Abstract: The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

Abstract: Robot-assisted package delivery with integrated curbside parking monitoring and curb operations optimization is disclosed herein. An example method includes dispatching a delivery vehicle and delivery robot to a delivery location, the delivery location including a parking location for the delivery vehicle that allows for deployment of the delivery robot on a delivery mission, determining occupancy of the parking location, the delivery vehicle parking at the parking location when the parking location is unoccupied, the delivery robot being deployed upon parking of the delivery vehicle, and instructing the delivery vehicle to remain parked during the delivery mission or to leave the parking location and return at later point in time based on an estimated time of arrival of the delivery robot after the delivery mission.

Abstract: Example embodiments described in this disclosure are generally directed to systems and methods for defining and operating a fleet of vehicles. In a first example method, a processor may evaluate a personal attribute of an individual such as, for example, an age of the individual (65+, under 65, infant, etc.) and/or a physical disability (requiring a wheelchair, for example). The processor selects a shared mobility service model that is defined, at least in part, on the basis of the personal attribute of the individual, and then applies the shared mobility service model to identify a first vehicle (having a wheelchair ramp, an infant seat, etc.). The identified vehicle is then assigned to provide service to the individual. In a second example method, a processor may evaluate a demography of a service area (various age groups, various physical disabilities, etc.) and generate a shared mobility service model based on the demography.

Abstract: Systems and methods for predicting travel patterns of a vehicle or one or more occupants in the vehicle are provided. A transceiver may receive data indicative of at least one of passenger information, vehicle occupancy, vehicle status, and vehicle location from at least one of a voice recognition device, IoT sensors such as seat sensors, vehicle status sensors, or a GPS system. A processor may determine travel patterns based on the data, predict travel patterns based on the determined travel patterns, and generate a database comprising the predicted travel patterns. In addition, the processor may generate an alert if the determined travel pattern deviates from the predicted travel patterns.

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.

Abstract: A method for generating an infrastructure management plan is described. The method may include determining, via a Regional Traffic Simulation (RTS) model, an opportunity area for connected and automated vehicle (CAV) integrated traffic flow. Generating a regional key performance indicator associated with the opportunity area and modeling a regional KPI associated with local traffic in the opportunity area using a Local Traffic Simulation (LTS) model. The method may further include determining a local KPI associated with the opportunity area, generating a tentative traffic solution associated with the opportunity area, connecting the LTS model to the RTS model using an outer feedback loop, and determining a ripple effect of the tentative traffic solution via the RTS model. The method concludes with generating the infrastructure management plan comprising the change to the LTS model responsive to confirming an improvement to the regional KPI and the local KPI.

Abstract: Systems and methods for mobility service demand prediction are disclosed herein. An example method includes determining trips of an existing transportation mode for a geo fenced area that are poorly served. The trips that are poorly served can include any one or more of the trips with a moderately high trip density, a moderately high ratio of transit-private vehicle trips, and/or a moderately high ratio of transit-time to private vehicle travel times. The method can include modeling trips of a new transportation service for a geofenced area, determining, based on the modeling, when the trips of the new transportation service have at least one improved travel parameter relative the trips that are poorly served, as well as generating a mobility service demand prediction that is indicates how likely users are to adopt new transportation service in view of the existing transportation mode.

Abstract: The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

Abstract: Robot-assisted package delivery with integrated curbside parking monitoring and curb operations optimization is disclosed herein. An example method includes dispatching a delivery vehicle and delivery robot to a delivery location, the delivery location including a parking location for the delivery vehicle that allows for deployment of the delivery robot on a delivery mission, determining occupancy of the parking location, the delivery vehicle parking at the parking location when the parking location is unoccupied, the delivery robot being deployed upon parking of the delivery vehicle, and instructing the delivery vehicle to remain parked during the delivery mission or to leave the parking location and return at later point in time based on an estimated time of arrival of the delivery robot after the delivery mission.

Abstract: Hybrid light personal electric vehicle analysis is disclosed herein. An example method includes determining empirical data from a light personal electric vehicle (LPEV) over a period of time, determining LPEV behaviors from the empirical data during the period of time, and building a hybrid simulation model over the period of time by switching between a pedestrian simulation model when the LPEV behaviors correspond to pedestrian behaviors and a vehicle simulation model when the LPEV behaviors correspond to vehicle behaviors.

Abstract: Geolocation based vehicle access systems and methods are disclosed herein. An example method includes receiving a blockchain ledger from a vehicle operating in a controlled area, the blockchain ledger including time-stamped, location and vehicle data. The method also includes comparing the time-stamped, location and vehicle data to database records that have control access parameters for controlled areas. In addition, the method includes generating a notice when the time-stamped, location and vehicle data matches control access parameters for the controlled area, which is one of the controlled areas. More so, the method includes providing the notice to the vehicle or a user of the vehicle.

Abstract: Hybrid light personal electric vehicle analysis is disclosed herein. An example method includes determining empirical data from a light personal electric vehicle (LPEV) over a period of time, determining LPEV behaviors from the empirical data during the period of time, and building a hybrid simulation model over the period of time by switching between a pedestrian simulation model when the LPEV behaviors correspond to pedestrian behaviors and a vehicle simulation model when the LPEV behaviors correspond to vehicle behaviors.

Abstract: Systems and methods for predicting travel patterns of a vehicle or one or more occupants in the vehicle are provided. A transceiver may receive data indicative of at least one of passenger information, vehicle occupancy, vehicle status, and vehicle location from at least one of a voice recognition device, IoT sensors such as seat sensors, vehicle status sensors, or a GPS system. A processor may determine travel patterns based on the data, predict travel patterns based on the determined travel patterns, and generate a database comprising the predicted travel patterns. In addition, the processor may generate an alert if the determined travel pattern deviates from the predicted travel patterns.

Abstract: The present disclosure relates to methods and systems for performing a cost-benefit analysis of a microtransit service, including a method utilizing both transportation simulation and experimental design in connection with the cost-benefit analysis.