Predictive transportation scheduling and related devices, systems, and methods

Abstract

A method for automatically scheduling transportation includes, among other things, extracting user data from one or more of a plurality of applications running on a user device with one or more processors and storing the user data in one or more memories in communication with the one or more processors. The plurality of applications includes a calendar application and a transportation application, and the user data includes event data from the calendar application. The method also includes predicting a travel event based on the event data and booking transportation for the travel event through the transportation application. A system includes a data integration module that extracts user data including event data, a predictive analytics module that predicts a travel event based on the event data, and a transport booking module that books transportation for the travel event.

Description

[0001] PREDICTIVE TRANSPORTATION SCHEDULING AND RELATED DEVICES, SYSTEMS, AND METHODS

[0002] CROSS-REFERENCE TO RELATED APPLICATION(S)

[0003]

[0001] This application claims the benefit under 35 U.S.C. § 119(e) to U.S. Provisional Application 63 / 595,175, filed November 1 , 2023, and entitled “PredictiveTransportation Schedulingand Related Devices, Systems, and Methods,” which is hereby incorporated herein by reference in its entirety.

[0004] TECHNICAL FIELD

[0005]

[0002] The disclosure generally relates to predictive transportation scheduling.

[0006] BACKGROUND

[0007]

[0003] Known methodsforschedulingtransportation require users to manually inputtransportation requests, such as ride-sharing bookings, which can be time-consuming and often done last-minute, leading to inconveniences such as longer wait times or missed appointments.

[0008] BRIEF SUMMARY

[0009]

[0004] Example 1 relates to a method for automatically scheduling transportation, comprising: extracting user data from one or more of a plurality of applications running on a user device with one or more processors and storing the user data in one or more memories in communication with the one or more processors, the plurality of applications comprising a calendar application and a transportation application, and the user data comprising event data from the calendar application; predicting a travel event based on the event data with the one or more processors; and booking transportation for the travel event through the transportation application with the one or more processors.

[0010]

[0005] Example 2 relates to Examples 1 and 3-12, wherein the plurality of applications comprises a messaging application, and further comprising sending a notification about the booked transportation through the messaging application with the one or more processors.

[0011]

[0006] Example 3 relates to Examples 1 -2 and 4-12, wherein the notification requests verification of the booked transportation.

[0007] Example 4 relates to Examples 1-3 and 5-12, further comprising receiving and storing user preferences in the one or more memories with the one or more processors, the user preferences comprising one or more of a preferred transportation provider, a preferred route, and a preferred time.

[0012]

[0008] Example 5 relates to Examples 1 -4 and 6-12, further comprising the one or more processors communicating with the plurality of applications through a plurality of corresponding APIs.

[0013]

[0009] Example 6 relates to Examples 1-5 and 7-12, wherein the event data comprises past travel data comprising at least one of a location, a date and / or time, and a transportation provider.

[0014]

[0010] Example 7 relates to Examples 1 -6 and 8-12, wherein predicting the travel event comprises analyzing the user data to determine a time and location for the travel event.

[0015]

[0011] Example 8 relates to Examples 1 -7 and 9-12, wherein the time and location are based on a series of past events.

[0016]

[0012] Example 9 relates to Examples 1 -8 and 10-12, wherein the time and location are based on location data.

[0017]

[0013] Example 10 relates to Examples 1-9 and 11-12, further comprising analyzing the user data with a machine learning system and / or an artificial intelligence system.

[0018]

[0014] Example 11 relates to Examples 1-10 and 12, wherein booking the transportation comprises booking a plurality of transportations.

[0019]

[0015] Example 12 relates to Examples 1 -11 , wherein the plurality of transportations comprise a plurality of transportation modes.

[0020]

[0016] Example 13 relates to a system for automatically scheduling transportation, comprising: one or more processors in communication with one or more memories storing computer-executable instructions to configure the one or more processors to implement a plurality of system modules, comprising: a data integration module configured to extract user data from one or more of a plurality of applications running on a user device, the plurality of applications comprising a calendar application and a transportation application, and the user data comprising event data from the calendar application; a predictive analytics module configured to predict a travel event based on the event data; and a transport booking module configured to book transportation for the travel event through the transportation application.

[0021]

[0017] Example 14 relates to Examples 13 and 15-18, wherein the plurality of system modules further comprises a notification system configured to send a notification about the booked transportation through the messaging application.

[0018] Example 15 relates to Examples 13-14 and 16-18, wherein the notification requests verification of the booked transportation.

[0022]

[0019] Example 16 relates to Examples 13-15 and 17-18, wherein the event data comprises recurring events.

[0023]

[0020] Example 17 relates to Examples 13-16 and 18, wherein the plurality of system modules further comprises a graphical user interface and a user settings module configured to receive and store user preferences from the graphical user interface, the user preferences comprising a preferred transportation provider.

[0024]

[0021] Example 18 relates to Examples 13-17, wherein the transport booking module is configured to book a plurality of transportations for the travel event.

[0025]

[0022] Example 19 relates to a non-transitory computer readable medium comprising instructions that when executed by one or more processors cause the one or more processors to: extract user data from one or more of a plurality of applications running on a user device, the plurality of applications comprising a calendar application and a transportation application, and the user data comprising event data from the calendar application; predict a travel event based on the event data; book transportation for the travel event through the transportation application; and send a notification about the booked transportation through the messaging application.

[0026]

[0023] Example 20 relates to Example 19, wherein the notification requests verification of the booked transportation.

[0027]

[0024] While multiple implementations and aspects are disclosed, still other embodiments of the disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the disclosed apparatus, systems and methods. As will be realized, the disclosed apparatus, systems and methods are capable of modifications in various obvious aspects, all without departing from the spirit and scope of the disclosure. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.

[0028] BRIEF DESCRIPTION OF THE DRAWINGS

[0029]

[0025] FIG. 1 is a block diagram of a system for scheduling transportation according to an implementation.

[0030]

[0026] FIG. 2 is a graphical representation of a transportation scheduling system according to an implementation.

[0027] FIG. 3 is a process diagram illustrating a method for scheduling transportation according to an implementation.

[0031]

[0028] FIG. 4 is a block diagram of a transportation scheduling system according to an implementation.

[0032] DETAILED DESCRIPTION

[0033]

[0029] Disclosed herein is a system and related devices and methods for predictive scheduling and execution of transportation solutions. That is, the various devices, systems, and methods are configured for proactively predicting and scheduling transportation needs based on a calendar of events and / or past transportation behaviors. In various cases the predicting and scheduling is discussed herein as predicting travel events and booking transportation for the predicted travel events.

[0034]

[0030] As would be understood, currently users have to manually view their calendar or rely on their memory and plan transportation, including, optionally manually inputting requests for ride-share bookings, purchasingfares, and other scheduled transportation. This can be time-consuming and is proneto usererror. Furtherthis known type of scheduling is prone to procrastination and last-minute scheduling which can lead to inconveniences such as long wait times, missed appointments, increased costs, and / or longer / less ideal routing.

[0035]

[0031] In various implementations disclosed herein, a user’s calendar(s) are integrated into the system such that the system is able to execute an algorithm or other process to anticipate transportation needs based on events in the calendar(s) and past behaviors. In further implementations, the system is able to automatically or semi-automatically schedule needed transportation. Optionally the system mayaccountfor user preferences such as preferred providers, preferred modes of transportation, preferred routes / timing, etc. Further, the system may issue notifications to a user, for example alerting a user to confirm the need for transportation, alerting a user of the scheduled or booked transportation for confirmation, altering a user of the upcoming transportation, etc.

[0036]

[0032] As would be understood, the system, devices, and methods disclosed herein anticipate transportation needs, schedule transportation automatically, and can notify the user of the scheduled transportation. The system, devices, and methods improve convenience and reduce the likelihood of transportation related stresses and / or oversights and streamline a user’s daily planning and activities.

[0033] Turning to the figures in further detail, FIG. 1 is a functional block diagram of a transportation scheduling system 100 according to various examples. The system 100 includes one or more computing devices that implement a number of functional modules in the form of, e.g., software systems and routines. In the depicted example, the system includes a first computing device 10, such as a server, and a second computing device 60, such as a user device. The system 100 can incorporate one, two, or more computing devices according to various implementations, as will be discussed.

[0037]

[0034] As shown in FIG. 1 , the system 100 includes a data integration module 20. The data integration module 20 is configured to extract user data from one or more applications running on the user device 60. In various implementations, the data integration module 20 interfaces with one or more digital calendar 62 or digital calendar applications 62, or any other application that may include calendar functionality, as would be understood. The data integration module 20 is configured to extract and synchronize user data that includes event data, such as data about a user’s upcoming events or past events. In various cases, the data integration module 20 is configured to extract other types of user data including, for example, user preferences and / or settings, past transportation behaviors, and location data. As would be understood the data integration module 20 may interface with the calendar applications 62 via one or more APIs 22. Additional APIs may be used to interact with other applications running on the user device.

[0038]

[0035] In various cases the data integration module 20 may be configured to analyze preferred transportation providers such as ride share providers (e.g. Uber, Lyft, etc.), local public transportation, regional / national train systems, airlines, taxi companies, on demand private transportation, ride hailing, digital dispatch, on demand private transportation, vehicle for hire, and / or other transportation avenues (e.g. black car companies, local / regional buses, etc.). The data integration module 20 may also be configured to determine and analyze past transportation behaviors to determine a preferred method of getting to and from a particular location or event. For example, a user may prefer to use a ride share when commuting to and from a local concert but may prefer to use a regional train system when traveling to a nearby city. Past transportation behaviors may also include repetitive tasks such as a daily commute, a weekly or biweekly trip to a grocery store, a monthly visit to a family member, and the like.

[0039]

[0036] The system 100 further includes a predictive analytics module 50. The predictive analytics module 50 is configured to analyze the extracted user data and predict a user’s transportation needs, such as when and where they will need transportation. In various cases this functionality is referred to herein as predicting a travel event. The predictive analytics module 50 may interface with or be in communication with the data integration module 20 and a transport booking module 30. The predictive analytics module 50 optionally includes a machine learning algorithm 52 and / or an artificial intelligence system 54 that assists in analyzing the user data and predicting travel events.

[0037] In various implementations, the predictive analytics module 50 is configured to predicttravel events and other transportation needs not only from scheduled events in a calendar application or other application with calendar-like functionality but also from past transportation behaviors. Past transportation behaviors may include, among other things, a daily commute, a weekly or biweekly trip to a grocery store, a monthly visit to a family member, and the like. For example, the predictive analytics module 50 can be configured to analyze user data such as past event data, past travel data, and / or location history data extracted from, respectively, a calendar application, a transportation application, and a location monitoring application. Such functionality may be provided in addition to or instead of the past transportation behavior analysis by the integration module 20 discussed previously. In various implementations, the predictive analytics module 50 is able to predict the need for transportation for future travel (e.g., a travel event) mirroring past transportation behaviors with or without an affirmative calendar entry for the event.

[0040]

[0038] The system 100 also includes a transport booking module 30 according to various implementations. The transport booking module 30 is configured to automatically schedule transportation based on the predictions made by the predictive analytics module 50. In various implementations the booking transport module 30 books transportation through one or more transportation applications 64 running on the user device 60. Such functionality may be referred to herein as booking transportation for one or more predicted travel events.

[0041]

[0039] In various implementations, the transport booking module 30 is configured to book end-to- end / round-trip transportation across various transportation providers / modes of transportation. For example, the system 100 may book transportation via a ride share to a departure airport, an airline from a departure airport to arrival airport, and a local bus from the arrival airport to an accommodation. The system 100 may further be able to align travel times with sufficient layovers / time between modes fortraversing locations as would be understood. The system 100 may account for user preferences for layover length / time between modes of transportation. The transport booking module 30 may include an API 32 for communicating with various transportation applications 64, such as, but not limited to, ride share applications 64, airline applications, and others.

[0040] Continuing with FIG. 1 , the system 100 may also include a notification system / module 40. The notification system 40 is configured to notify users about the scheduled or booked transportation, changes to upcoming transportation, reminders about upcoming transportation, confirmations of scheduled transportation, confirmations of transportation needs, and the like. In various cases the notification system 40 is configured to send or push a notification or message about the booked transportation through a messaging application or another application with messaging or other notification capabilities. In various cases the notification may request verification of the booked transportation.

[0042]

[0041] In various cases, the notification system 40 may interface with one or more messaging applications 66 via one or more APIs 42. In the depicted example, the messaging applications 66 run on the user device 60, though in various cases one or more messaging applications could be run on the server 10 or another computing device or web service. According to various implementations, the messaging applications 66 may include an email application, SMS application, in-app notification system, or the like, as would be appreciated.

[0043]

[0042] The system 100 optionally includes a privacy management module 70. The privacy management module 70 is configured to secure user data and protect user privacy.

[0044]

[0043] According to various implementations, the system 100 may include the user device or cloud interface 60 with an optional graphical user interface (GUI) 68. The GUI may be generated locally on the user device or remotely. For example, in some cases the GUI 68 may be web or mobile based. According to various implementations, the GUI 68 is configured to allow users to set preferences, view booked transportation and make manual adjustments to transportation needs. In some cases the system 100 further includes an optional user settings module 80 that receives and stores user preferences and other settings. Such an optional user settings module 80 may be located locally on the user device 60 or on the server 10 or other remote computing device 10.

[0045]

[0044] In various implementations, the user device 60 may be implemented as a mobile phone and the GUI 68 may, for example, be generated by an application on the mobile phone. In such cases, the data integration module 20, predictive analytics module 50, transport booking module 30, and / or notification system 40 may be local to the user device 60 or remote such as on the cloud, on a remote computing device 10 as shown in FIG. 1 , or any combination thereof. Optionally, the GUI 28 may include voice-activation.

[0046]

[0045] Accordingly, the functionality of the system 100 can be distributed among one or more computing devices in various implementations. As shown in FIG. 1 , the system 100 is implemented with a computing device 10, such as a server, that communicates with a user device 60, which in some cases may be a web orcloud-based interface. Various implementations of the system 100 can include the computing device or server 10, the user device / interface 60, both, or a combination of one or both with one or more additional computing devices.

[0047]

[0046] According to various implementations, communication between the computing device 10 and the user device / interface 60 can occur over any suitable wired and / or wireless connection, as would be understood. Although not shown in FIG. 1 , in various implementations the functionality of the computer server 10 and the user device 60 may be provided as a local installation on a single hardware device. For example, the functionality of the system 10 may in some cases be provided in whole or part by one or more applications running on the user device 60.

[0048]

[0047] FIG. 2 depicts one example of a possible hardware implementation of the transportation scheduling system 100 that includes a server 10 in communication with a database 12 and a number of user devices 60 over a network 200, such as the Internet. The user devices 60 can be implemented with various combinations of hardware, software, and / or firmware, as would be appreciated. As examples, FIG. 2 illustrates possible user devices implemented as a smart phone, a laptop computer, and a desktop computer. In various cases the user device may take the form of a web interface generated by a remote computer system.

[0049]

[0048] According to various implementations, the disclosed technology provides one or more methods for automatically scheduling transportation. Such methods can include steps such as extracting user data from one or more applications running on a user device, predicting a travel event based on event data forming at least part of the user data, and booking transportation for the travel event. In various cases the transportation is booked through one or more transportation applications running on the user device. In some cases a method further includes sending a notification about the booked transportation and optionally requesting verification of the booked transportation.

[0050]

[0049] FIG. 3 is a process diagram that illustrates a method for scheduling transportation that the system 100 is configured to execute according to various implementations. The method includes a series of steps that make up a method for predicting and automatically scheduling transportation according to various implementations. Each step may be optionally performed in any order or not at all. Optionally the steps may be performed sequentially, iteratively, or simultaneously in various cases.

[0051]

[0050] In a first optional step, a user installs / initializes (box 302) a predictive transportation scheduling system or application according to an implementation of the disclosed technology. The user grants permissions for the application to access calendar applications 62 and transportation accounts / applications 64. The system 100 may also request access to the user’s location data.

[0052]

[0051] In a further optional step, the system 100, optionally via the data integration module 20, performs data extractions (box 304), extracting and monitoring user data including, for example, event data from a user’s calendar applications 62. In various cases the event data is indicative of new events and changes to existing events. The system 100 may also optionally monitor a user’s location changes to determine repetitive travel and travel behaviors.

[0053]

[0052] In a still further optional step, the system 100 via the predictive analytics module 50 predicts transportation needs (box 306), in the form of one or more travel events, by analyzing the user data from the data integration module 20. In various cases predicting transportation needs, such as one or more travel events, takes into account event timings, locations, user preferences, predicted traffic patterns, simultaneously occurring events near the transportation route, weather, and other data points relevant to transportation.

[0054]

[0053] In another optional step, the system 100 via the transport booking module 30 books (box 308) the transportation for the user. In various cases the booking (308) taking into account event timing, location, pickup and drop-off timing, and other relevant factors including possible walking needs.

[0055]

[0054] In a further optional step, the system 100 via the notification system 40 sends notifications (box 310) to a user about the booked transportation, confirmations of transportation booking and needs, changes to transportation bookings, recommendations for transportations, and the like. Notifications may be sent (box 310) via any known or developed notification method including SMS, email, application push notification, automated phone call, and the like, as would be understood.

[0056]

[0055] In another optional step, the user interacts (box 312) with the system 100 application to view details of scheduled transportation, make changes, set preferences, override automatic bookings, and the like.

[0057]

[0056] As would be appreciated in light of this disclosure, the system 100 provides integrated predictive analysis. Traditional systems rely on manual input and simple notifications about upcoming events to cause a user to book their own transportation. In various cases the disclosed system 100 integrates a machine learning module 52 and / or an artificial intelligence module 54 to analyze data from various applications, such as calendars, to assess user habits to predict transportation needs in advance and book transportation solutions as necessary.

[0057] The system 100 also optionally provides calendar synchronization. In such cases the system 100 integrates with calendar applications already in use by a user to extract, update, and analyze data about upcoming events ensuring real-time responsiveness to changes in event schedules.

[0058]

[0058] As previously discussed, the system 100 also provides automatic transportation booking. Instead of requiring a user to manually input booking needs and manually select transportation, the system 100 predicts the transportation need and automatically books the needed transportation. Optionally, the system 100 books the transportation without requiring user input. Alternatively, in various cases the system 100 may ask the user for confirmation or verification prior to (or after) confirming the booking. This can be advantageous for routine events or during busy scheduling times.

[0059]

[0059] By automating transportation booking, the system 100 may reduce the number of times a user needs to manually book transportation and reduce the number of occurrences of not having transportation when it is needed.

[0060]

[0060] Further, the system 100 can improve the user experience by adapting to user behaviors and preferences. In some cases machine learning algorithms / Al allow for improved user experience overtime as the system 100 learns about user preferences and behaviors. By learning over time, the system 100 reduces or eliminates unnecessary and / or incorrectly timed transportation bookings leading to cost savings and better user experiences.

[0061]

[0061] In various cases a notification module is integrated into the system 100 to merge notifications for events and transportation. This can reduce the number of notifications to be received by the users streamlining user experience and reducing cognitive load.

[0062]

[0062] In various cases the system 100 also optionally includes privacy management to ensure user data is used properly and responsibly with user consent.

[0063]

[0063] FIG. 4 is a simplified block diagram illustrating a hardware implementation of the transportation scheduling system 100 according to various implementations. The system 100 is implemented by one or more computing devices that provide the system modules, processes and other functionality described elsewhere herein. Such computing devices may communicate over different types of suitable communication networks and protocols, as would be appreciated. As shown in FIG. 4, the system 100 includes a first computing device 10, such as a remote web or application server, which in some cases may be connected to a database 12 containing computer memory. The system 100 also includes a second computing device in the form of a user device 60. In some cases the second computing device may instead be a user interface provided over a network, such as the Internet, for interacting with the system 100.

[0064]

[0064] The computing devices 10, 60 shown in FIG. 4, as well as any additional computing devices incorporated into the system 100, can include various combinations of hardware, software, and / or firmware configured to perform operations or actions that implement the various system modules. Respectively, the computing devices 10, 60 include a processor or processing circuitry 400, 410 in communication with a computer memory 402, 212, an input / output (I / O) module 406, 416, and a networking module 402, 414. It would be appreciated that the computing devices are depicted in a simplified manner to highlight various aspects and that the devices include other known components and circuits as needed.

[0065]

[0065] Accordingto various implementations, one or more of the processors is a Central Processing Unit (CPU), although other processors such as, but not limited to, microprocessors, microcontrollers, Field Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs) are possible. In various cases the processor includes or is coupled with one or more physical, non-transitory computer accessible or readable storage devices 402, 412, which are also referred to herein as “memory” and “memory devices.” The memories 402, 412 may be implemented using any suitable memory technology, which may include, e.g., temporary and more long-term configurations, volatile and non-volatile configurations, and solid state and / or other physical formats. Examples of possible memory include random access memory (RAM), dynamic randomaccess memory (DRAM), static random-access memory (SRAM), magnetic hard discs, optical discs, floppy discs, flash memory, forms of electrically programmable memory (EPROM) and electrically erasable and programmable (EEPROM) memory, and other forms known in the art.

[0066]

[0066] As would be understood, the memory or memories coupled with each processor contain instructions for configuring the processor to perform particular operations or actions by virtue of loading and executing the instructions. The processors 400, 410 carry out the instructions in order to provide the functionality of the various system modules, as well as applications running on the system. Accordingly, references herein to the processor, processing circuits and / or the system 100 carrying out various activities imply that the processor is configured with corresponding instructions for execution.

[0067]

[0067] In various cases one or more of the computing devices includes an I / O system that enables interaction with the computing devices. The I / O system(s) 406, 416 can include, for example, a monitor or other screen device and an input device, such as a keyboard, a mouse, a touchpad, or any other such known input device. Further, the computing devices include respective networking systems 404, 414 that enable the devices to communicate with one another and / or with other external computing devices providing various functionality. For example, the networking systems may implement a software interface or communication protocol and may also provide one or more physical interfaces, including transmitters, receivers, cable connectors, antennas, and other known components that enable the computing devices to communicate.

[0068]

[0068] The computing devices 10, 60, and the system 100 as a whole, can include various additional known hardware, software, and / or firmware components as would be appreciated.

[0069]

[0069] Although the disclosure has been described with reference to certain implementations and embodiments, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the disclosed apparatus, systems and methods.

Claims

CLAIMSWhat is claimed is:1 . A method for automatically scheduling transportation, comprising: extracting user data from one or more of a plurality of applications running on a user device with one or more processors and storing the user data in one or more memories in communication with the one or more processors, the plurality of applications comprising a calendar application and a transportation application, and the user data comprising event data from the calendar application; predicting a travel event based on the event data with the one or more processors; and booking transportation for the travel event through the transportation application with the one or more processors.

2. The method of claim 1 , wherein the plurality of applications comprises a messaging application, and further comprising sending a notification about the booked transportation through the messaging application with the one or more processors.

3. The method of claim 2, wherein the notification requests verification of the booked transportation.

4. The method of claim 1 , further comprising receiving and storing user preferences in the one or more memories with the one or more processors, the user preferences comprising one or more of a preferred transportation provider, a preferred route, and a preferred time.

5. The method of claim 1 , further comprising the one or more processors communicating with the plurality of applications through a plurality of corresponding APIs.

6. The method of claim 1 , wherein the event data comprises past travel data comprising at least one of a location, a date and / or time, and a transportation provider.

7. The method of claim 1 , wherein predicting the travel event comprises analyzing the user data to determine a time and location for the travel event.

8. The method of claim 7, wherein the time and location are based on a series of past events.

9. The method of claim 7, wherein the time and location are based on location data.

10. The method of claim 7, further comprising analyzing the user data with a machine learning system and / or an artificial intelligence system.11 . The method of claim 1 , wherein booking the transportation comprises booking a plurality of transportations.

12. The method of claim 11 , wherein the plurality of transportations comprise a plurality of transportation modes.

13. A system for automatically scheduling transportation, comprising: one or more processors in communication with one or more memories storing computerexecutable instructions to configure the one or more processors to implement a plurality of system modules, comprising: a data integration module configured to extract user data from one or more of a plurality of applications running on a user device, the plurality of applications comprising a calendar application and a transportation application, and the user data comprising event data from the calendar application; a predictive analytics module configured to predict a travel event based on the event data; and a transport booking module configured to book transportation for the travel event through the transportation application.

14. The system of claim 13, wherein the plurality of system modules further comprises a notification system configured to send a notification about the booked transportation through the messaging application.

15. The system of claim 14, wherein the notification requests verification of the booked transportation.

16. The system of claim 13, wherein the event data comprises recurring events.

17. The system of claim 13, wherein the plurality of system modules further comprises a graphical user interface and a user settings module configured to receive and store user preferences from the graphical user interface, the user preferences comprising a preferred transportation provider.

18. The system of claim 13, wherein the transport booking module is configured to book a plurality of transportations for the travel event.

19. A non-transitory computer readable medium comprising instructions that when executed by one or more processors cause the one or more processors to: extract user data from one or more of a plurality of applications running on a user device, the plurality of applications comprising a calendar application and a transportation application, and the user data comprising event data from the calendar application; predict a travel event based on the event data; book transportation for the travel event through the transportation application; and send a notification about the booked transportation through the messaging application.

20. The non-transitory computer readable medium of claim 19, wherein the notification requests verification of the booked transportation.

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