the lane-choice behavior model to the ABM so that when the ABM is executed the simulated agents behave at least in part according to the lane-choice behavior model; varying the parameters of the lane-choice behavior model, and, for each variation of the parameters, executing the ABM, wherein each execution of the ABM provides a corresponding set of emergent outputs that correspond to a collective behavior of the agents that each behave according to the lane-choice behavior model with a current variation of the parameters; and storing indicia of the sets of outputs from the ABM. ### Claim 2 (depends on 1) The method according to claim 1, further comprising: obtaining demand data for the metropolitan geographic area of interest, the demand data comprising daily activity patterns; and selecting a portion of the demand data corresponding to the metropolitan geographic area of interest. ### Claim 3 (depends on 2) The method according to claim 2, further comprising converting the selected portion of the demand data into a format compatible with the ABM. ### Claim 4 (depends on 3) The method according to claim 3, wherein the converting comprises translating demand statistics into discrete travel instances. ### Claim 5 (depends on 1) The method according to claim 1, wherein the sets of emergent outputs comprise one or more of: expected demand on dedicated CAV lane average consumer wait time, average trip time, average vehicle occupancy, optimal fleet requirement, or road network performance. ### Claim 6 (depends on 1) The method according to claim 1, wherein an execution of the ABM outputs indicia of probabilities that respective users will adopt dedicated CAV lanes modeled in the ABM. ### Claim 7 (depends on 1) The method according to claim 1, wherein the ABM models a CAV service comprising a CAV demand rapid transit (DRT) service or a CAV bus rapid transit service (BRT), and wherein an execution of the ABM outputs an indication of predicted usage of the CAV service. ### Claim 8 Computer-readable storage hardware storing instructions that, when executed by a computing device, cause the computing device to perform a process, the process comprising: generating a mesoscopic city-scale ABM comprising agents, the ABM corresponding to a city, the generating comprising: accessing a road network corresponding to the city and at least an area around the city; thinning the road network in correspondence to the city such that there a portion of the road network corresponding to the city has higher fidelity than a portion of the road network corresponding to the area around the city; designating one or more roads in the road network as having dedicated CAV lanes; incorporating the thinned road network into the ABM; generating a CAV lane-choice behavior model that models CAV lane-choice behavior of the agents of the ABM; and incorporating the CAV lane-choice behavior model into the ABM, wherein the CAV lane-choice behavior informs decisions of the agents on whether to use the dedicated CAV lanes. ### Claim 9 (depends on 8) The computer-readable storage hardware according to claim 8, the process further comprising: obtaining demand data corresponding to the city; and converting the demand data into discrete travel plans for the agents of the ABM. ### Claim 10 (depends on 9) The computer-readable storage hardware according to claim 9, wherein the demand data comprises summary statistics of travel within, into, and out of the city. ### Claim 11 (depends on 10) The computer-readable storage hardware according to claim 10, wherein the converting the demand data into discrete travel plans comprises converting production-attraction matrices to corresponding origin-destination (OD) trips. ### Claim 12 (depends on 8) The computer-readable storage hardware according to claim 8, wherein the ABM comprises a traffic model that represents CAV impacts on agent decisions. ### Claim 13 (depends on 8) The computer-readable storage hardware according to claim 8, wherein the accessing and thinning of the road network is performed by a network editing tool configured to read road network files of varying formats from varying respective network resources. ### Claim 14 (depends on 13) The computer-readable storage hardware according to claim 13, wherein the network editing tool is further configured to validate and match roadway type, speed, number of lanes on key roadways, or roadway changes due to construction. ### Claim 15 (depends on 16) The computer-readable storage hardware according to claim 16, wherein the network editing tool is further configured to convert the road network into a format compatible with the ABM. ### Claim 16 A method performed by a computing device, the method comprising: generating an agent-based model (ABM), the ABM comprising agents, the generating including incorporating a road network corresponding to a metropolitan geographical area of interest, wherein the ABM can be executed to simulate behavior of the agents; the generating the ABM further comprising a road network to the ABM, the road network comprising designations of CAV-dedicated lanes. the generating the ABM further comprising implementing a connected autonomous vehicle (CAV) lane-choice behavior model that models CAV lane choice behavior of drivers, the CAV lane-choice behavior model comprising parameters that can be varied to vary lane choice behavior modeled by the lane-choice behavior model; applying the lane-choice behavior model to the ABM so that when the ABM is executed the simulated agents behave at least in part according to the lane-choice behavior model, and wherein an execution of the ABM outputs probabilities that users modeled by the agents will use the CAV-dedicated lanes. ### Claim 17 (depends on 16) The method according to claim 16, wherein the lane-choice behavior model comprises parameters derived from user surveys. ### Claim 18 (depends on 17) The method according to claim 17, wherein the user surveys comprise virtual reality driving simulations that simulate driving on roads with CAV-dedicated lanes. ### Claim 19 (depends on 16) The method according to claim 16, wherein the ABM models characteristics of the CAV-dedicated lanes. ### Claim 20 (depends on 19) The method according to claim 19, wherein the characteristics comprise lane speed, lane priority, or whether a CAV-dedicated lane is a toll lane.