WO2019182555A1

WO2019182555A1 - Customizing resources in a shared vehicle environment

Info

Publication number: WO2019182555A1
Application number: PCT/US2018/023135
Authority: WO
Inventors: Jamel Seagraves; Chih-Wei Tang; Sudipto Aich; Beaudry KOCK
Original assignee: Ford Motor Company
Priority date: 2018-03-19
Filing date: 2018-03-19
Publication date: 2019-09-26

Abstract

An intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle use parameter predictions based on historical data, including various utilization statistics. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may include the parameters used by other users based on one or more pieces of biometric information about the users. In some embodiments, a model, such as a machine learning model (e.g., neural network) may be trained using the historical data as training data such that the model can predict bicycle use parameters for users based on biometric information.

Description

CUSTOMIZING RESOURCES IN A SHARED VEHICLE ENVIRONMENT

BACKGROUND

[0001] As cities become more complex and populations continue to grow and become more mobile, demand on transportation and related infrastructure is increased. And as lifestyles become increasingly dynamic, people and places are becoming more connected than ever before. People often need to travel within, and between, cities for various reasons such as for work, socializing, and recreation, among others. The frequency and scheduling of travel may vary greatly as well, be it a daily commute, a scheduled event, or a spontaneous trip. Regardless of the distance, destination, or time of travel, people expect to have the freedom to move about and be at the desired location at the desired time. Thus, transportation and mobility systems are fundamental in making such a lifestyle possible. In urban and suburban areas alike,

transportation mode sharing programs, such as bicycle (“bike”) sharing programs, have become commonplace. Such programs, in essence, provide a service whereby bicycles are made available for shared use to individuals on a short-term basis, such as for rent or borrow.

However, conventional bike sharing systems have various shortcomings that lead to a suboptimal user experience and suboptimal utilization efficiency. For example, it may be difficult for users to know or remember various bike settings, such as seat height or handle bar position.

Accordingly, the user may not properly adjust the bike, leading to a suboptimal user experience that may decrease the likelihood of using the bike again.

BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which:

[0003] FIG. 1 illustrates an example transportation landscape in which the present systems and methods may be implemented. [0004] FIG. 2 illustrates an example of a smart bicycle shaimg sysiem wun siauun-uaseu intelligence, in accordance with various embodiments of the present disclosure.

[0005] FIG. 3 illustrates an example of a smart bicycle sharing system with bike-based intelligence, in accordance with various embodiments of the present disclosure.

[0006] FIG. 4 illustrates an example user interface of a smart bicycle sharing system, in accordance with various embodiments.

[0007] FIG. 5A illustrates an example of a smart bicycle with adjustable components, in accordance with various embodiments.

[0008] FIG. 5B illustrates an example of a smart bicycle with adjustable components, in accordance with various embodiments.

[0009] FIG. 6 illustrates a diagram of an example system implementation for providing resource use parameter predictions for a vehicle sharing environment, in accordance with various embodiments.

[0010] FIG. 7 illustrates an example process for predicting use parameters of a resource in a vehicle-sharing environment, in accordance with various embodiments of the present disclosure.

[0011] FIG. 8 illustrates an example process for predicting and updating use parameters of a resource in a vehicle-sharing environment, in accordance with various embodiments of the present disclosure.

[0012] FIG. 9 illustrates a set of basic components of one or more devices of the present disclosure, in accordance with various embodiments of the present disclosure.

DETAILED DESCRIPTION

[0013] In the following description, various embodiments will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the embodiments. However, it will also be apparent to one skilled in the art that the embodiments may be practiced without the specific details ruiuieimuie, wen-Kiiuwn features may be omitted or simplified in order not to obscure the embodiment being described.

[0014] Systems and methods in accordance with various embodiments of the present disclosure may overcome one or more of the aforementioned and other deficiencies experienced in conventional approaches for vehicle sharing. In particular, various embodiments provide for predicting and recommending use parameters for vehicles to users interested in utilizing the vehicles, such as bicycles in certain embodiments.

[0015] A user parameter prediction system, which may be used with a bicycle sharing system, is able to provide information to users regarding biometric settings of the vehicle. For example, the user parameter may include a seat height, a handle bar location, a pedal position, and the like. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may be tied to user profiles associated with the bicycle sharing system, which may include biometric information such as user height, user weight, and the like. Additionally, in various embodiments, ergonomic parameters may be sourced and compiled in order to predict user settings based on user biometric information. In some embodiments, a model, such as a machine learning model (e.g., neural network) may be trained using the historical data and/or the ergonomic parameters as training data such that the model can predict user parameters based on biometric information associated with a user. In various embodiments, the user parameter prediction may be determined using additional types of data, such as user feedback.

[0016] For example, a user may request, using a user device (e.g., smartphone), user parameters for using a bicycle that may be rented or otherwise shared. Upon receiving the request, biometric information associated with the user may be processed through the above- mentioned trained model to determine the user parameters. The user parameters may be further processed and presented to the user in a variety of forms. For example, components of the bicycle may be adjustable, with adjustments represented by numbers of letters (e.g., a seat height of 6 or a handlebar position of B). Furthermore, in embodiments, the bicycle may be equipped with one or more adjustment mechanisms, such as motors, that automatically adjust a position of the various components. [0017] In various embodiments, the user may provide feedba legaiumg me cuniiui i ui associated with the recommended user parameters. For example, the user device may present a questionnaire to the user regarding the seat height or the handlebar position. Furthermore, the user device may display instructions to the user, such as a video animation illustrating a“proper” seat height. The user may provide feedback, which may be loaded into the model as additional training information or may be stored with the user profile. Accordingly, as information is collected, more precise predictions may be provided to users. Further, by providing information to the users regarding“proper” settings, the information may be normalized because feedback will be associated with the same target setting.

[0018] In various embodiments, user information may be stored such that at a later time, for example upon receiving a request to use another bicycle, that the user device may display the previously recommended properties to the user. Accordingly, the user may save time from having to adjust the components of the bicycle to“guess” which setting will be most comfortable for them. Furthermore, providing the information to the user enhances the user experience, which may cause users to utilize the bicycle service more often.

[0019] Although examples illustrated in the present disclosure describe a bicycle sharing system, that systems and methods provided here are applicable to any type of vehicle or mobility resources, such as cars, airplanes, boats, carts, scooters, motorized bikes or scooters, skates, hoverboards, among many others.

[0020] Various other features and application can be implemented based on, and thus practice, the above described technology and presently disclosed techniques. Accordingly, approaches in accordance with various embodiments improve the technology of bicycle sharing systems.

Traditional bicycle sharing technology includes mechanisms for checking out (e.g., unlocking) bicycles from docking stations based user authentication or payment authentication.

[0021] The present disclosure provides an intelligent networked bicycle sharing system that is instrumented with specialized sensors, network interfacing devices, and other electronics that enable users to receive up to date information and even future predictions that can enable them to better utilize the bicycles. Various other applications, processes, and uses are presented below with respect to the various embodiments, each of which improves me opeiauon anu peiu imance of the computing device(s) on which they are implemented.

[0022] FIG. 1 illustrates an example transportation landscape 100 in which the present systems and methods may be implemented, in accordance with various embodiments. As illustrated in FIG. 1, many types of modes of transportation and mobility may be available in various cities, often depending on certain characteristics of the city, such as population size, population distribution, terrain, among others. Examples of modes of transportation and mobility may include personally owned vehicles 102, public transportation systems such as buses 104 and trains 106, bike sharing 108, and walkingl 10. Cities may often have a primary mode of transportation or a combination of several modes.

[0023] For example, individual car ownership is common in sprawling cities where the population is relatively less dense. Such cities tend to have a less developed public transportation system (e.g., buses, commuter trains) due to the low utilization efficiencies. For example, the cost of developing, maintaining, and operating such systems may outweigh the benefit they provide to the community. In such cities, since locations of interest may be further apart, walking and biking may be less common as well. Thus, the population in cities are heavily reliant on individual cars. However, there may be certain times or locations that are prone to road congestion due to the number of cars on the road or in the area, such as during typical work commuting hours also known as "rush hour" and when there are special events that cause large groups of people to congregate in a small area, such as for a concert or sporting event.

Additionally, individual cars are rarely used at full capacity, especially when used for work commutes, resulting in efficiency losses in terms of both space and energy.

[0024] Conversely, densely populated cities tend to have a more multi-modal mobility landscape. For example, in addition to individual cars, desnsely populated cities also tend to have a more established public transportation system and the population tends to rely more heavily on public transportation. Locations are likely to be closer together and more people are likely to live close to bus stops, subway stations, etc., making such mean of transportation useful and convenient. However, public transportation conventionally runs on fixed schedules and fixed routes and have fixed pick-up/drop-off locations. This means mai peupie nave LU pian aiuunu me factors, including planning their schedules, determining which destinations are convenient, and even where they want to live. For example, some buses only run during certain operating hours and are thus are not available as an option during off hours. The inflexibility of conventional transportation also affects businesses and real estate utilization. For example, businesses located close to subway stations or other public transportation access points may have increased foot traffic or patronage due to the convenience. Similarly, residential buildings that are close to such public transportation access points may also be more desirable at least to some. In effect, the fixed nature of conventional transportation coupled with the population's reliance on it may cause these densely populated cities to become even more clustered around these access points, rather than evenly utilizing space across the city. Commuter trains provide a means for traveling within and between several cities and is prevalent between cities with populations that may live in one city and work in another city or other have populations that frequently travel between the cities. However, like other forms of conventional public transportation, commuter trains typically run according to a set schedule between set stops.

[0025] Densely populated cities may also be more conducive for walking, as destinations may often be within a short, walkable, distance. Walking also provides the added benefits of independence, energy conservation, and fitness gains. However, cities and neighborhoods may vary in pedestrian safety and ease. For example, well-lit sidewalks and other paved pedestrian paths may provide a better environment for pedestrians, and thus more people may consider walking as a practical form of travel. Additionally, weather may also influence pedestrians. For example, inclement weather may make walking impossible at times, even for a short distance. Additionally, there may be other circumstances that make walking particularly difficult, such as if a person is carrying large or heavy items or wearing uncomfortable shoes, the destination being further away, among other situations. Thus, although walking may be an available form of mobility in certain types of environments, it may be difficult to rely upon it all of the time.

Similar to walking, bicycles provide an alternative to that allows people to travel relatively independently while conserving energy and gaining fitness benefits. However, people may not have space to store a bicycle or may not use bicycles frequently enough to warrant owning one. As illustrated in FIG. 1, bike sharing has recently become a pupuiai means ui piuviumg snaieu access to bicycles when needed. For example, a user can rent or borrow a bicycle from a bike station for a particular trip and return the bicycle to another bike station at their destination or to the original bike station upon returning from the trip.

[0026] As populations grow and lifestyles become increasingly dynamic, people and places are becoming more connected than ever before. People often need to travel within and between cities for various reasons such as for work, socializing, recreation, among others. Such travels may vary greatly in distance, such as a few blocks, across town, across the country, or even abroad. The frequency and scheduling of the travels may vary greatly as well, such as daily commutes, a scheduled event, and spontaneous trips. Regardless of the distance, destination, or time of travel, people expect to have the freedom to move about and be at their desired location at a desired time. Thus, transportation and mobility systems are fundamental in making such a lifestyle possible. However, conventional means of mobility are legacy systems may no longer be suitable to meet the needs of present and future cities and their populations. For example, as people become more connected, there may often be influxes of traffic to certain area due to large gatherings, such as for organized events, to spontaneous gatherings quickly galvanized through social media, among other phenomenon that is unique to modem societies. Transportation systems and modalities need to be robust enough to handle the the changing demands, redefining and yet working within the constraints of existing infrastructures. However, as population grows, more land is used to build housing, office, and retail space to meet the demands of the increasing population. Thus, additional transportation is needed to support the mobility of the population, yet less space is available for transportation. For example, less space may be available for parking and yet there may be an increase in the number of cars. Such resource constraints mean that transportation and mobility technology must be designed and innovated upon for increased efficiency, providing dynamic services that meet the needs of present and future populations while reducing the resources required to do so.

[0027] The present disclosure is directed to technology for bicycle sharing systems as well as other vehicle sharing systems. Existing bicycle sharing systems 108 typically include a station 112 which holds a plurality of bikes 114. A user may interact with a kiosk 116 at the station 112 to rent or borrow one of the available bikes 114 if there are any. rui example, me usei may swipe a card (e.g., credit card, membership card, identification card) to unlock a bike.

Conversely, when a user if finished using a bike, they may return the bike by docking the bike back onto a station. However, the situations may arise in which a user arrives at a bicycle station with the intention of getting a bicycle, only to find that there are no bikes available, throwing a wrench into their plans. Similarly, a user may want to return their bike to a station when they arrive at their destination but find that the station is full and has no docking spots available.

Thus, the user may have to find another station, which may be further away and without knowing if there will be docking spots available at that station.

[0028] FIG. 2 illustrates an example of a smart bicycle sharing system 200 with station-based intelligence, in accordance with various embodiments. An intelligent bicycle sharing system 200 may include a plurality of bike stations 202 located in different geographic locations, such as various parts of a neighborhood, city, or across multiple regions across the country. The bike stations are connected to one or more networks 204, such as the Internet, a cellular network, a local area network (LAN), an Ethernet, Wi-Fi, or a dedicated network, among other such options. The bike stations 202 may collect various data regarding bike utilization and other parameters associated with respective stations. Such data collected from the plurality of bike stations, coupled with respective metadata, may be used by a compute server 214 to determine various utilization statistics, patterns, and other insights that can be used to optimize the intelligent bicycle sharing system 200. User devices 216, such as smart phones, tablet, wearables, personal computer, and the like, may be communicative with individual bike stations 202 and/or the compute server 214 over the one or more networks 204, allowing users to provide input information and receive output information with respect to the bicycle sharing system 200.

[0029] In various embodiments, a bike station of the intelligent bicycle sharing system 200 may include a docking portion 206 for holding a plurality of bicycles 208. In some embodiments, the docking station 206 may have a specific number of docking spots 210 and thus can hold a maximum number of bicycles 208. In some other embodiments, the docking station 202 does not have individually defined docking spots. The docking portion may include locking mechanisms for locking the bicycles to the bike station 202. In some embodiments, there is one locking mechanism for each docking spot for locking one bicycle to tne LM KC siauun. in uns example, me locking mechanism may lock conventional bicycles to the bike station, in which the bicycles do not need specific or corresponding hardware. Thus, in this embodiment, the bicycles 208 of the intelligent bike sharing system may be conventional bicycles that do not include special hardware or electronic devices. In some embodiments, the docking portion 206 may not include locking mechanism such that the bicycles can be freely used. Specifically, the bicycles 208 in such embodiments may be removed and returned without needing to be unlocked from the bike station. In such embodiments, the bicycles 208 and/or the bike stations may include various sensor devices to detect when a bicycle is removed from the station, when a bicycle is returned to the station, or general availability of bicycles at a station, among other utilization data.

[0030] A bike station 202 may include a kiosk portion 212 for facilitating checking out or checking in of bicycles. In some embodiments, the bike station 202 may include one kiosk that controls the locking and unlocking of all of the docking spots at the bike station. In some other embodiments, each docking spot may include its own kiosk. A kiosk 212 may include an interface, such as a human-machine interface that may include a combination of user interfacing components, such as a display, a keypad, buttons, a touchscreen, audio output, microphone, camera, among others. The kiosk 212 may also include various payment or identity verification devices, such as coin-drops or cash receptacles, magnetic card readers for reading credit cards, debit cards, account cards, or other types of magnetic cards. The kiosk 212 may also include near-field communication (NFC) readers, Bluetooth, among various other wireless

communication interfaces and devices. The kiosk 212 may also include one or more biometric identification features such as a fingerprint recognition, facial recognition, and the like.

[0031] The kiosk portion 212 may enable a user to checkout a bicycle by performing one or more actions, such as entering account information, swiping, tapping, or holding a card at the card reader, depositing cash, among others. If the information provided by the user, either in the form of entered authentication parameters (e.g., account number, password), credit card, account card, or other device (e.g., phone, smartwatch) is authenticated, a bike 208 may be unlocked from the bike station 202 and the user can use the bike 208. In some embodiments, depositing a required amount of cash may also cause the bike to be unlocked. [0032] In some other embodiments, the bike station 202 may meiuue a wneiess communication interface that does not include human interfacing components. Rather, in such embodiments, the bike station 202 may communicate with a user device 216 directly through a wireless communication protocol. For example, the user device 216 may include a mobile device carried by a user. The user device 216 may have a specific software application (i.e., "app") installed thereon for providing a user interface between the user and the bike station 202. The user may perform certain actions on the user device through the app to check out and/or check in a bicycle 208. In some embodiments, the app may be associated with an account for the user and/or be connected to a form of payment such as credit card credentials (e..g, credit card number) or bank account credentials (e.g., account number, routing number), or other third party payment platforms. In some embodiments, authentication and user identification may be performed passively, such as through proximity based sensing. For example, a device may emit a signal and a user carrying such a device may approach a bike station, and when the device is within a signal detection range of the bike station, the bike station may detect the device and receive a signal emitting from the device. The signal may include authentication parameters, thereby causing the user to be authenticated and a bicycle to become unlocked.

[0033] In any of the above embodiments, among other embodiments, the intelligent bike sharing system may collect various types of data across the plurality of bike stations 202. For example, each bike station 202 may collect data regarding when a bike is checked in or out, and by whom. In some embodiments, each bike 208 in the intelligent bike sharing system includes a unique identifier such that the bike stations 202 can identify which bike is being checked out or checked in. Thus, the journey of a particular bicycle 208 can be tracked. For example, it can be detected that bike A was checked out at a bike station at a first location and checked in at another bike station at a second location at a later time, and thus it can be inferred that bike A was used for a trip from the first location to the second location. The data collected from the bike stations may include metadata such as a bike station identifier and timestamp, and may include or be associated with a geographic location among other metadata.

[0034] The compute server 214 may receive the data and the metadata collected from bike stations via the one or more networks 204. The at least one network 204 can include any appropriate network, including an intranet, the Internet, a celluiai HCLWUI , a lucai aiea HCLWUI K (LAN), or any other such network or combination, and communication over the network can be enabled via wired and/or wireless connections. In various embodiments, the compute server 214 may include one or more servers with one or more processors and storage elements for storing and processing the data received from the bike stations 202 and performing various functions utilizing the data, such as authenticating a user based on provided credentials, performing transactions, recording and analyzing bike usage data, tracking a location of a bike, among other computer functions. In various embodiments, one or more data analysis models (e.g, trained machine learning based model) may be stored in the compute server 214 and used to make determinations or predictions based on various data. In some embodiments, the compute server 214 may include a distributed computing system, or "cloud computing" environment, in which computing and storage may be distributed across a network of resources, such as servers and storage, which may be rapidly provisioned as needed.

[0035] In various embodiments, a user interface to the intelligent bike sharing system may be provided via the user devices 216, which are connected to the one or more networks 204. The user devices 216 may include devices through which a user can watch, listen to, or read content, and include at least one form of input such as a keyboard, buttons, or touchscreen, and at least one form of output such as a display or speaker. The user devices 216 can include various computing devices such as smart phones, tablet computers, wearable computers (e.g., smart glasses or watches), desktop or notebook computers, and the like. The user devices 216 can include any appropriate electronic device operable to send and receive requests, messages, or other such information over an appropriate network and convey information back to a user of the device. In this example, the user devices 216 can communicate with the server compute environment 214 over the at least one network 204. A user is able to utilize a user device 216 to interact with the intelligent bike sharing system, such as to view updates or data related to various bike stations 202, such as currently available bikes, and the like. The user may also be able to check out a bike or check in a bike through the user device 216, access their account, among other interactions. In some embodiments, a software application ("app") may be installed on the user device 216 specifically to provide a user interface iui nneiacung wun me iineingein bike sharing system.

[0036] FIG. 3 illustrates an example of a smart bicycle sharing system 300 with bike-based intelligence, in accordance with various embodiments. An intelligent bicycle sharing system 300 may include a plurality of networked bicycles 302. The bicycles are connected to one or more networks 304, such as the Internet, a cellular network, a local area network (LAN), an Ethernet, Wi-Fi, or a dedicated network, among other such options. The bicycles 302 may collect various data regarding bike utilization, geographic location, routes taken, biometric properties of riders, among other information. Such data collected from the plurality of bicycles, coupled with respective metadata, may be used by a compute server 306 to determine various user parameters associated with different riders. For example, if a number of riders that self-report themselves as being approximately six feet tall position a seat height at a certain level (e.g., level 4), that information may be utilized to predict how other riders having a similar height may adjust components on their bicycle. User devices 308, such as smart phones, tablet, wearables, personal computer, and the like, may be communicative with individual bicycles 302 and/or the compute server 306 over the one or more networks 304, allowing users to provide input information and receive output information with respect to the bicycle sharing system 300.

[0037] In various embodiments, the intelligent bicycle sharing system 300 may be dockless, in which the bicycles 302 do not need to be docked at individual docking spots as described above with respect to the bike stations 202 in FIG. 2. Rather, the bicycles 302 may be parked at designated zoned areas, conventional parking spots and bicycle racks, or anywhere a bicycle may be positioned. The bicycles 302 may each include a processor, a network communications interface, and a location tracking device such as a global position system (GPS) unit. These components allow the bicycle to collect data and communicate the data over the one or more networks. For example, the GPS unit tracks the geographic location of the bicycle 302, allowing the current location as well as a travel path of the bicycle 302 to be known.

[0038] In some embodiments, a bicycle 302 may include a locking mechanism that locks the bicycle to a structure. For example, a bicycle 302 may be locked to a designated structure. In some other embodiments, the locking mechanism may lock the luncuuns ui me uicycie, rendering it unusable without necessarily locking it to a structure. For example, the locking mechanism may lock a wheel of the bicycle, a gear, a chain, or any other component of the bicycle that is needed in order for a user to ride the bicycle. In various embodiments, the locking mechanism of a bicycle may be released upon performing a user authentication process, which may take many forms.

[0039] A bicycle 302 may include an interface, such as a human-machine interface that may include a combination of user interfacing components, such as a keypad or touch screen through which a user may enter credentials (e.g., username, password, pin number). In some

embodiments, the credentials may be in the form of biometric data such as fingerprint, retina scan, and the like. In some embodiments, the bicycle may include detectors or readers for accepting cards (e.g., credit cards, debit cards, account cards, or other types of

membership/identification cards) or other signal-based tokens (e.g., key fob, smart phone, wearable device, RFID devices). The detectors or readers on the bicycle may include near-field communication (NFC) readers, Bluetooth, among various other wireless communication interfaces and devices. The interface on the bicycle enables a user to unlock or otherwise checkout a bicycle by performing one or more actions, such as entering account information, swiping, tapping, or holding a card or at the card reader, presenting a smart phone or other user device, among others. If the user is successfully authenticated, the bicycle may be unlocked and the user can use the bike. In some embodiments, the detector on the bicycle may be a proximity based sensor, which may detect a signal-based token within range and automatically unlock the bicycle when a user carrying such a token is within range. The identity of the user may also be identified through the token. In various embodiments, the bicycle may include various output devices as a part of the human-machine interface, such as as speakers, displays, tactile feedback device, among others, for presenting various information to the user.

[0040] In some other embodiments, the bicycles 302 may include a wireless communication interface that does not include human interfacing components. Rather, in certain such embodiments, the bicycles 302 may communicate with a user device through a wireless communication protocol. In other such embodiments, the bicycle may communicate with a computer environment 306 over the one or more networks30^ lamei man unecuy wun me usei device 308. For example, the user device 308 may include a mobile device carried by a user. The user device 308 may have a specific software application (i.e., "app") installed thereon for providing a user interface between the user and the bicycles. The user may perform certain actions on the user device through the app to check out and/or check in a bicycle. In some embodiments, the app may be associated with an account for the user and/or be connected to a form of payment such as credit card credentials (e..g, credit card number) or bank account credentials (e.g., account number, routing number), or other third party payment platforms. In some embodiments, authentication and user identification may be performed passively, such as through proximity based sensing. For example, a device may emit a signal as the user carrying such a device approaches a bike station, and when the device is within a signal detection range of the bike station, the bike station may detect the device and receive a signal emitting from the device. The signal may include authentication parameters, thereby causing the user to be authenticated and a bicycle to become unlocked. In other embodiments, the user device 308 may submit a request to the compute environment 306, including credentials and location or a specific bicycle the user would like to unlock. The computer environment may authenticate the request and transmit instructions to the bicycle to be unlocked.

[0041] As described, the bicycles 302 may include various sensors, processors, and other electronic devices to gather and transmit data. For example, it may be detected when a user checks out or unlocks the bicycles as well as the identity or account associated with the the user, and when the user checks the bicycle back in to be available for use by another user.

Additionally, the location of the bicycles during these events, and at any other time, may be known as well. Various other types of data may be detected as well, and can be used to provide various useful insights or perform various tasks.

[0042] The compute server 306 may receive the data and the metadata collected from the bicycles via the one or more networks 304. The at least one network 304 can include any appropriate network, including an intranet, the Internet, a cellular network, a local area network (LAN), or any other such network or combination, and communication over the network can be enabled via wired and/or wireless connections. In various embodiments, the compute server 306 may include one or more servers with one or more processors anu sun age eiemems iui suning and processing the data received from the bike stations and performing various functions utilizing the data, such as authenticating a user based on provided credentials, performing transactions, recording and analyzing bike usage data, tracking a location of a bike, receiving feedback from users, among other computer functions. In various embodiments, one or more data analysis models (e.g, trained machine learning based model) may be stored in the compute server 306 and used to make determinations or predictions based on various data. In some embodiments, the compute environment may include a distributed computing system, or "cloud computing" environment, in which computing and storage may be distributed across a network of resources, such as servers and storage, which may be rapidly provisioned as needed.

[0043] In various embodiments, as mentioned, a user interface to the intelligent bike sharing system 300 may be provided via the user devices 308. The user devices 308 may include devices through which a user can watch, listen to, or read content, and include at least one form of input such as a keyboard, buttons, or touchscreen, and at least one form of output such as a display or speaker. The user devices 308 can include various computing devices such as smart phones, tablet computers, wearable computers (e.g., smart glasses or watches), desktop or notebook computers, and the like. The user devices 308 can include any appropriate electronic device operable to send and receive requests, messages, or other such information over an appropriate network and convey information back to a user of the device. In this example, the user devices 308 can communicate with the server compute server 306 over the at least one network 304. A user is able to utilize a user device 308 to interact with the intelligent bike sharing system, such as to view updates or data related to various bike stations, such as currently available bikes, and the like. The user may also be able to check out a bike or check in a bike through the user device, access their account, among other interactions. In some embodiments, a software application ("app") may be installed on the user device specifically to provide a user interface for interacting with the intelligent bike sharing system.

[0044] The present disclosure provides an intelligent vehicle sharing system, such as the bicycle sharing systems of FIGS. 2 and 3, able to provide helpful information related to the vehicles, such as user parameters, availability predictions, utilization statistics, and the like. For example, the user parameters may come from historical data lepiesemauve ui useis uiai nave used the bikes. The historical data may track certain biometric properties of the users, such as a height of the user, and correlate the height to one or more settings related to a relative position of components of the bike, such as a seat height or handlebar position. This historical data may be compiled and utilized to predict user parameters for a new user that has not utilized the service, or to recommend improved parameters to users to enhance comfort and the user experience associated with the bicycles. In some embodiments, a model, such as a machine learning learning model (e.g., neural network) may be trained using the historical data, among other data, as training data such that the model can predict user parameters based on biometric information of the user.

[0045] FIG. 4 illustrates an example user interface 400 on a user device 402 for optimizing and predicting user parameters, in accordance with various embodiments. Specifically, a user may log into or create a user account associated with the user and the vehicle sharing service. In the illustrated embodiment, a user identification 404 identifies“John Doe” as the user preparing to utilize the vehicle sharing service. In various embodiments, the user identification 404 and associated user account may include information such as payment options (e.g., credit card numbers, bank account information, third party payment services, etc.) and biometric

information of the user (e.g., height, weight, etc.). This information may be input via the user, or in certain embodiments, may be obtained via one or more programs associated with the application. For instance, a user may upload a photo and the one or more programs may determine features of the user, such as leg length relative to height or arm length relative to height. As will be described below, this information may be used to predict user parameters for the user to enhance the user’s experience with the vehicle sharing service.

[0046] The illustrated embodiment further includes a vehicle identifier 406, which directs the user to a particular vehicle (e.g., bike in the illustrated embodiment). It should be appreciated that in various embodiments the user may be free to choose any available vehicle. However, in embodiments, the application may be utilized to identify a bike having parameters already set that are close to the recommended parameters for the user. As a result, fewer adjustments will be made to the bike, thereby streamlining the process and providing an improved user experience. [0047] FIG. 4 further illustrates a set of user parameters 40b cuiiespuiiuing iu a pusuiun ui adjustment of one or more components of the bike. The illustrated embodiment includes seat position, handle bar position, and pedal position. For instance, seat position may refer to a height of the seat, relative to a ground plane (e.g., a position along a y-axis). Handle bar position may refer to a lateral position of the handle bars relative to the seat (e.g., a position along an x-asis). Pedal position may refer to a lateral position of the pedals relative to the seat (e.g., a position along a z-axis). It should be appreciated that the illustrated embodiment is not intended to be limiting and other adjustments to the bike or other vehicles may be used. For example, a width of the handle bars (e.g., along the z-axis) may be adjusted. Furthermore, a tilt of the seat (e.g., an angle with respect to the y-axis) may also be adjusted. Accordingly, any type of adjustment associated with one or more of the components of the vehicle may be included as a user parameter 408.

[0048] In various embodiments, the system may receive feedback from the user in order to improve the model described above. As shown in FIG. 4, the interface 400 includes a feedback option 410 to receive information from the user regarding the fit of the seat based on the recommended user parameters 408. The user may click or otherwise select the feedback option 410 and provide information related to the seat position, handle bar position, pedal position, or the like. For example, the user may indicate that the seat position 5 illustrated in FIG. 4 is too high. This information may be received by the user interface 400, in the form of historical data, and thereafter utilized to improve future recommendations. Additionally, the information may be stored in memory associated with the user profile associated with the identified user. As a result, future uses of the bicycles may lead to recommendations for seat position 4, rather than 5, based on the user’s personal preferences. It should be appreciated that in various embodiments the user interface 400 may provide guidance or information to assist the user in providing feedback. For instance, upon selection of the feedback option 410 an animation or video may play to illustrate the“proper” position for various components of the bicycle. Accordingly, the user will be better informed on how to answer the questions. Furthermore, in embodiments, the feedback option 410 may present a variety of questions to the user to determine exactly what areas of the recommended use parameters are unsatisfactory. For instance, the feedback option 410 may ask the user“Do you have trouble reaching the grounu ai mis seai neiginr ui uv yuui knees straighten or lock while pedaling?” This information may provide useful biometric information that may be utilized to improve future predictions.

[0049] FIG. 5A illustrates an example of a bicycle 500 arranged on a ground plane 502. In various embodiments, one or more components of the bicycle 500 are adjustable. For example, a seat 504 may be adjusted such that a seat height 506, relative to the ground plane 502, is modified. Furthermore, the handle bars 508, pedals 510, and the like may also be adjusted in order to provide efficient operation and comfort to users. In various embodiments, the bicycle 500 includes adjustment mechanisms 512 arranged at various locations to facilitate adjustment of the components. The adjustment mechanisms 512 may be manually operated, such as via pin- and-hole connectors, geared connectors, adjustable fasteners, and the like. Furthermore, in various embodiments, the adjustment mechanisms 512 may be automatically controlled, for example, upon receipt of instructions from the network. By way of example, the adjustment mechanism 512 on the seat 504 may change the seat height 506 based on a control received from the network, which may be transferred to the network via the user interface. Accordingly, components of the bicycle 500 may be adjusted by the user, either manually or automatically.

[0050] As described above, in various embodiments the relative position of the various components may be determined before the bicycle 500 is authorized for check out to a user.

That is, the network may store information about each bicycle 500 at the bike stations 202 and thereafter authorize for use a bicycle 500 that would necessitate the fewest adjustments based on the use parameters for a given user. For example, if the bicycle 500 in slot 1 had components in positions corresponding to the user parameters for a given user, the bicycle 500 in slot 1 may be selected as the bicycle 500 that the user will be authorized to check out. Additionally, if the bicycle 500 in slot 4 would have appropriate use parameters by adjusting a single component while the bicycle 500 in slot 2 would have appropriate use parameters by adjusting 3

components, the bicycle 500 in slot 4 may be selected to thereby reduce the number of adjustment the user will make manually or the number of automatic adjustments. This intelligent selection of bicycles 500 improves the user experience by reducing the number of adjustments the user makes and also reduces wear and tear on automatically adjusting components. Additionally, it should be appreciated that in various embodimeins me uicycies JW may icceive information directly from the user. For example, the bicycles 500 may include one or more communication devices, such as wireless transceivers and the like, which enable communication with an associated user device of the user. As a result, when the user requests to a bicycle 500 or otherwise evaluates adjustments the bicycle 500 may be enabled to do so without interaction with the network.

[0051] FIG. 5B illustrates an example of the bicycle 500 arranged on the ground plane 502 after one or more adjustments to the components have been made. In the illustrated

embodiment, a seat height 514 is less than the seat height 506 illustrated in FIG. 5 A. That is, the seat 504 has been lowered relative to the ground plane 502. In various embodiments, the user may manually lower the seat 504, for example, by loosening a bolt or removing a pin from a slot, and thereafter position in the seat 504 at a desired, or recommended, height. Furthermore, in various embodiments, an automated system, such as a motor, may be utilized to lower the seat 504 to the seat height 514. For example, the bicycle 500 may receive instructions from the user device and thereafter automatically adjust one or more components. Additionally, in

embodiments, the station supporting the bicycle 500 may receive the instructions and thereafter transfer the instructions to the bicycle 500. In this manner, the use parameters related to the bicycle 500 may be adjusted in order to provide more efficient use of the bicycle 500 and/or improve the comfort for the user.

[0052] FIG. 6 illustrates a diagram 600 of an example system implementation for providing resource use parameter predictions for a vehicle sharing environment, in accordance with various embodiments. Resource may refer to vehicles, docking spots, or any other such resources that may have an available state and an unavailable state. In various embodiments, a user device 602 may be used by a user to request and obtain a resource for use and receive associated

recommendations for use parameters associated with the resource. In an example, the user device is able to send and receive information, such as requests, calls, and data, across one or more networks 604 to a resource use parameter prediction system 606. This may include a request for different settings for one or more components associated with the vehicle, such as a seal or handle bars, in various embodiments. The user device 602 may receive, over the one or more networks 604, the requested resource use parameter prediction, amung umei imuimauuu. m some embodiments, the user device 602 may include any type of computing devices having network connectivity, including smart phones, tablets, smart watches, smart glasses, other wearables, personal computers, notebook computers, and the like. The one or more networks 604 can include any appropriate network, such as the Internet, a local area network (LAN), a cellular network, an Ethernet, Wi-Fi, Bluetooth, radiofrequency, or other such wired and/or wireless network. In some embodiments, a plurality of user devices 602 may access the resource use parameter prediction system through different types of networks. The resource use parameter prediction system 606 can include any appropriate resources for performing the various functions described herein, and may include various servers, data stores, and other such components known or used for providing content from across a network (or from the“cloud”).

[0053] In various embodiments, the resource use parameter prediction system 606 may include an interface 608, a prediction model 610, and a recommendation layer 612. The system 606 may also include a historical data database 614, and a biometric data database 616. Such modules and databases may be implemented jointly, separately, or in any combination on one or more devices, including physical devices, virtual devices, or both. Information may be passed between any of the modules and databases through the physical and/or virtual devices on which the modules and databases are implemented.

[0054] The interface layer 608 of the player matching system 606 may include a networking interface that can facilitate communication between the user device and the resource use parameter prediction system 606. Requests received by the resource use parameter prediction system 606 can be received through the interface layer 608. Example requests may include a request for a resource use parameter prediction for a user-selected time and location. The interface layer 608 may also provide outputs from the resource use parameter prediction system 606 to the user device, such as recommended use parameters for vehicles. The interface may also facilitate communication between the resource use parameter prediction system and individual vehicles or vehicle stations. For example data (e.g., utilization data) collected by individual vehicles or vehicle stations may be transmitted to the resource use parameter prediction system where it is received through the interface. In the illustrated example, a request is sent from the user device over the one or more networks and received at the nneiiace. m sume emuuuimeius, the request includes a base parameter (e.g., height, weight, etc.). The base parameters are input into the prediction model to determine a resource use parameter prediction for the queried conditions. In various embodiments, the model 610 may be trained on historical data stored in the historical data database 614.

[0055] The prediction model 610 may receive the base parameters 620 and determine a resource use parameter prediction 618. The prediction model 610 may include various types of models including machine learning models such as a neural network trained on the historical data. Other types of machine learning models may be used, such as decision tree models, associated rule models, neural networks including deep neural networks, inductive learning models, support vector machines, clustering models, regression models, Bayesian networks, genetic models, various other supervise or unsupervised machine learning techniques, among others. The prediction model 610 may include various other types of models, including various deterministic, nondeterministic, and probabilistic models. For example, the prediction model 610 includes one or more neural networks trained to determine a resource use parameter prediction for user based on biometric data associated with the user. As mentioned, the model may be trained on historical data 614 which may include, for example, a record of use parameters for users having similar biometric properties. Additionally, the historical data may also include biometric data 616, such as ergonometric charts or the like to predict proportions of human beings based on one or more biometric properties. The biometric data 616 may include data regarding the average leg length for a human being of a particular size, the average arm length for a human being of a particular size, and the like. As such factors may influence resource use parameters, the prediction model 610 may take into account this biometric data as well. In some embodiments, the historical data 614 and biometric data 616 may make up training data used to train the model. In certain such embodiments, the training data may include a large number of example input-output pairs. For example, a particular input-output pair may include as an input of a height, a weight, a gender, and various biometric data associated with the height, weight, and gender. The output may include the number of recommended settings to adjust components of the resources. Given a large enough number of such example input-output pairs, the model may be trained to estimate an output based on a certain input apecmcany, me muuei may estimate a resource use parameter prediction 618 given a certain condition 620 (e.g., height, weight, and biometric data).

[0056] The neural network may be a regression model or a classification model. In the case of a regression model, the output of the neural network is a value on a continuous range of values representing the use parameter prediction results. In the case of a classification model, the output of the neural network is a classification into one or more discrete classes. For example, the output representing the use parameter prediction may be classified as“bad”,“good”, or“great” with respect to comfort or efficiency related to the use parameters. The prediction model may output the estimated resource use parameter prediction, which may be transmitted to the user device via the interface. In some embodiments, the estimated use parameter may be presented in various forms, such as recommended settings for components of the vehicle, recommending a certain vehicle already tuned to the recommended settings, or the like.

[0057] In some embodiments, the use parameter prediction output from the model 610 is used in the recommendation layer 612, which generates a recommendation 622 for the user based on the use parameter prediction. For example, if the use parameter prediction for the base parameters 620 has a history of further adjustments, the recommendation 622 may include a range of use parameters (e.g., different settings for the user to try) to provide a range to allow the user to determine what settings are most comfortable. The recommendation 622 may be transmitted to the user device 602 via the interface 608 and the one or more networks 604.

[0058] FIG. 7 illustrates an example process 700 for training and using a neural network for predicting use parameters of resources in a vehicle-sharing environment, in accordance with various embodiments. It should be understood that, for any process discussed herein, there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments. In this example, historical data collected from a vehicle sharing system is obtained 702 and used as training data to train 704 a neural network or other machine learning model. The vehicle sharing system may include a plurality of resources and the historical data includes data regarding resource use parameter data for a plurality of users having different biometric properties, such as heights and weigms. m sume einuuuiineius me historical data also includes biometric data associated with a general population or a segment of a population. In some embodiments, the vehicle sharing system includes a docked bicycle sharing system comprising a plurality of docking stations and a plurality of bicycles, and the queried location is associated with one or more docking stations. In some embodiments, the vehicle sharing system includes dockless bicycles having geolocation, processing, and networking capabilities. In such embodiments, the dockless bicycles may be located and movable throughout a plurality of geographic regions, and the query location is associated with one or more of the regions.

[0059] The neural network is trained 704 using the historical data to predict vehicle use parameters based at least in part on biometric information associated with a user. Thus, after the neural network is trained, a query for a resource use parameter prediction may be received 706 from a user device. The query may include biometric properties of the user, such as height, weight, or gender. For example, the query may be generated when a user operating the user device inputs biometric properties into an application associated with the bike sharing system. The biometric properties of the user are processed 708 through the trained neural network, which determines 710 the use parameter prediction for the biometric properties of the user based on the historical data, the biometric data, or a combination thereof. In some embodiments, biometric data associated with at least a segment of a population is obtained and used in determining 710 the use parameter prediction. For example, users in a particular region may be statistically taller than users in other regions, and as a result, use parameters associated with a seat height may be higher in the particular region. Specifically, the historical data may include records of use parameters for other users in the particular region and used in determining the present use parameter prediction. A response may then be generated 712 based on the determined use parameter prediction and provided 714 to the user device.

[0060] FIG. 8 illustrates an example process 800 for predicting use parameters for vehicles in a vehicle-sharing environment, in accordance with various embodiments. In this example, a query for a resource use parameter prediction associated with a vehicle sharing system may be received 802, such as from a user device or generated based on a request from a user device. The query may include biometric properties of the user for the use parameiei pieuicuun rui example, the query may be generated when a user operating the user device determine they would like to check out a vehicle and thus requests a recommendation on the settings for certain components of the vehicle, such as a seat or handle bars. In some embodiments, the biometric properties may be determined based on a camera, scale, or the like located at a vehicle sharing docking station.

[0061] In various embodiments, the vehicle sharing system includes a plurality of vehicles of one or more types, an individual vehicle having either an available state or an unavailable state at a given time, and wherein the resource use parameter prediction includes a prediction of vehicle use parameters based on one or more biometric properties associated with the user. In some embodiments, the vehicle sharing system includes dockless bicycles having geolocation, processing, and networking capabilities. In such embodiments, the dockless bicycles may be located and movable throughout a plurality of geographic regions, and the query location is associated with one or more of the regions. In various embodiments, the vehicle sharing system includes a plurality of vehicle docking spots, in which an individual vehicle docking spot associated with one of a plurality of locations and having either an available state or an unavailable state at a given time.

[0062] The biometric properties submitted by the user are processed 806 through a neural network trained to determine use parameter predictions based at least in part on the biometric properties. Specifically, the neural network may be trained using historical data that includes use parameters for other users having a variety of biometric properties. In various embodiments, the users may be in a similar region or may be aggregation throughout the country or world. Thus, using the neural network trained on the historical data, a resource use parameter prediction can be determined 808 for the biometric properties. In some embodiments, the historical data includes biometric data as describe above, which may utilize charts or ergonomic properties to correlate biometric features for certain human beings. For example, biometric data may include heights, weights, proportions, and the like.

[0063] In some embodiments, biometric data associated with a segment of a population may be utilized to determine the use parameters. For example, as described above, certain regions may have different properties related to height, weight, and the like /veeuiumgiy, uunzmg uaia iium a region with different biometric properties may inadvertently/unintentionally skew results. In various embodiments, biometric data may be assembled from ergonomic or other tables that provide information related to proportionality of human beings based on different factors. That is, different charts or tables may provide information that correlates a height of a person to an average leg length, which may be useful when determining the proper seat height, for example. Furthermore, these charts or tables may further be utilized to determine other components of the vehicles, thereby increasing the user experience by reducing the duration of time to adjust the vehicle and also improving the comfort associated with using the vehicle.

[0064] In various embodiments, a weight may be applied to one or more parameters to enhance the effect of that parameter within the model. For example, a location of the vehicles may be determined and ergonomic information, associated with the above-described biometric data, may be weighted for information corresponding to that region. In other words, ergonomic data associated with the region may be given more importance or impact on the model than ergonomic data for different regions. As such, predictions for users in one city may be different than predictions for users in a second city. Furthermore, historical data may be given greater weights due to previous successful predictions.

[0065] In certain embodiments, biometric information of the users may be automatically obtained as the user approaches the vehicle sharing station. For instance, a photograph of the user may be taken and imagine processing software may be utilized to segment or otherwise identify the legs, arms, torso, and the like. Accordingly, this information, which may be part of the biometric data described above, may be utilized to prepare the use parameter predictions.

For example, if the length of the user’s legs were analyzed and determined, the seat height may be predicted based at least in part on the leg length. In this manner, the user’s interaction with the application may be limited to making a request to check out or use the vehicle, while information regarding the biometric properties may be automatically obtained.

[0066] Information may then be generated 810 based on the determined use parameters prediction and provided 812 to the user device to be presented to the user. In some embodiments, the information includes recommended settings for one or moie cumpuneins ui me vemcie, suen as a seat height or a handle bar position. This information may then be utilized by the user to make the appropriate adjustments. In various embodiments, the adjustments may be

automatically applied to the vehicle, for example, via one or more adjustment mechanisms described above. The information may also include identification as to which vehicle the user should select, which may be based in part on reducing the number of adjustments made to the vehicle. For example, the system may recommend a vehicle where the user would need to make the fewest number of adjustments, compared to other available vehicles. As such, the user experience is improved by reducing the work the user does before utilizing the shared vehicle resource.

[0067] In various embodiments, feedback is requested 814, from the user regarding how the use parameters feel for the user. For example, the user device may prompt the user to answer one or more questions related to the use parameters. These questions may be related to efficiency, comfort, ease of adjustment, and the like. If the user is satisfied with the settings, then the process may stop 816. If the user is not satisfied, the process may request which parameter is not satisfactory 818. For example, the process may go through each component and request feedback. If the given parameter is satisfactory, the feedback may be logged. If the given parameter is unsatisfactory, the feedback may be utilized to update the model and process the biometric properties based on the new information obtained. Thus, the recommended use parameters may be constantly updated as new information is obtained from new users utilizing the system.

[0068] FIG. 9 illustrates a set of basic components of one or more devices 900 of the present disclosure, such as the user devices, bike station, and bicycles. In this example, the device includes at least one processor 902 for executing instructions that can be stored in a memory device or element 904. As would be apparent to one of ordinary skill in the art, the device can include many types of memory, data storage or computer-readable media, such as a first data storage for program instructions for execution by the at least one processor 902, the same or separate storage can be used for images or data, a removable memory can be available for sharing information with other devices, and any number of communication approaches can be available for sharing with other devices. The device typically wm mciuue ai ieasi une iype ui display element 906, such as a touch screen, electronic ink (e-ink), organic light emitting diode (OLED) or liquid crystal display (LCD), although the devices may output information via other means, such as through audio speakers. The device can include at least one communication component 1008 that may enable wired and/or wireless communication of voice and/or data signals, for example, over a network such as the Internet, a cellular network, a Wi-Fi network, BLUETOOTH®, and the like. The device can include at least one additional input device 910 able to receive conventional input from a user. This conventional input can include, for example, a push button, touch pad, touch screen, camera, microphone, keypad, scanner, detector, or any other such device or element whereby a user can input a command to the device. These EO devices could even be connected by a wireless infrared or Bluetooth or other link as well in some embodiments. In some embodiments, however, such a device might not include any buttons at all and might be controlled only through a combination of visual and audio commands such that a user can control the device without having to be in contact with the device. As discussed, different approaches can be implemented in various environments in accordance with the described embodiments.

[0069] The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the claims.

Claims

WHAT IS CLAIMED IS:

1. A system, comprising:

at least one computing device processor; and

a memory device including instructions that, when executed by the at least one computing device processor, cause the system to:

obtain historical data collected from a vehicle sharing system, the vehicle sharing system comprising a plurality of resources connected over a network, and the historical data including biometric properties of users of the plurality of resources;

train a neural network, using at least the historical data as training data, to generate a trained neural network to output use parameters of the plurality of resources for the segment;

receive at least one biometric property of the user;

process the at least one biometric property using the trained model to determine the use setting prediction;

provide the use setting prediction to at least one of a user or a resource of the plurality of resources, the use setting prediction being utilized by at least one of the user or the resource of the plurality of resources to adjust one or more components of the resource.

2. The system of claim 1, wherein the instructions when executed further cause the system to:

request feedback from the user, via a user device, corresponding to an accuracy of the use setting prediction; and

update the trained neural network based on the feedback.

3. The system of claim 1, wherein the vehicle sharing system includes a docked bicycle sharing system comprising a plurality of docking stations and a plurality of bicycles, wherein the at least one biometric property of the user is obtained via the plurality of docking stations.

4. The system of claim 1, wherein the insuucuuns wiien execuieu iuiuiei cause the system to:

obtain biometric data associated with a segment of a population, the biometric data including at least one of a height, a weight, or a proportion of users in the segment, wherein the biometric data further comprises ergonomic tables including at least proportions of human bodies related to at least one of height or weight.

5. A computer-implemented method, comprising:

receiving a query for a use setting prediction associated with a vehicle sharing system, the vehicle sharing system comprising a plurality resources, and the query including at least one biometric property of a user;

processing the at least one biometric property through a model, the model trained using at least historical data including biometric properties of users of the plurality of resources;

determining the use setting prediction based at least in part on the at least one biometric property;

determining information to provide to a user in response to the query; and providing the information to a user device for the user.

6. The computer-implemented method of claim 5, the method further comprising:

adjusting one or more components of the resource based on the information, the adjustment physically changing a position of the one or more components.

7. The computer-implemented method of claim 6, wherein the adjustment is manually conducted by the user.

8. The computer-implemented method of claim 5, wherein the adjustment is automatically performed by an adjustment mechanism.

9. The computer-implemented method of ciaim J, wneiem me piuiaiuy ui resources includes a plurality of vehicle docking spots, an individual vehicle docking spot associated with one of the resources, the method further comprising:

selecting a resource of the of the plurality of resources based at least in part on a number of adjustments recommended by the information, wherein the resource having the fewest number of adjustments is selected.

10. The computer-implemented method of claim 5, wherein the use setting prediction includes an identifier corresponding to a physical location of one or more components associated with the resources.

11. The computer-implemented method of claim 5, further comprising:

requesting feedback from the user corresponding to an accuracy of the use setting prediction; and

updating the model based on the feedback.

12. The computer-implemented method of claim 5, further comprising:

training the model using at least biometric data including biometric properties of a segment of a population, wherein the biometric data comprises ergonomic information for a subset of a population;

determining a location of the plurality of resources; and

applying a weight to the biometric data, the weight increasing an impact of ergonomic data corresponding to the subset of the population corresponding to the determined location.

13. The computer-implemented method of claim 5, further comprising:

determining the user is approaching the plurality of resources;

evaluating the user using a camera proximate the plurality of resources; and determining the at least one biometric property m me usei via me camel a, me ai least one biometric property of the user calculated based at least in part on a proportion of one area of the user’s body relative to another.

14. The computer-implemented method of claim 5, further comprising: prompting the user to enter the at least one biometric property; and correlating the at least one biometric property with a user account, the user account granting the user permission to access the plurality of resources.

15. The computer-implemented method of claim 5, further comprising: storing the use settings for the user; and

displaying the use settings to the user a next time the user attempts to access the plurality of resources.