Systems and methods for generating transportation recommendations for improved efficiency - Patents.com

Abstract

The present technology relates to a system and method for determining one or more alternative transportation modes for a user. The alternative transportation modes may improve the value of a transportation metric compared to the value of the same transportation metric of the user's current transportation mode. The transportation metric may be determined based on the user's current transportation mode and travel history. The travel metric may include total travel distance, time spent traveling, costs associated with the travel, carbon footprint of the travel, parking options, availability of specific fuel stops, vehicle maintenance, etc. The value of the transportation metric associated with the alternative transportation mode may be determined and compared to the value of the transportation metric of the user's current transportation mode.

Description

[Background technology]

[0001] When people travel in their daily lives, they may not understand the impact of such travel, such as fuel costs, quantifying harmful emissions to the environment, etc. People often struggle with transportation decisions, such as what type of vehicle to purchase or the best way to get to a particular destination, because it is very difficult to consider all the different factors. Thus, while users may want to choose environmentally friendly or cost-effective transportation options, they are often unable to do so effectively. Summary of the Invention [Means for solving the problem]

[0002] The present disclosure generally relates to a system and method for generating adapted recommendations for more efficient transportation based on an individual's travel patterns and requirements. The adapted recommendations may suggest alternative transportation modes compared to one or more current transportation modes. The adapted recommendations may improve the value of a transportation metric compared to the value of the same transportation metric of the user's current transportation mode. For example, the adapted recommendations may make suggestions to improve monetary, time, or environmental costs, such as the carbon footprint of the user, with more environmentally friendly transportation options.

[0003] One aspect of the disclosure relates to a method, which may include, by one or more processors, identifying at least one first transportation mode of a user's transportation; determining travel information including a trip start point and one or more destination locations; determining a value of a transportation metric for the at least one first transportation mode based on the travel information and the at least one first transportation mode; determining at least one alternative transportation mode based on the travel information; determining a value of a transportation metric for the at least one alternative transportation mode based on the travel information and the at least one alternative transportation mode; comparing a value of the transportation metric associated with the at least one alternative transportation mode to a value of the transportation metric of the at least one first transportation mode; and providing for output an indication to identify the at least one alternative transportation mode, wherein a value of the transportation metric associated with the at least one alternative transportation mode is improved compared to a value of the transportation metric associated with the at least one first transportation mode.

[0004] In some embodiments of the present disclosure, the at least one first transportation mode and at least one of the at least one alternative transportation modes may be different from each other. Further, in some examples, the transportation metric may include at least one of a total distance traveled, a total travel time, a carbon footprint, a driving cost, or vehicle operation data. In some embodiments of the present disclosure, the vehicle operation data may include a fuel type of the vehicle, a fuel usage of the vehicle, an estimated maintenance of the vehicle, and a government incentive for the vehicle. In some embodiments of the present disclosure, determining the transportation metric may be further based on toll information, parking options, and refueling options.

[0005] In some aspects of the disclosure, the at least one alternative transportation mode may include a plurality of alternative transportation modes, and the method further includes ranking the plurality of alternative transportation modes based on values ​​of a transportation metric associated with each of the alternative transportation modes, where the ranking of the plurality of alternative transportation modes is based on a relatively low cost to the user to a highest cost to the user. The method may further include outputting the ranked plurality of alternative transportation modes.

[0006] Some aspects of the present disclosure relate to a method, which may further include providing at least one of the value of the transportation metric of the first transportation mode and the value of the transportation metric of the at least one alternative transportation mode for output. In some aspects of the present disclosure, receiving location information corresponding to the user's travel history may further include receiving the location information on at least one of a memory device of the user device, one or more servers, or a navigation application. In some aspects of the present disclosure, determining the one or more travel information may include determining a pattern or frequency of the plurality of trips based on the location information, and identifying one or more daily trips based on the determined pattern or frequency of the plurality of trips. In some aspects of the present disclosure, when identifying one or more daily trips based on the frequency of the plurality of trips, the method may further include comparing the frequency of each of the plurality of trips to a threshold frequency. Furthermore, when the frequency of each of the trips is higher than the threshold frequency, each of the trips may be identified as a daily trip.

[0007] In some aspects of the present disclosure, the method may further include identifying attributes of the at least one first transportation mode. The method may further include automatically identifying attributes of the at least one first transportation mode. In some aspects of the present disclosure, the at least one attribute of the alternative transportation mode may correspond to at least one attribute of the first transportation mode. In some aspects of the present disclosure, when identifying the at least one alternative transportation mode with improved transportation metrics, alternative transportation modes that do not include at least one attribute of at least one of the at least one first transportation mode may be excluded from consideration. In some aspects of the present disclosure, the step of determining the at least one alternative transportation mode may further include identifying at least one available alternative transportation mode having the identified attributes. In some examples, the step of determining the at least one alternative transportation mode may further include receiving a user selection.

[0008] Some aspects of the disclosure relate to a system that may include one or more processors configured to: identify at least one first transportation mode of a user's transportation; determine travel information including a trip start location and one or more destination locations; determine a value of a transportation metric of the at least one alternative transportation mode based on the travel information and the at least one first transportation mode; determine the at least one alternative transportation mode based on the travel information; determine a value of the transportation metric of the at least one alternative transportation mode based on the travel information and the at least one alternative transportation mode; compare a value of the transportation metric associated with the at least one alternative transportation mode to a value of the transportation metric of the at least one first transportation mode; and provide an indication for output to identify the at least one alternative transportation mode, wherein a value of the transportation metric associated with the at least one alternative transportation mode is improved compared to a value of the transportation metric associated with the at least one first transportation mode.

[0009] In some aspects of the present disclosure, at least one of the at least one first transportation mode and at least one of the at least one alternative transportation mode may be different from each other. The transportation metric may include at least one of a total distance traveled, a total travel time, a carbon footprint, a driving cost, or vehicle operation data. The vehicle operation data may include a fuel type of the vehicle, a fuel usage of the vehicle, an estimated maintenance of the vehicle, and a government incentive for the vehicle. In some aspects of the present disclosure, determining the transportation metric may be further based on toll information, parking options, and refueling options. In some aspects of the present disclosure, the at least one alternative transportation mode may include a plurality of alternative transportation modes, and the method further includes ranking the plurality of alternative transportation modes based on values ​​of the transportation metric associated with each of the alternative transportation modes, the ranking of the plurality of alternative transportation modes being based on a lowest cost to the user to a highest cost to the user. The system may further include one or more processors configured to output the ranked plurality of alternative transportation modes. The system may further include one or more processors configured to provide for output at least one of a value of the transportation metric of the first transportation mode and a value of the transportation metric of the at least one alternative transportation mode.

[0010] In some aspects of the present disclosure, the system may further include one or more processors configured to receive location information corresponding to the user's travel history, and receiving the location information corresponding to the user's travel history may further include receiving the location information from at least one of a memory of the user device, one or more servers, or a navigation application. The system may further include one or more processors configured to determine the one or more travel information by determining a pattern or frequency of the plurality of trips based on the location information, and identifying one or more daily trips based on the determined pattern or frequency of the plurality of trips. When identifying the one or more daily trips based on the frequency of the plurality of trips, the system may further be configured to compare the frequency of each of the plurality of trips to a threshold frequency. In some examples, when the frequency of each of the trips is higher than the threshold frequency, each of the trips may be identified as a daily trip. In some aspects of the disclosure, the system may be further configured to automatically identify attributes of the at least one first transportation mode. In some examples, the at least one attribute of the alternative transportation mode may correspond to the at least one attribute of the first transportation mode. In some examples, when identifying the at least one alternative transportation mode with the improved transportation metric, all alternative transportation modes that do not include at least one attribute of at least one of the at least one first transportation mode may be removed from consideration. In some examples, determining the at least one alternative transportation mode further includes identifying at least one available alternative transportation mode having the identified attribute. In some examples, determining the at least one alternative transportation mode may further include receiving a user selection. [Brief description of the drawings]

[0011] [Figure 1]11 is an exemplary screenshot illustrating a user interface for receiving a request to calculate transportation metrics for a first transportation mode, according to an aspect of the present disclosure. [Diagram 2] 11 is another example screenshot illustrating a user interface providing example transportation metrics for a first transportation mode, according to an aspect of the present disclosure. [Diagram 3] 1 illustrates an exemplary user interface for selection of attributes according to an aspect of the present disclosure. [Figure 4] 1A-1C illustrate example user interfaces depicting different types of transportation modes that may be selected by a user for consideration as alternative transportation modes, according to aspects of the present disclosure. [Diagram 5] FIG. 13 illustrates example predicted transportation metrics for alternative transportation modes according to aspects of the present disclosure. [Figure 6A] FIG. 1 is a functional block diagram of an exemplary system according to an aspect of the present disclosure. [Figure 6B] FIG. 6B is a pictorial diagram of the example system of FIG. 6A according to an embodiment of the present disclosure. [Figure 7] 1 is a flow diagram illustrating an example method for determining a transportation metric according to an aspect of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The present technology generally relates to a system and method for generating adapted recommendations for a user's transportation based on the user's automatically determined travel needs. The adapted recommendations may suggest alternative transportation modes compared to one or more current transportation modes implemented by the user. The adapted recommendations may improve the value of a transportation metric compared to the value of the same transportation metric of the user's current transportation mode.

[0013] The transportation metric may assess many trip factors and provide a clear and concise value or values ​​that indicate the effect of changing the user's typical transportation mode to an optimized transportation plan on specific costs such as the user's environmental impact, overall trip cost, etc. The transportation metric may include, for example, the costs associated with the trip such as the total distance traveled, the time spent traveling, and the monetary cost of the trip. Other transportation metrics may include the carbon footprint of the trip, parking options, availability of specific fuel stops, vehicle maintenance, etc.

[0014] The value of the transportation metric may be determined for a predetermined period of time, such as a day, a week, a month, a year, etc. The value of the transportation metric may be determined based on the user's daily or regular trips. In some examples, the value of the transportation metric may be determined based on the user's transportation mode for the user's daily trips.

[0015] The value of the transportation metric may be determined automatically without user input. For example, the system may determine daily trips, associated destinations, etc., without user input, based on the user's travel history. An objective assessment of the daily trips is provided, thereby ensuring accurate determination of the average value of the user's transportation metric.

[0016] A first value of one or more transportation metrics may be determined based on the user's current transportation mode and travel history. For example, the user's travel history may be used to determine how far the user drives in a given period of time, how much time the user spends driving, etc. Based on that information and the user's current transportation mode, values ​​of one or more transportation metrics, such as fuel consumption, carbon footprint, wear and tear, etc. Other transportation metric values ​​that may be determined may include, for example, total cost of ownership or rental cost for a given period of time, insurance rates, maintenance, etc.

[0017] A second value of one or more transportation metrics may be determined based on the proposed alternative transportation mode and the user's travel history. Similar to determining the first value of the one or more transportation metrics, the second value of the one or more transportation metrics may be determined based on the user's travel history, such as distance traveled in a given time period, time spent driving, etc. Based on the information, e.g., the user's travel history, and the user's current transportation mode, values ​​of one or more transportation metrics of the proposed alternative transportation mode, such as fuel consumption, carbon footprint, wear and tear, etc. Other transportation metric values ​​that may be determined may include, for example, total cost of ownership or rental cost for a given time period, insurance rates, maintenance, etc.

[0018] A second value of the one or more transportation metrics may be determined based on the different transportation options, such that the second value is improved compared to the first value. For example, if the first and second values ​​quantify a carbon footprint, the second value may represent a reduced carbon footprint compared to the first value.

[0019] By automatically determining the current value of one or more transportation metrics of the user and the predicted value of the one or more transportation metrics, the user is provided with an objective and accurate indication of at least one alternative transportation mode that improves the value of the one or more transportation metrics. Furthermore, the user can make an informed decision to reduce the user's travel costs and improve the user's lifestyle by selecting at least one alternative transportation mode that improves the value of the one or more transportation metrics. In some examples, the determined values ​​of the one or more transportation metrics may be used to identify more cost-effective or environmentally friendly routes that meet the user's preferences to keep the value of the one or more transportation metrics below a threshold value. Additionally or alternatively, the determined values ​​of the one or more transportation metrics may be used to determine alternative transportation modes that meet or are below a threshold value of the one or more transportation metrics. This may increase energy efficiency and reduce the user's travel costs.

[0020] The value of the one or more transportation metrics may be used to evaluate the impact of modifying a user's typical route on a given transportation metric. For example, the predicted value of the one or more transportation metrics may provide an indication of the total travel distance, expected vehicle maintenance associated with daily use, total travel time, travel costs such as estimated fuel costs, availability of refueling options, public transportation tickets, carbon footprint caused by travel, etc. that the user would experience when traveling to their daily trips or related destinations using the optimized route.

[0021] According to some examples, values ​​of one or more transportation metrics may be used to identify alternative modes of transportation. For example, if a user's current mode of transportation is a pickup truck, the alternative modes may be a sedan, an electric bike, public transportation, carpooling, etc.

[0022] 1 illustrates an example display of application 102 showing transportation metric options. As shown in this example, the transportation metrics include travel information 118, refueling cost 120, travel expense 122, and carbon footprint 124. The value of the transportation metric may be determined based on the travel information 118, such as the user's current transportation mode, the user's travel history and / or location information, etc.

[0023] When determining the value of the transportation metric, the system may identify the user's first transportation mode 116. The first transportation mode 116 may be, for example, the user's existing transportation mode. The transportation mode may be, for example, a type of vehicle, public transportation, ride sharing, a taxi, etc.

[0024] According to some examples, the first transportation mode 116 may include one or more transportation modes. For example, the first transportation mode may include multiple vehicles, a combination of vehicle types such as internal combustion engine ("ICE") vehicles, hybrid vehicles, or electric vehicles ("EV"), bicycles, public transportation, etc.

[0025] The user's current mode of transportation may be determined based on information received as input from the user. For example, the system may receive input corresponding to the mode of transportation's manufacturer 143, model 144, year 145, mileage 146, number of seats 147, maintenance history, etc. The input may be provided as a drop-down menu, sliding scale, radio buttons, etc. for selecting information about the first mode of transportation 116. According to some examples, instead of having various enumerated inputs, a pop-up, overlay, etc. may be provided in response to receiving an input corresponding to the selection of the first mode of transportation 116. Within the pop-up may be an input that allows the user to provide information about the current mode of transportation 116 from the user.

[0026] Additionally or alternatively, the first transportation mode 116 may be determined based on the user's location information and / or travel history, such as by detecting public transportation routes, toll routes, and the like. The system may scan the user's travel history and detect that the user frequently travels routes that match local bus routes and / or that the user's travel times match bus schedules along the same routes. The system may obtain that information to determine that the user's first transportation mode is public transportation, specifically, a city bus. Similarly, the system may scan the user's travel information and detect whether the user frequently pays tolls specific to high occupancy vehicle (HOV) lanes. The system may use this information to infer that the user's first transportation mode includes carpooling. This information may be utilized later to help determine alternative transportation modes. For example, the system may use this information to determine whether the user is eligible for carpooling or HOV benefits, such as reduced tolls or use of designated lanes.

[0027] Additionally or alternatively, the first transportation mode 116 may be determined based on the user's location information and / or travel history based on information from the user's ride-sharing application. The system may determine the user's travel information using information from a ride-sharing application profile. For example, the user's profile may indicate how frequently the user requests rides through the application, and such information may be used to infer that the user's first transportation mode is a ride-sharing option.

[0028] The user's travel history or travel information 118 may be based on location information provided by a personal device, a navigation system, a map application, etc. Location information may be, for example, data collected by a device having a location sensor, such as a Global Positioning System (GPS), that indicates locations to which the user has traveled. In some examples, location information may include destinations identified in emails, calendar appointments, etc. to which the user has granted the system access.

[0029] The location information may be stored locally on the personal device or the navigation system. In some examples, the location information may be shared to a remote location, such as a remote server. According to some examples, the location information may be used to identify the type and frequency of destinations visited, such that the system does not need or obtain the location of a particular destination. For example, the system may know that a user visits a particular chain of gas stations, but not know the specific location of one of the chains. The system may use this location information to better understand and predict the user's route and suggest alternative transportation modes.

[0030] The personal device may be any device that includes a location sensor, such as a smartphone, tablet, laptop, smartwatch, AR / VR headset, smart helmet, etc. The navigation system may be integrated into the user's vehicle or personal device in some examples. The device and navigation system may be a device that has hardware that can determine the user's location. The hardware may be, for example, a location sensor. The map application may be, for example, an application executed by the personal device or vehicle.

[0031] A user may consent to sharing their location and movement information. For example, a user may grant authorization to an application to determine transportation metrics. Authorization may be for the application to access one or more databases or sub-databases in memory of the device, vehicle, remote server, etc. According to one example, a user may select the particular sub-databases that an application is allowed to access. For example, a user may grant access to a location history database but not a calendar archive database.

[0032] Based on the user's travel information 118, the system may determine a type of trip based on the respective origin and destination locations. The type of trip may be a routine trip or a non-routine trip. A routine trip may be a trip that occurs with a regular or routine frequency such that the routine trip creates a pattern in the travel information. The pattern may be a repeating occurrence over a specified period of time or a periodic occurrence. The pattern may be identified by the time of day the trip occurs, the time of year the trip is made, whether the trip occurs every other week, once a month, etc. In some examples, a routine trip may be a trip that occurs with or above a threshold frequency. The threshold frequency may be, in some examples, daily, three times a week, three times a month, etc. A non-routine trip may be a trip that does not occur in a pattern. In some examples, a non-routine trip may be a trip that occurs less than a threshold frequency.

[0033] The user's travel information may be used to identify the user's daily trips to destinations. Examples of daily trips may include the user's commute to work, the user's dropping off and picking up dry cleaning, weekly grocery shopping, etc. Daily trips may include a starting location and a destination location. In some examples, a daily trip may include a starting location and two or more destination locations. A destination may be, for example, a location that is frequently visited by the user. In some examples, the user's travel history may be used to identify destinations or types of destinations, such as a pet groomer, a nail salon, a barber shop, a pool supply store, etc.

[0034] According to some examples, the system may determine an aggregate value of one or more transportation metrics for multiple users who share a current transportation mode. For example, multiple users may live in the same residential unit. A primary user may select household members to include in the calculation of the value of the transportation metric. The system may determine the value of one or more transportation metrics for each user based on each user's transportation mode and travel information, with each user's consent. Furthermore, the system may combine the values ​​of one or more transportation metrics for each selected user to determine a value of a household transportation metric. For example, in a household with three selected users, the system may determine and take into account the individual values ​​of one or more transportation metrics to suggest a combined route, thus reducing the value of the household transportation metric.

[0035] According to some examples, the system may identify a pair of locations based on the user's travel information. The pair of locations may include a start location and a destination location for a trip. The system may identify the start location based on user input, information from a position sensor such as a GPS, etc. The destination location may be identified based on a location entered into a navigation application, a calendar event, an email, a position sensor, etc. In some cases, a trip may include stops between the start location and the destination location. For example, a user may stop at a coffee shop while traveling between the start location and the destination location.

[0036] The system may determine a pattern or frequency for each identified pair of starting and destination locations. The pattern for each identified pair of starting and destination locations may be, for example, whether the trips occur on the same day of the week, whether they occur at the same time every day, whether they occur on the same day every month, whether they occur once a month, etc. In some examples, the pattern may be whether the trips occur during a particular part of the year, such as seasonal trips or school year pickups and drop-offs. In yet another example, the pattern may be whether the trips occur during a particular part of the day, such as a user's daily commute at the same time every day.

[0037] The frequency of each identified pair of starting and destination locations may be a measure of how frequently the starting or destination location is traveled by the user within a given time period. For example, the frequency of a trip may be once a day, multiple times a day, every Tuesday and Thursday, once every four days, multiple times a month, etc. If the system determines, based on the user's travel information, that a trip of a given starting and destination location pair is completed more than a threshold number of times within a given time period, the system may determine that the pair is a routine trip that should be included when determining the value of one or more transportation metrics. Additionally or alternatively, if the system determines that a trip of a given location pair does not occur more than a threshold number of times within a given time period, the system may determine that the pair is a non-routine trip.

[0038] According to some examples, the system may use a machine learning ("ML") model to determine whether a trip is a routine or non-routine trip. The ML model may be trained to determine whether a trip is a routine trip. Each training example may consist of a start location and a destination location. The input features to the ML model may be the frequency of the trip. The ML model may use the input features to more accurately determine whether the trip is a routine trip. The output of the ML model may be a determination of whether the trip is a routine trip. In some examples, the system may ask for feedback from the user. For example, the user may be asked to confirm whether the trip is a routine trip. The user may provide feedback such as "yes" or "no" indicating that the determination was correct.

[0039] The user's travel information may be used to identify destinations or types of destinations that are frequently visited by the user. In some examples, the destinations may be destinations that are frequently visited by the user, destinations stored in the user's address book, destinations identified by the user as relevant, etc.

[0040] Destinations that are frequently visited by a user may be used when determining values ​​for one or more transportation metrics, regardless of whether the destination is outside a given threshold. For example, the system may identify a trip destination as a destination to be included if the system determines that the user has visited the destination more than a predetermined number of times within a given time period. In some examples, the system may receive input from a user identifying a destination as relevant. In such examples, the system may receive input regarding the location of each destination, identifying the destination as relevant, unrelated, periodically visited, etc. In some examples, the system may receive input identifying the location of the destination as a location type such as "workplace," "gym," "clinic," etc. The system may identify the labeled destination as a destination to be included when determining values ​​for one or more transportation metrics.

[0041] Thus, by automatically determining travel information based on data received by the system (such as average travel distance or calculated distance), the system advantageously determines improved options for alternative transportation modes that objectively provide an average reduction in the user's fuel consumption, travel time, fuel emissions, transit time, travel costs, and / or travel distance. Although a single transportation metric was considered in the above example, the determination of alternative transportation modes may be based on multiple transportation metrics.

[0042] As described above, since it is tedious, computationally intensive and time consuming for a user to comb through information provided by various entities and manually aggregate the data, automating the determination of travel information corresponding to user-specific data, improved values ​​of one or more transportation metrics may improve the efficiency of the system.

[0043] FIG. 2 illustrates an example value of a transportation metric based on the transportation mode and travel information. For example, the transportation metric may be based on the user's current transportation mode and trips identified as being routine trips or destinations identified as associated destinations. Additionally or alternatively, the system may determine a value of the transportation metric based on expected traffic and road conditions based on a forecasted time of when the drive must be made. The value of the transportation metric may be determined based on the identified trip and / or associated destination in association with the type of transportation mode. For example, the system may determine travel information 118 and use the travel information to determine values ​​of transportation metrics such as refueling costs 120, expenses 122, carbon footprint 124, etc. The expenses 122 may be expenses associated with the transportation mode, such as expected car maintenance and monthly payments, for example. According to some examples, the system may determine suggestions for reducing the value of the transportation metric based on the travel information 118 and the first transportation mode 116, such as alternative routes, taking trips at different times to reduce the amount of traffic the user encounters, different parking options, etc.

[0044] According to some examples, the system may have access to an online database of vehicle characteristics. The online database may include information regarding specific manufacturers, models, and years of various modes of transportation. In particular, the database may have information regarding characteristics of a particular model of vehicle based on the model year of the vehicle, such as fuel efficiency, emissions, highway miles per gallon, fuel capacity, passenger capacity, storage capacity, cost of ownership, etc. The system may use the database when determining values ​​for one or more transportation metrics based on determined or input vehicle attributes.

[0045] According to some examples, the system may use an ML model to determine vehicle characteristics associated with various transportation modes when determining values ​​for one or more transportation metrics. The ML model may be trained using multiple training examples including different transportation modes. Feature inputs for the ML model may include features such as fuel efficiency, emissions, highway miles per gallon, fuel capacity, passenger capacity, storage capacity, cost of ownership, etc. The ML model may use the input features to determine values ​​for one or more transportation metrics for the current transportation mode. The output of the ML model may be a determination of whether the feature should be included when determining values ​​for the one or more transportation metrics. In some examples, the system may ask for feedback from the user. For example, the user may be asked to confirm whether the determined characteristics were accurate. The user may provide feedback such as "yes" or "no" indicating that the determination was correct.

[0046] The system may use user movement information 118 for a period corresponding to a predetermined period used to determine the value of one or more transportation metrics. For example, if the value of the transportation metric is for two weeks in September, the system may use user movement information 118 from the most recent two weeks in September when calculating the value of the transportation metric for the current transportation mode.

[0047] In some examples, the system may determine a value of the transportation metric based on the user's travel information, the current transportation mode, and / or the transportation metric based on one or more additional transportation modes. The system may make suggestions 126 to reduce the value of one or more transportation metrics. These suggestions 126 may include recommended changes that reduce the value of one or more transportation metrics determined for the first transportation mode. For example, if a user has a first transportation mode, such as a large SUV, and a second transportation mode available to the user, such as an electric vehicle, the system may determine that the value of the transportation metric may be improved if the user utilizes the second transportation mode instead of the first transportation mode when completing routine trips with fewer than five passengers or trips that require only limited trunk space for luggage / equipment. In another example, as depicted in FIG. 2, the suggestions 126 may include considering alternative transportation modes. The system may determine that the value of the transportation metric may be improved by utilizing an alternative transportation mode, such as public transportation or carpooling, to complete at least a portion of a routine or non-routine trip.

[0048] According to some examples, the value of the transportation metric may include a refueling cost 120 associated with the first transportation mode 116. The refueling cost 120 may include, for example, local gasoline prices, local charging prices, availability of charging or gas stations along the user's usual route, etc. The refueling cost 120 associated with the first transportation mode 116 may be determined based on the user's travel information 118. For example, based on the total distance traveled in a given period and the first transportation mode, the system may determine the refueling cost 120 for the given period based on the average cost of fuel in the area. As shown in FIG. 2, knowing that the first transportation mode is a sedan of make "X" and model "Y" made in 2016, the system may determine the average mileage per gallon of the transportation mode. As an example, the average mileage per gallon of the first transportation mode 116 may be 22 miles per gallon. Based on the user's travel history, the system may determine that the user typically travels 275 miles per week. To determine the refueling cost to travel 275 miles per week, the system may determine how many gallons of gasoline are needed for the mode of transportation 116. For example, the system may divide the number of miles per week, 275, by the mode's miles per gallon, 22. In this example, the user needs 12.5 gallons of gasoline per week. The refueling cost for the week is the number of gallons of gasoline needed, 12.5, multiplied by the cost of the gasoline. Although the example shown in FIG. 2 is based on a one-week period, the duration or period may be one day, one week, one month, one year, etc. Thus, the example shown in FIG. 2 is merely illustrative and is not intended to be limiting.

[0049] According to some examples, the value of the transportation metric may include a cost 122 associated with the first transportation mode 116. The cost 122 may include, for example, car payments, insurance rates, standard maintenance costs, and the like. The standard maintenance costs may include, for example, regular oil changes, tire rotations, brake maintenance, and the like. The cost 122 associated with the first transportation mode 116 may be determined based on the user's travel information 118. For example, based on the daily route, destination, total mileage, and the first transportation mode 116 in a given time period, the system may determine the cost 122 for the given time period. As shown in FIG. 2, knowing that the first transportation mode is a sedan of make "X" and model "Y" made in 2016, the system may determine or identify the suggested regular maintenance for the particular transportation mode. As an example, the determined first transportation mode 116 may require an oil change every 8,250 miles. The system may determine, based on the user's travel information 118, that the user typically travels 275 miles per week. To determine the cost of having the oil changed in a vehicle, the system may determine how many weeks the user must travel to reach the point where an oil change is needed, every 30 weeks. The system may divide the expected cost of an oil change by the number of weeks until an oil change is needed and apply that number to the transportation metric for cost 122. In another example, the system may factor in travel costs such as tolls along the daily route taken by the user, or parking costs based on the user's destination. Knowing that the first mode of transportation is a sedan of make "X" and model "Y" made in 2016, as shown in FIG. 2, the system may determine the amount of tolls to be charged based on toll roads frequently visited by the user. As an example, a toll road on the user's commute route may charge $2.50 for two-axle vehicles and $5 for vehicles with three or more axles five days a week. The system may determine that the first mode of transportation is a sedan and calculate $2.50 five times a week in the transportation metric for cost 122.Although the example shown in Figure 2 is based on a one week period, the duration or period may be one day, one week, one month, one year, etc. Thus, the example shown in Figure 2 is merely illustrative and not intended to be limiting.

[0050] According to some examples, the value of the transportation metric may include a carbon footprint 124 associated with the first transportation mode 116 and the user's travel information 118. The carbon footprint 124 may be determined based on the distance traveled by the user and the first transportation mode 116. As shown in FIG. 2, knowing that the first transportation mode is a sedan of make "X" and model "Y" made in 2016, the system may determine an emission factor for the first transportation mode 116, where the emission factor is the amount of CO2 emitted by the combustion of one gallon of that fuel. The formula for determining the carbon footprint of a transportation mode based on the user's travel history is as follows: CF = g × EF where CF is the determined carbon footprint, g is the amount of gasoline needed per week, and EF is the emission factor. For example, the emission factor for the first transportation mode 116 may be 0.4. The system may determine based on the user's travel history that the user typically travels 275 miles per week. To determine the carbon footprint of the 275 miles / week travel, the system may determine how many gallons of gasoline are needed for the transportation mode 116. For example, the system may divide the number of miles per week, 275, by the number of miles per gallon of the transportation mode, 22. In this example, the user requires 12.5 gallons of gasoline per week. The weekly carbon footprint 124 is the number of gallons of gasoline required, 12.5, multiplied by the emission factor. Although the example shown in FIG. 2 is based on a one-week period, the duration or period may be a day, a week, a month, a year, etc. Thus, the example depicted in FIG. 2 is merely illustrative and not intended to be limiting.

[0051] According to some examples, the system may identify attributes of the first transportation mode 116 and use the identified attributes when determining the value of the transportation metric. The attributes may include, for example, type of transportation, model of vehicle, manufacturer, capacity, year, fuel efficiency, estimated initial cost, etc.

[0052] According to some examples, the system may receive input corresponding to a current transportation mode having multiple users. The system may determine an aggregate value of one or more transportation metrics for the current transportation mode based on movement information of the multiple users utilizing the current transportation mode. Each user of the vehicle may agree to share user location information and movement information for purposes of determining values ​​of the one or more transportation metrics.

[0053] In some examples, the system may determine that a first user of the multiple users is a primary user of a first mode of transportation, and the rest of the users are secondary users. For example, the first mode of transportation may be a vehicle shared by multiple users of a household. In this example, the system may indicate a primary user, such as the first user, and a secondary user, such as the second user. The indication of the primary and secondary users may be determined based on the user's travel history. For example, the primary user may be a user who utilizes vehicles more for regular trips compared to the secondary users.

[0054] In some examples, the system may determine that there are multiple users of the current transportation mode based on shared characteristics between users, such as a shared home address, similar travel history and / or routine trips, time spent together at a particular location, etc.

[0055] When considering multiple users and multiple current transportation modes, the system may automatically determine which users utilize which transportation modes most frequently in order to allocate a particular transportation mode to each user. For example, the system may recognize that a primary user uses pickup trucks most frequently, while a secondary user uses hybrid cars most frequently. This information may be taken into account when determining the value of the users' transportation metric. The system may use the primary user's current transportation mode, pickup trucks, and the primary user's travel history to calculate the value of the primary user's transportation metric. Similarly, the system may use the secondary user's current transportation mode, hybrid cars, and the secondary user's travel history to calculate the value of the secondary user's transportation metric. The values ​​of the primary user and secondary user's metrics are correlated or combined to obtain the value of the household transportation metric.

[0056] In all examples, location sharing is authorized by the user, who may revoke authorization at any time, such as by adjusting settings on the user's electronic device.

[0057] FIG. 3 illustrates exemplary inputs corresponding to criteria for alternative modes of transportation. Alternative modes of transportation may be modes of transportation not currently utilized by the user, modes of transportation not currently owned or leased by the user, etc. For example, if the user's current mode of transportation is a sedan owned by the user, the alternative modes of transportation may be a sedan of a different make, model, and / or year, a coupe, a minivan, a pickup truck, public transportation such as a bus, a ride-share or carpool, etc. The alternative modes of transportation may be proposed as partial replacements of the current modes, such as using the alternative modes for only a portion of the user's daily trips, or may be proposed as a complete replacement. In other examples, a combination of alternative modes of transportation may be proposed.

[0058] The system may receive information corresponding to criteria required for alternative modes of transportation. The criteria may include, for example, vehicle type, manufacturer, model, year, number of doors, number of seats, price range, maximum price, minimum price, initial cost, monthly cost, costs associated with the mode of transportation, fuel efficiency, fuel type, mileage range, etc. Although the inputs are shown as drop-down menus, the inputs may also be radio buttons, sliding scales, range indicators, pictorial diagrams, etc. Thus, the inputs shown and the selection options shown are only a few examples and are not intended to be limiting.

[0059] According to some examples, the system may receive inputs for one or more criteria. For example, the system may receive inputs corresponding to the number of seats required by the user. According to some examples, the system may use the travel history as described above to autofill the inputs for one or more of the criteria. For example, the system may determine that the vehicle is a high efficiency vehicle based on the user's travel history. As depicted in FIG. 3, the display may show attributes 341 on one side of the screen and a user input module 342 on the other side of the screen. The attributes 341 may include, for example, type of transportation, model of vehicle, manufacturer, capacity, year, fuel efficiency, initial cost, etc. The user input module 342 corresponding to the attributes 341 may have predefined selections based on the attributes. For example, the user input module 342 for the number of doors attribute may show a selection of 2, 3, or 4 doors. The user input module 342 may have predefined selections based on the user's inputs for the other attributes 341. For example, if a user selects a hybrid vehicle for the vehicle type attribute, the system limits the input options for the manufacturer attribute in the user input module 342 associated with that attribute to vehicles with a hybrid option.

[0060] In some examples, the system may automatically determine attributes of the alternative transportation mode. For example, the system may receive input regarding a vehicle of interest and populate attributes of the vehicle to determine values ​​of one or more of its transportation metrics. According to some examples, the system may identify characteristics of the first transportation mode and use the identified attributes to determine the alternative transportation mode. Additionally or alternatively, the system may automatically determine criteria for the alternative transportation mode based on the travel information 118. For example, the system may identify that the value of one or more of the user's transportation metrics may be reduced by using an electric vehicle based on the user's daily route. The system automatically inputs the type of vehicle as electric. As another example, the system may identify that the household may reduce the value of the household's transportation metric by reducing the number of vehicles in the household and purchasing a vehicle with a higher capacity based on the value of the household transportation metric. The system automatically inputs the higher capacity for the alternative transportation mode into the system.

[0061] According to some examples, the system may identify one or more alternative transportation modes based on a user's transportation cost budget. For example, the system may receive an input corresponding to a user's weekly, monthly, yearly, etc. budget. The system may compare the received budget to a budget threshold to identify alternative transportation modes that match or improve on the value of the transportation metric of the current transportation mode. The budget threshold may be equal to the user's weekly, monthly, yearly, etc. budget.

[0062] According to some examples, alternative transportation modes may be identified for routine trips, non-routine trips, all trips, etc. For example, alternative transportation modes may be identified as improving the value of one or more transportation metrics for routine trips, non-routine trips, all trips, etc. In one example, values ​​of one or more transportation metrics of the user's current transportation mode for one or more non-routine trips may be compared to values ​​of one or more transportation metrics of alternative transportation modes for the same non-routine trips. Continuing with this example, if the user's current transportation mode is an electric vehicle, the system may determine values ​​of one or more transportation metrics for the identified electric vehicle non-routine trip based on the user's travel information. The non-routine trip may be, for example, a trip across the country for a vacation. Based on the values ​​of the one or more transportation metrics for the electric vehicle, the system may identify one or more alternative transportation modes that improve at least one value of the one or more transportation metrics. For example, the system may identify airplane travel or train as an alternative transportation mode. In such an example, travel costs for airplane travel or train travel may be higher compared to using an electric vehicle, but using airplane travel or train travel may reduce travel time and travel distance. The system may determine that using another form of travel, e.g., airplane or train, improves the value of a transportation metric (in this example, the transportation metric is travel time or travel distance) compared to using a current transportation mode, e.g., an electric vehicle. Thus, the transportation metric to be improved may be predetermined by a user's selection or preference, such that the proposed alternative transportation mode improves the predetermined transportation metric. In this manner, the user can customize the transportation metric to be improved, such as reducing travel time or reducing travel distance. Thus, the method described herein may include a step of selecting the transportation metric to be improved and may receive an input selecting the transportation metric to be improved.

[0063] FIG. 4 illustrates a display in which a user may select a category of transportation modes. The categories of transportation modes may include internal combustion engine ("ICE") cars 431, hybrid / electric cars 432, ride-share options / taxis 433, two-wheel options such as electric bikes or motorcycles 434, public transportation including but not limited to subways / trains 435 and buses 436, carpooling options 437, and manual bicycles 438. The user may select one or more categories for the system to select when suggesting alternative transportation modes. For example, the user may select the categories of ICE cars, electric cars, and trains as acceptable transportation modes for the system to suggest. In response, the system may suggest routes that reduce the value of one or more transportation metrics, including electric cars and trains.

[0064] The system may utilize values ​​of the transportation metrics of the different travel modes to determine one or more alternative transportation modes. The alternative transportation modes may be travel modes that improve the value of one or more transportation metrics. For example, the alternative transportation modes may reduce fuel consumption, carbon footprint, overall cost of owning the transportation mode, may reduce daily travel expenses, may increase miles per gallon, etc.

[0065] 5 illustrates an example output of predicted values ​​of one or more transportation metrics. Although trip information 518, fueling costs 520, expenses 522, and carbon footprint 524 are all shown, the system may output predicted values ​​for only one or some of the transportation metrics. For example, application 502 may include menu options that allow a user to select which transportation metrics should be shown.

[0066] According to some examples, the system may determine a value of a transportation metric based on the user's travel information and a current transportation mode. The output of the system may include identification of an alternative transportation mode 517. For example, the system may identify an alternative transportation mode based on the user's travel information, transportation attributes, etc. The transportation attributes may include, for example, a type of transportation, such as motorcycle, car, truck, SUV, carpool, public transportation, initial ownership cost, fuel efficiency, maintenance cost, etc. The system may determine a value of a transportation metric for the identified alternative transportation mode. In some examples, the system may select the alternative transportation mode 517 based on a list of automobiles recently considered by the user. In some examples, the application 502 may include an input for a user to provide a transportation mode. For example, the user may input a desired attribute of the transportation mode, or a manufacturer and model in which the user is interested.

[0067] According to some examples, the system may use an ML model to determine vehicle characteristics associated with various modes of transportation when determining the value of one or more transportation metrics. The ML model may be trained using multiple training examples that include different modes of transportation. Feature inputs for the ML model may include features such as fuel efficiency, emissions, highway miles per gallon, fuel capacity, passenger capacity, storage capacity, cost of ownership, etc. The ML model may use the input features to more accurately determine how the value of the transportation metric of the alternative mode of transportation compares to the current mode of transportation. The output of the ML model may be a determination of whether the alternative mode of transportation should be included when proposing a way to reduce the value of the one or more transportation metrics. In some examples, the system may ask for feedback from the user. For example, the user may be asked to confirm whether the alternative mode of transportation was a good fit for their needs. The user may provide feedback such as "yes" or "no" indicating that the determination was correct.

[0068] The system may compare the value of one or more transportation metrics of the current transportation mode with each of the alternative transportation modes. The comparison may be used to identify which transportation mode provides an improved value of one or more transportation metrics. In some examples, the system may select an alternative transportation mode 517 based on a transportation mode that is likely to reduce the value of one or more of the user's transportation metrics compared to the user's current transportation mode. In this way, the user can obtain an objective measure of how the alternative transportation mode 517 will affect the value of one or more of the user's transportation metrics, assuming all daily trips are maintained. Thus, the described method can either automatically provide a proposed alternative transportation mode option that reduces the value of one or more of the user's transportation metrics if the user were to utilize any of the proposed alternative transportation modes, or automatically select an alternative transportation mode 517 for the user as a mode that reduces the value of one or more of the user's transportation metrics. For example, the automatically selected alternative transportation mode 517 may reduce the user's refueling costs 520, expenses 522, or carbon footprint 524. Such transportation metrics may be based on the assumption that users maintain all routine trips.

[0069] According to some examples, the system may calculate values ​​of one or more transportation metrics for multiple transportation modes simultaneously. Thus, a user may consider multiple proposals for alternative transportation modes, such as by clicking on different categories. According to some examples, some transportation modes may be excluded from consideration or calculation of the value of the transportation metric. For example, transportation modes that cannot accommodate the user's needs, such as capacity requirements, trunk space, fuel type, etc., may be excluded from consideration. In this regard, an optimal transportation mode may be determined in a computationally efficient manner.

[0070] 5, the predicted values ​​of one or more transportation metrics 518-524 may be output as text showing amounts per time period, e.g., per week, time, distance, cost, carbon footprint, etc. However, the predicted values ​​of one or more transportation metrics 518-524 may also be output based on any predetermined time period, such as a day, a week, a month, three months, a year, etc. In some examples, the predicted values ​​of one or more transportation metrics 518-524 may be output in a graphical format, such as a histogram, a pie chart, etc.

[0071] According to some examples, the predicted values ​​of the one or more transportation metrics 518-524 may include an indication of the predicted values ​​of the one or more transportation metrics and the current values ​​of the one or more transportation metrics. The current values ​​of the one or more transportation metrics may be values ​​of the one or more transportation metrics based on the user's current transportation mode, such as the user's current vehicle. The user may use the predicted values ​​of the one or more transportation metrics when evaluating the impact on the user's overall trip based on changing the user's current transportation mode to an alternative transportation mode. This may be useful, for example, when pricing a vehicle purchase.

[0072] Distance 518 based on data collected from the user's travel over a predetermined period of time may be 297 miles per week. In contrast, the user's current distance of the user's daily trips based on the user's travel information may be 275 miles per week. Thus, the user may determine based on the distance metric that they may be likely to drive an additional 22 miles per week to accommodate the alternative mode of transportation. In this example, the system takes into account that the alternative mode of transportation is an electric vehicle that needs to be charged at public charging stations that may not be along the user's daily route, requiring additional driving in order to function.

[0073] A refueling cost 520 for traveling to the associated destination or the proposed alternative destination based on the alternative transportation mode 517 may be $25.69 per week. In contrast, the user's current refueling cost 520 for the user's daily trips and to the user's associated destination based on the current transportation mode may be $43.37 per week. Thus, the transportation metric may indicate that the user could potentially save $17.68 per week if they changed their transportation mode.

[0074] The cost 522 to travel to the associated destination or the proposed alternative destination based on the alternative transportation mode 517 may be $87.75 per week. Similarly, the user's current cost 522 for the user's daily trips and to the user's associated destination based on the user's current transportation mode may be $87.75 per week. Thus, the transportation metric may indicate that the user's cost may not change between the current transportation mode and the alternative transportation mode. In this example, the system may recognize that the cost is the same or similar to the current transportation mode. For example, if the current transportation mode and the proposed alternative transportation mode are both four-door sedans, the cost to park a vehicle in the same parking lot may remain the same.

[0075] A carbon footprint 524 for traveling to the associated destination or the proposed alternative destination based on the alternative transportation mode 517 may be 111.1 kg CO2 per week. In contrast, a user's current carbon footprint 524 for the user's daily trips and to the user's associated destination based on the user's current transportation mode may be 128.4 kg CO2 per week. Thus, the user may determine based on the carbon footprint 524 metric that the user could potentially save 17.3 kg CO2 per week if they changed their transportation mode.

[0076] Based on the transportation mode providing the improved value, the system may provide one or more suggestions for improving the value of one or more of the user's transportation metrics. According to some examples, the system may output a suggestion 526 for reducing the value of one or more transportation metrics based on the predicted value of one or more transportation metrics 518-524. The suggestion for reducing the value of one or more transportation metrics may include using public transportation for certain daily trips or using more convenient fueling options. The suggestion may also include, for example, switching from a first transportation mode to a second transportation mode. For example, the system may provide a suggestion to switch from using an ICE vehicle to using public transportation. In some examples, the suggestion may include purchasing another vehicle that reduces the value of one or more transportation metrics of the user. For example, the system may suggest selling the current vehicle and purchasing a vehicle that uses alternative fuels, such as an electric or hybrid model, or a vehicle that is more cost-effective to park at the usual destination point. In some examples, the suggestion may include switching the time the user travels to the destination location. For example, if a user normally gets to work via a highway, the system may suggest taking an alternative route to avoid traffic, thereby reducing the user's travel time. The suggestions may additionally or alternatively include public transportation options for one or more trips. For example, the system may suggest taking the bus to work or biking to the bank instead of driving to reduce the value of one or more transportation metrics (in this example, the one or more transportation metrics are travel time).

[0077] According to some examples, the system may provide suggestions for improving the value of one or more transportation metrics based on aggregate values ​​of one or more transportation metrics for multiple users who utilize the transportation mode. For example, the system may provide suggestions to combine users' regular routes to reduce the number of rides required for the household. In some examples, the suggestions may include public transportation options for one household member. For example, the system may provide a suggestion for one household member to travel with a second household member to a public transportation stop along the second household member's route to reduce the number of rides required and thus reduce the household's value of one or more transportation metrics.

[0078] According to some examples, the output may include suggestions of alternative transportation modes, such as alternative vehicles, to improve the value of the transportation metric when traveling to the destination location. For example, the output may include an indication of one or more alternative transportation modes, including other vehicle options and public transportation options, having a predicted value of the same transportation metric compared to a current value of the user's transportation metric, or a predicted value of the improved transportation metric compared to a current value of the user's transportation metric. Improving the value of the transportation metric may include reducing the value of the transportation metric. Providing an indication for output may include providing an indication for display on or output via the user's device, such as highlighting, selecting, or otherwise indicating the alternative transportation mode as improving the transportation metric.

[0079] According to some examples, the system may determine an alternative transportation mode based on movement information of multiple users utilizing the current transportation mode. Each user of the vehicle may agree to share the user's location and movement information for purposes of determining values ​​of one or more transportation metrics.

[0080] In some examples, the system may determine that a first user of the multiple users is a primary user of a first mode of transportation, and the rest of the users are secondary users. For example, the first mode of transportation may be a vehicle shared by multiple users of a household. In this example, the system may indicate a primary user, such as the first user, and a secondary user, such as the second user. The indication of the primary and secondary users may be determined based on the user's travel history. For example, the primary user may be a user who utilizes vehicles more for regular trips compared to the secondary users.

[0081] In some examples, the system may determine that there are multiple users of the current transportation mode based on shared characteristics between users, such as a shared home address, similar travel history and / or routine trips, time spent together at a particular location, etc.

[0082] When considering multiple users and multiple current transportation modes, the system may automatically determine which users use which transportation modes most frequently in order to allocate a particular transportation mode to each user. For example, the system may recognize that a primary user uses pickup trucks most frequently, while a secondary user uses hybrid cars most frequently. This information may be taken into account when determining the value of the user's transportation metric. In some examples, the system may make suggestions about who among the multiple users should use which transportation mode. For example, the system may suggest that the primary user use a first mode of alternative transportation modes and the secondary user use a second mode of alternative transportation modes.

[0083] In all instances, each selected user must individually consent and authorize the sharing of their location information with the system to be taken into account when determining the values ​​of one or more transportation metrics for the current transportation mode and alternative proposals.

[0084] 6A illustrates an example system 600 in which the features described above may be implemented. The example system 600 should not be considered as limiting the scope of the disclosure or the usefulness of the features described herein. In this example, the system 600 may include a device 602, a transportation system 612, a server computing device 630, a storage system 640, and a network 620.

[0085] Each of the devices 602 may include one or more processors 632, memory 642, data 662, and instructions 652. Each of the devices 602 may also include an output 672, a user input 682, and a position sensor 692. The devices 602 may be any device that includes a position sensor 692, such as a smartphone, a tablet, a laptop, a smart watch, an AR / VR headset, a smart helmet, etc., as shown in FIG.

[0086] The memory 642 of the device 602 may store information accessible by the processor 632. The memory 642 may also include data that may be retrieved, manipulated, or stored by the processor 632. The memory 642 may be of any non-transitory type capable of storing information accessible by the processor 632, including non-transitory computer-readable media or other media that store data that may be read with the aid of electronic devices, such as hard drives, memory cards, read-only memory ("ROM"), random access memory ("RAM"), optical disks, and other writable and read-only memories. The memory 642 may store information accessible by the processor 632, including instructions 652 and data 662 that may be executed by the processor 632.

[0087] Data 662 may be retrieved, stored, or modified by processor 632 in accordance with instructions 652. For example, data 662 may be stored in a computer register, in a relational database as a table with multiple different fields and records, in an XML document, or in a flat file, although this disclosure is not limited by a particular data structure. Data 662 may also be formatted in a computer-readable format, such as, but not limited to, binary values, ASCII, or Unicode. Simply by way of further example, data 662 may include sufficient information to identify relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memory (including other network locations), or information used by a function to calculate the relevant data.

[0088] The instructions 652 can be any set of instructions that are executed by the processor 632 directly, such as machine code, or indirectly, such as a script. In this regard, the terms "instructions," "application," "steps," and "program" may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computing device language, including scripts or a collection of independent source code modules that are interpreted on demand or precompiled. The functions, methods, and routines of the instructions are described in more detail below.

[0089] The one or more processors 632 may include any conventional processor, such as a commercially available CPU or microprocessor. Alternatively, the processors can be dedicated components, such as ASICs or other hardware-based processors. Although not required, the computing device 602 may include specialized hardware components to perform certain computing functions faster or more efficiently.

[0090] 6A functionally depicts the processor, memory, and other elements of device 602 as being within the same respective blocks, it will be understood by those skilled in the art that a processor or memory may actually include multiple processors or memories that may or may not be housed within the same physical housing. Similarly, a memory may be a hard drive or other storage medium located in a housing different from that of device 602. Thus, reference to a processor or device is understood to include reference to a collection of processors or devices or memories that may or may not operate in parallel.

[0091] The output 672 may be a display, such as a monitor having a screen, a touch screen, a projector, or a television. The display 672 of the one or more computing devices 602 may electronically display information to a user via a graphical user interface ("GUI") or other type of user interface. For example, as discussed below, the display 672 might electronically display a map interface that identifies associated destinations, daily trips, or one or more transportation metrics and their values ​​based on a specified starting location.

[0092] The user input 682 may be a mouse, keyboard, touch screen, microphone, or any other type of input. The user input may receive a user's authorization to use the location sensor 692 to obtain location information for transportation metrics. For example, the user may select particular applications for which location services are allowed, particular times when location services may be enabled, or other permissions or restrictions regarding location services.

[0093] The location sensor 692 may be, for example, a global positioning system ("GPS") sensor, a wireless communication interface, etc. The location sensor 692, when enabled by a user, may provide a coarse indication of the location of the device. According to some examples, when authorized by a user, the location sensor may provide location information indicative of associated destinations or routine trips.

[0094] The location information may be stored locally on the device 602 or on a navigation system such as part of an application or integrated into the vehicle 612. In some examples, the location information may be shared remotely, such as to a remote server 630 or storage system 640. According to some examples, the location information may be used to identify the types and frequency of destinations visited, such that the system does not need or obtain the location of certain destinations.

[0095] The device 602 may be at various nodes of the network 620 and may be capable of communicating directly and indirectly with other nodes of the network 620. Although three computing devices are depicted in FIG. 6A, it should be understood that a typical system may include one or more computing devices, with each computing device at a different node of the network 620. The network 620 and intervening nodes described herein may be interconnected using a variety of protocols and systems such that the network may be part of the Internet, the World Wide Web, a particular intranet, a wide area network, or a local network. The network 620 may utilize standard communication protocols such as WiFi, Bluetooth, 4G, 5G, etc., proprietary to one or more companies. Although certain advantages are obtained when information is transmitted or received as described above, other aspects of the subject matter described herein are not limited to any particular method of transmission.

[0096] In one example, the system 600 may include one or more server computing devices 630, e.g., a load-balanced server farm, having multiple computing devices that communicate with different nodes of a network to receive, process, and transmit data from and to other computing devices. For example, the one or more server computing devices 630 may be web servers that can communicate with one or more client computing devices 602 over the network 620. Additionally, the server computing device 630 may use the network 620 to transmit and present information to a user of one of the other computing devices 602 or a passenger in the vehicle. In this regard, the vehicle 612 may be considered a client computing device. The server computing device 630 may include one or more processors, memory, instructions, data, location sensors, etc. These components operate in the same or similar manner as described above with respect to the computing device 602.

[0097] As shown in FIG. 6B, each device 602 may be a personal computing device intended for use by a respective user 622 and may have all of the components typically used in association with a personal computing device, including one or more processors (e.g., a central processing unit (CPU)), memory (e.g., RAM and an internal hard drive) for storing data and instructions, an output such as a display (e.g., a monitor having a screen, a touch screen, a projector, a television, or other device such as a smart watch display operable to display information), and a user input device (e.g., a mouse, keyboard, touch screen, or microphone). The device may also include a camera for recording video streams, speakers, a network interface device, and all of the components used to connect these elements to each other. The device 602 may be able to wirelessly communicate or obtain data over the network 620.

[0098] Each of the client computing devices may include a full-sized personal computing device, but may alternatively include a mobile computing device capable of wirelessly communicating data with a server over a network such as the Internet. By way of example only, the devices 602 may be devices such as mobile phones, or wireless-enabled PDAs, smartphones, tablet PCs, wearable computing devices (e.g., smart watches, AR / VR headsets, smart helmets, etc.), or netbooks that can obtain information over the Internet or other networks.

[0099] The users 622 may operate their respective vehicles 612. The vehicles 612 may include location sensors. In some examples, the vehicles 612 may include an integrated navigation system. According to some examples, a navigation system may be integrated into the devices 602 of the users 622. In yet another example, the devices 602 or the vehicles 612 may run a map application that provides maps or directions, locates the user, etc.

[0100] All uses of the user's 622 location information or travel history are authorized by the respective user. For example, the user 622 may grant authorization to an application to determine values ​​for one or more transportation metrics by setting certain permissions for the application. The authorization may be for the application to access one or more databases or sub-databases in memory of the device, vehicle, remote server, etc. According to one example, the user may select the specific sub-databases that the application is allowed to access. For example, the user may allow access to a location history database but not to a calendar archive database.

[0101] The transportation system 612 may include a computing device (not shown). The computing device may include one or more components similar to the device 602, such as one or more processors, memory, data, instructions, displays, user inputs, etc. According to some examples, the transportation system 612 may include a perception system to detect objects outside the transportation system, such as other vehicles, roadway obstacles, traffic signals, signs, trees, etc., and perform analysis on those objects. Additionally or alternatively, the perception system may determine whether the vehicle is moving or parked. For example, the perception system may include a laser, sonar, radar, one or more cameras, or any other detection device that records data that may be processed by a computing device (not shown) in the transportation system 612. If the vehicle is a small passenger vehicle such as a car, the car may include a laser mounted on the roof or other convenient location, and other sensors (not shown), such as cameras, radar, sonar, and additional lasers.

[0102] Storage system 640 may store various types of information. For example, storage system 640 may store data or information related to a user's location information, such as the user's travel information, places of interest to the user, and associated destinations. In some examples, storage system 640 may store data or information related to destinations or points of interest ("POIs") for retrieval in response to a request to determine values ​​for one or more transportation metrics. As used herein, a POI may include any location or destination that a user may visit, such as an office building, an apartment building, a home address, a barber shop, a nail salon, a big box store, a local hardware store, a park, a green space, a restaurant, a theater, an amusement park, a shopping center, and the like.

[0103] Storage system 640 may store map data. This map data may include, for example, locations of POIs, locations of driving lanes, parking lanes, designated parking areas, no parking areas, drop-off locations, etc. Map data may also include locations, road names, road configurations, etc.

[0104] According to some examples, the storage system 640 may store data or information related to the location information of the user 622 after receiving authorization from the user 622. Authorization may be given, for example, by setting permissions for the system to access the location information and travel history. For example, the user may be provided with controls that allow the user to make selections about both whether and when the systems, programs, or features described herein may enable collection of location information, and whether the user will have content or communications sent from the server. Additionally, certain data may be processed in one or more ways before being stored or used, such that personally identifiable information is removed. The user may control what information is collected about the user, how that information is used, and what information is provided to the user. The location information or travel history of the user 622 may be used to identify or determine the user's daily trips or associated destinations.

[0105] 6A and 6B show a single user 622 and its respective devices 602 and vehicles 612, it should be understood that there may be multiple users and their respective devices and vehicles. The location information and movement information of each user may be used to determine the movement information of each user. The location information and movement history of each user may be aggregated and used to indicate an average or median value of one or more transportation metrics for a given area, such as a neighborhood, town, city, county, state, etc.

[0106] Each user gives authorization for applications to access their location information and movement information. The user may set permissions on the application to indicate what location information and movement history the application may access. The location information may be stored locally on the user device. In some examples, the location information from the user device and / or the navigation application may be shared with, and thus stored by, one or more servers. In yet another example, the location information may be captured and stored by the navigation application. The location information and movement history may be shared with, and stored by, the server only after the user authorizes the sharing of the location information and movement information.

[0107] 7 illustrates an exemplary method for determining a predicted value of one or more transportation metrics of a user based on the user's travel information and transportation mode. The following operations do not necessarily have to be performed in the strict order described below. Rather, various operations can be processed in different orders or simultaneously, and operations may be omitted.

[0108] In block 710, the system may identify at least one first transportation mode of the user's transportation. The user's first transportation mode may be the user's existing transportation mode, such as a type of vehicle utilized by the user. According to some examples, the first transportation mode may include one or more transportation modes. For example, the existing transportation mode may include multiple vehicles, a combination of vehicles, bicycles, public transportation, etc. In some examples, the system may receive an indication of a current transportation mode from the user. Additionally or alternatively, the system may determine the first transportation mode based on the user's travel history, such as by detecting public transportation routes, toll routes, ride shares, etc.

[0109] At block 720, the system may determine travel information including the location of the trip start point and one or more destinations. The user's travel history or travel information may be based on location information provided by a personal device, a navigation system, a map application, etc. The location information may be, for example, data collected by a device having a location sensor, such as a Global Positioning System (GPS), that indicates locations to which the user has traveled. In some examples, the location information may include destinations identified in emails, calendar appointments, etc. to which the user has granted the system access. The locations identified by the location information may correspond to destinations, such as the location of the trip destination or associated destinations.

[0110] At block 730, the system may determine a value of a transportation metric for the at least one first transportation mode based on the travel information and the at least one first transportation mode. When determining a value of the transportation metric for the first transportation mode, the system may take into account expenses associated with the first transportation mode, such as car payments, insurance rates, refueling costs, standard maintenance costs, etc. Standard maintenance costs may include, for example, regular oil changes, tire rotations, brake maintenance, etc. According to some examples, the system may identify attributes of the first transportation mode and use the identified attributes when determining the value of the transportation metric. The attributes may include, for example, transportation type, vehicle model, manufacturer, capacity, model year, fuel efficiency, initial cost estimate, etc.

[0111] In block 740, the system may determine at least one alternative transportation mode based on the trip information. The system may receive an indication of required attributes for the alternative transportation mode. For example, the system may receive input corresponding to the number of seats required by the user. In some examples. The system may receive input regarding attributes corresponding to the type of transportation, the model of the vehicle, the manufacturer, the capacity, the year, the fuel efficiency, the initial cost, etc. In some examples, the system may automatically determine the attributes of the alternative transportation mode. For example, the system may receive input regarding a vehicle of interest and populate the attributes of the vehicle to determine the value of one or more of its transportation metrics. According to some examples, the system may use the identified attributes to identify characteristics of the first transportation mode and determine the alternative transportation mode.

[0112] At block 750, the system may determine a value of a transportation metric for the at least one alternative transportation mode based on the trip information and the at least one alternative transportation mode. When determining a value of the one or more transportation metrics for the alternative transportation mode, the system may take into account expenses associated with the alternative transportation mode, such as car payments, insurance rates, refueling costs, standard maintenance costs, etc. Standard maintenance costs may include, for example, regular oil changes, tire rotations, brake maintenance, etc. According to some examples, the system may identify attributes of the alternative transportation mode and use the identified attributes when determining a value of the one or more transportation metrics. The attributes may include, for example, transportation type, vehicle model, manufacturer, capacity, model year, fuel efficiency, initial cost estimates, etc.

[0113] In block 760, the system may compare values ​​of the transportation metric associated with the at least one alternative transportation mode with values ​​of the transportation metric of the at least one first transportation mode. The comparison may be used to identify which transportation mode provides an improved value of the transportation metric. In some examples, the system may select the alternative transportation mode based on a transportation mode that is likely to reduce the value of the transportation metric compared to the user's current transportation mode. In this way, the user can obtain an objective measure of how the alternative transportation mode will affect the value of the transportation metric, assuming all routine trips are maintained.

[0114] At block 770, the system may provide for output an indication to identify at least one alternative mode of transportation. According to some examples, the system may output a suggestion to reduce the value of the transportation metric to the predicted value of the transportation metric. The output may include an alternative vehicle suggestion to improve the value of the transportation metric when traveling to the destination location, such as switching from the first mode of transportation to the alternative mode of transportation. For example, the system may suggest selling the current vehicle and purchasing a vehicle that uses alternative fuels, such as an electric or hybrid model, or a vehicle that is more cost-effective to park at regular destination points.

[0115] Unless otherwise specified, the above-mentioned alternative examples are not mutually exclusive and may be implemented in various combinations to realize unique advantages. Since these and other variations and combinations of the features discussed above may be utilized without departing from the subject matter defined by the claims, the above description of the embodiments should be taken as illustrative rather than limiting the subject matter defined by the claims. In addition, the provision of examples described herein and clauses such as "such as," "including," and the like should not be construed as limiting the subject matter of the claims to any particular examples, but rather the examples are intended to show only one of many possible embodiments. Furthermore, the same reference numbers in different drawings may identify the same or similar elements. [Explanation of symbols]

[0116] 102 Applications 116 First Transportation Method 118 Travel Information 120 Refueling Cost 122 Expenses 124 Carbon Footprint 126 Proposals for Reducing Transportation Metrics 143 Manufacturer 144 Model 145 year model 146 Mileage 147 seats 341 attributes 342 User Input Module 431 Internal Combustion Engine ("ICE") Vehicles 432 Hybrid / Electric Vehicles 433 Ride Share Options / Taxi 434 Two-wheeled options 435 Subway / Train 436 Bus 437 Carpooling options 438 Manual Bicycle 502 Application 517 Alternative Transportation Methods 518 Travel information, distance 520 Refueling Cost 522 Expenses 524 Carbon Footprint 526 Proposals for Reducing Transportation Metrics 600 System 602 Devices 612 Transportation methods, vehicles 620 Network 622 users 630 Server Computing Device 632 processor 640 Storage System 642 Memory 652 command 662 Data 672 Output, Display 682 User Input 692 Position Sensor

Claims

1. identifying, by one or more processors, at least one first transportation mode of the user; determining, by the one or more processors, travel information including a trip start point and one or more destination locations; determining, by the one or more processors based on the travel information and the at least one first transportation mode, a value of a transportation metric for the at least one first transportation mode; determining, by the one or more processors, at least one alternative mode of transportation based on the travel information; determining, by the one or more processors based on the travel information and the at least one alternative transportation mode, a value of the transportation metric for the at least one alternative transportation mode; comparing, by the one or more processors, the value of the transportation metric associated with the at least one alternative transportation mode to the value of the transportation metric of the at least one first transportation mode; providing, by the one or more processors, for output an indication identifying the at least one alternative mode of transportation; Including, 4. A method according to claim 3, wherein the value of the transportation metric associated with the at least one alternative transportation mode is improved compared to the value of the transportation metric associated with the at least one first transportation mode.

2. The method of claim 1 , wherein at least one of the at least one first mode of transportation and at least one of the at least one alternative mode of transportation are different from each other.

3. The method of claim 1 or 2, wherein the transportation metrics include at least one of total distance traveled, total travel time, carbon footprint, operating cost, or vehicle operational data.

4. The method of claim 3 , wherein the vehicle operational data includes vehicle fuel type, vehicle fuel usage, estimated vehicle maintenance, and government incentives associated with the vehicle.

5. The method of claim 1 , wherein determining the transportation metric is further based on toll information, parking options, and refueling options.

6. the at least one alternative mode of transportation includes a plurality of alternative modes of transportation; 6. The method of claim 1, further comprising: ranking, by the one or more processors, the plurality of alternative transportation modes based on the value of the transportation metric associated with each of the alternative transportation modes, wherein the ranking of the plurality of alternative transportation modes is based on lowest cost to a user to highest cost to a user.

7. The method of claim 6 , further comprising outputting, by the one or more processors, the ranked plurality of alternative transportation modes.

8. 8. The method of claim 1, further comprising providing, by the one or more processors, for output, at least one of the value of the transportation metric of the first transportation mode and the value of the transportation metric of the at least one alternative transportation mode.

9. 9. The method of claim 1, wherein receiving the location information corresponding to the movement history of the user further comprises receiving, by the one or more processors, the location information from at least one of a memory of a user device, one or more servers, or a navigation application.

10. determining the one or more pieces of movement information, determining, by the one or more processors based on the location information, a pattern or frequency of a plurality of trips; identifying one or more routine trips based on the determined pattern or frequency of the plurality of trips; 10. The method of any one of claims 1 to 9, comprising:

11. 11. The method of claim 10, when identifying the one or more routine trips based on the frequency of the plurality of trips, the method further comprising: comparing a trip frequency of each of the plurality of trips to a threshold frequency.

12. The method of claim 11 , wherein the respective trip is identified as a routine trip when the frequency of the respective trip is greater than the threshold frequency.

13. 13. The method of claim 1, further comprising determining, by the one or more processors, attributes of the at least one first mode of transportation.

14. The method of claim 13 , further comprising automatically identifying the attributes of the at least one first mode of transportation.

15. 15. The method of claim 13 or 14, wherein at least one attribute of the alternative mode of transport corresponds to at least one attribute of the first mode of transport.

16. 16. The method of claim 15, wherein when identifying the at least one alternative transportation mode with an improved transportation metric, alternative transportation modes that do not include the at least one attribute of at least one of the at least one first transportation modes are excluded from consideration.

17. 17. The method of claim 13, wherein determining at least one alternative mode of transportation further comprises identifying, by the one or more processors, at least one available alternative mode of transportation having the determined attributes.

18. 18. The method of claim 1, wherein determining at least one alternative mode of transportation further comprises receiving, by the one or more processors, a user selection.

19. one or more processors, Identifying at least a first mode of transportation of the user; determining travel information including a trip start point and one or more destination locations; determining a value of a transportation metric for the at least one first transportation mode based on the travel information and the at least one first transportation mode; determining at least one alternative mode of transportation based on the travel information; and determining a value of the transportation metric for the at least one alternative transportation mode based on the travel information and the at least one alternative transportation mode; comparing the value of the transportation metric associated with the at least one alternative transportation mode to the value of the transportation metric of the at least one first transportation mode; providing for output an indication identifying said at least one alternative mode of transportation; one or more processors configured to The system, wherein the value of the transportation metric associated with the at least one alternative transportation mode is improved compared to the value of the transportation metric associated with the at least one first transportation mode.

20. 20. The system of claim 19, wherein at least one of the at least one first mode of transportation and at least one of the at least one alternative mode of transportation are different from each other.

21. 21. The system of claim 19 or 20, wherein the transportation metrics include at least one of total distance traveled, total travel time, carbon footprint, operating costs, or vehicle operational data.

22. 22. The system of claim 21, wherein the vehicle operational data includes vehicle fuel type, vehicle fuel usage, estimated vehicle maintenance, and government incentives associated with the vehicle.

23. 23. The system of claim 19, wherein determining the transportation metric is further based on toll information, parking options, and refueling options.

24. the at least one alternative mode of transportation includes a plurality of alternative modes of transportation; 24. The system of any one of claims 19 to 23, wherein the system is further configured to rank, by the one or more processors, the plurality of alternative transportation modes based on the value of the transportation metric associated with each of the alternative transportation modes, and wherein the ranking of the plurality of alternative transportation modes is based on lowest cost to a user to highest cost to a user.

25. 25. The system of claim 24, further comprising outputting, by the one or more processors, the ranked plurality of alternative transportation modes.

26. 26. The system of claim 19, further comprising providing, by the one or more processors, for output, at least one of the value of the transportation metric of the first transportation mode and the value of the transportation metric of the at least one alternative transportation mode.

27. 27. The system of claim 19, wherein receiving the location information corresponding to the movement history of the user further comprises receiving, by the one or more processors, the location information from at least one of a memory of a user device, one or more servers, or a navigation application.

28. the one or more processors: determining a pattern or frequency of a plurality of trips based on the location information; identifying one or more routine trips based on the determined pattern or frequency of the plurality of trips; and 28. The system of claim 19, configured to determine one or more pieces of movement information by:

29. 30. The system of claim 28, wherein when identifying the one or more routine trips based on the frequency of the plurality of trips, the system is further configured to compare a trip frequency of each of the plurality of trips to a threshold frequency.

30. 30. The system of claim 29, wherein the respective trip is identified as a routine trip when the frequency of the respective trip is greater than the threshold frequency.

31. 31. The system of any one of claims 19 to 30, further configured to determine attributes of the at least one first mode of transport.

32. 32. The system of claim 31, further configured to automatically identify the attributes of the at least one first mode of transportation.

33. 33. The system of claim 31 or 32, wherein at least one attribute of the alternative mode of transport corresponds to at least one attribute of the first mode of transport.

34. 34. The system of claim 33, wherein when identifying the at least one alternative transportation mode with an improved transportation metric, all alternative transportation modes that do not include the at least one attribute of at least one of the at least one first transportation modes are excluded from consideration.

35. 35. The system of claim 31, wherein determining the at least one alternative mode of transportation further comprises identifying, by the one or more processors, at least one available alternative mode of transportation having the determined attributes.

36. 36. The system of claim 19, wherein determining at least one alternative mode of transportation further comprises receiving, by the one or more processors, a user selection.

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