ANALYTICS SYSTEM AND METHOD FOR BUILDING AN AUTONOMOUS VEHICLE DEPLOYMENT READINESS MODEL FOR A GEOGRAPHIC AREA

Abstract

An automated computerized system and method that evaluates and determines readiness of multiple geographic locations for employment of autonomous vehicles. The system receives and analyzes input data for the locations based on evaluation of (a) weather constraint data, (b) population size constraint data, and (c) speed limit constraint data. In the next phase, it evaluates and applies (1) total food related delivery vehicles data, (2) total ride hail vehicles data, (3) historic growth rate for each type of vehicle; and (4) turnover rate for each type of vehicle. In the next phase, the system evaluates and applies (i) economic indicators, (ii) city transportation indicators, and (iii) city operator metrics. After processing and automatically combining and evaluating the data based on the constraints and indicators of the various phases, the system computes the resulting values, indicating readiness for the deployment of automated vehicles and projected implementation timeline for the multiple locations and provides the result for use in implementing the AV readiness programs at the researched locations.

Claims

1. An automated computerized system for evaluating readiness of one or more geographic locations for employment of autonomous vehicles at the respective geographic locations, comprising: at least one processor executing a plurality of computer instructions stored in memory, causing the processor to perform: (1) receiving and automatically processing input data for one or more geographic locations; (2) evaluating the received input data for one ore more geographic locations based on evaluation of a weather constraint data, a population size constraint data and a speed limit constraint data as part of a first phase of the automated processing and analysis; (3) evaluating input data for one ore more geographic locations selected after the first phase, wherein the evaluation comprises determining and applying a total food related delivery vehicles data, total ride hail vehicles data, a historic growth rate for each type of vehicle and a turnover rate for each type of vehicle, as part of a second phase of the automated processing and analysis; (4) evaluating input data for one ore more geographic locations selected after the second phase, wherein the evaluation comprises determining and applying one or more economic indicators, city transportation indicators and city operator metrics, as part of a third phase of the automated processing and analysis; (5) automatically combining and evaluating the data based on the constraints and indicators of the first, second and third phases and computing results of the evaluations; (6) providing and displaying a calculated value indicating readiness for the deployment of automated vehicles and a projected implementation timeline for one or more evaluated geographic locations; and (7) utilizing the calculated value to implement an automated vehicle use or vehicle introduction program in one or more of the evaluated geographic locations.

2. The system of claim 1, wherein the evaluation of the weather constraint data comprises evaluation and selection for further processing of one or more geographic locations that have a higher percentage of days without heavy rain, heavy snow, or fog, based on one or more weather data sources.

3. The system of claim 1, wherein the evaluation of the population constraint data comprises evaluation and selection for further processing of one or more geographic locations that have a higher concentration of human population per square mile and an overall population greater than a minimal set limit, based on one or more census data sources.

4. The system of claim 1, wherein the evaluation of the speed limit constraint data comprises evaluation and selection for further processing of one or more geographic locations wherein at least 90 percent of speed limitations zones within the location are below 45 miles an hour.

5. The system of claim 1, wherein at least one processor executing a plurality of computer instructions stored in memory further causes the processor to reduce the number of evaluated geographic locations after performing the first phase and after the second phase of the automated processing and analysis.

6. The system of claim 1, wherein the food related delivery vehicles data evaluated by the system comprises data related to a grocery, a restaurant, a local and a last mile delivery vehicles.

7. The system of claim 1, wherein the food related delivery vehicles data comprises data for a plurality of vehicle that deliver food from restaurants, and wherein the grocery delivery vehicles data comprises data for a plurality of vehicles that deliver groceries from multiple food stores and supermarkets.

8. The system of claim 6, wherein the last minute vehicles data utilizes a market share of UPS, FedEx, DHL, Amazon and other regional delivery services.

9. The system of claim 1, wherein the ride hail vehicles data evaluated by the system comprises vehicle data pertaining to taxis, TaaS vehicles, limousines and other passenger vehicles, and public transportation that carry multiple passengers.

10. The system of claim 1, further comprising evaluation of data pertaining to autonomous vehicles that are used as food related delivery vehicles or ride hail vehicles, and wherein a lifespan of the autonomous vehicles is set to 4 years of use.

11. The system of claim 1, wherein the turnover rate for at least one type of vehicle is calculated based on a number of vehicles of the same or similar type that are replaced each year.

12. The system of claim 1, wherein the determining and applying the economic indicators includes applying data and analysis of a luxury goods sales and a local shopping sales.

13. The system of claim 1, wherein the determining and applying the city transportation constraints includes applying data and analysis of a vehicle miles travelled, a public transport share of travel and an added travel time from a congestion data and evaluation at the location.

14. The system of claim 1, wherein the determining and applying the city operator metrics includes applying data and analysis of a number of taxi companies and a number of local delivery companies at the location.

15. An automated computerized method for evaluating readiness of one or more geographic locations for employment of autonomous vehicles comprising: (1) receiving and automatically processing input data for one or more geographic locations; (2) evaluating the received input data for one ore more geographic locations based on evaluation of a weather constraint data, a population size constraint data and a speed limit constraint data as part of a first phase of the automated processing and analysis; (3) evaluating input data for one ore more geographic locations selected after the first phase, wherein the evaluation comprises determining and applying a total food related delivery vehicles data, total ride hail vehicles data, a historic growth rate for each type of vehicle and a turnover rate for each type of vehicle, as part of a second phase of the automated processing and analysis; (4) evaluating input data for one ore more geographic locations selected after the second phase, wherein the evaluation comprises determining and applying an economic indicators, a city transportation indicators, and a city operator metrics, as part of a third phase of the automated processing and analysis; (5) automatically combining and evaluating the data based on the constraints and indicators of the first, second and third phases and computing results of the evaluations; (6) providing and displaying a calculated value indicating readiness for the deployment of automated vehicles and a projected implementation timeline for one or more evaluated geographic locations; and (7) utilizing the calculated value to implement an automated vehicle use or vehicle introduction program in one or more of the evaluated geographic locations.

16. The method of claim 15, wherein the evaluating the weather constraint data comprises evaluating and selecting for further processing one or more geographic locations that have a higher percentage of days without heavy rain, heavy snow, or fog, based on one or more weather data sources.

17. The method of claim 15, wherein the evaluating the population constraint data comprises evaluating and selecting for further processing one or more geographic locations that have a higher concentration of human population per square mile and an overall population greater than a minimal set limit, based on one or more census data sources.

18. The method of claim 15, wherein the evaluating the speed limit constraint data comprises evaluating and selecting for further processing one or more geographic locations where at least 90 percent of speed limitations zones within the location are below 45 miles an hour.

19. The method of claim 15, further comprising: reducing the number of evaluated geographic locations after performing the first phase and after the second phase of the automated processing and analysis.

20. The method of claim 15, wherein the evaluation of food related delivery vehicles data comprises evaluation of data related to a plurality of grocery, restaurant, local and last mile delivery vehicles.

21. The method of claim 15, wherein the evaluation of food related delivery vehicles data comprises evaluation of data related to a plurality of vehicle that deliver food from restaurants, and wherein the grocery delivery vehicles data comprises data for a plurality of vehicles that deliver groceries from multiple food stores and supermarkets.

22. The method of claim 21, wherein the last minute vehicles data utilizes a market share of UPS, FedEx, DHL, Amazon and other regional delivery services.

23. The method of claim 15, wherein the evaluating of the ride hail vehicles data comprises evaluating vehicle data pertaining to taxis, TaaS vehicles, limousines and other passenger vehicles, and public transportation that carry multiple passengers.

24. The method of claim 15, further comprising evaluating data pertaining to a plurality of autonomous vehicles that are used as food related delivery vehicles or ride hail vehicles, and wherein a lifespan of the autonomous vehicles is set to 4 years of use.

25. The method of claim 15, wherein the turnover rate for at least one type of vehicle is calculated based on a number of vehicles of the same or similar type that are replaced each year.

26. The method of claim 16, wherein the determining and applying the economic indicators includes applying data and analysis of a luxury goods sales and a local shopping sales.

27. The method of claim 15, wherein the determining and applying the city transportation constraints includes applying data and analysis of a vehicle miles travelled, a public transport share of travel and an added travel time from a congestion data and evaluation at the location.

28. The method of claim 1, wherein the determining and applying the city operator metrics includes applying data and analysis of a number of taxi companies and a number of local delivery companies at the location.

29. An automated computerized method for evaluating readiness of one or more geographic locations for employment of autonomous vehicles comprising: (1) receiving at a computer server having one or more processors and connected to a network an input data for one or more geographic locations; (2) evaluating the received input data for one ore more geographic locations based on evaluation of a weather constraint data, a population size constraint data and a speed limit constraint data as part of a first phase of the automated processing and analysis; (3) evaluating input data for one ore more geographic locations selected after the first phase, wherein the evaluation comprises determining and applying a total food related delivery vehicles data, total ride hail vehicles data, a historic growth rate for each type of vehicle and a turnover rate for each type of vehicle, as part of a second phase of the automated processing and analysis; (4) evaluating input data for one ore more geographic locations selected after the second phase, wherein the evaluation comprises determining and applying an economic indicators, a city transportation indicators, and a city operator metrics, as part of a third phase of the automated processing and analysis; (5) automatically combining and evaluating the data based on the constraints and indicators of the first, second and third phases and computing results of the evaluations; (6) storing or displaying a calculated value indicating readiness for the deployment of automated vehicles and a projected implementation timeline for one or more evaluated geographic locations; and (7) transmitting the calculated value to another computer or mobile device; whereby the transmitted calculated value is utilized to implement an automated vehicle use or vehicle introduction program at one or more of the evaluated geographic locations.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The following detailed description, given by way of example and not intended to limit the present invention solely thereto, will best be appreciated in conjunction with the accompanying drawings, wherein like reference numerals denote like elements and parts, in which:

[0025] FIG. 1 illustrates a general structure, organization and operation of Phase 1, the city selection process for the US market and the binary selection process based on such factors as speed, weather, and population density metrics for a given city or geographic area, and for determining whether to include the evaluated city (or cities) or a geographic area in the Phase 2 analysis in accordance with at least one embodiment.

[0026] FIG. 2 illustrates a general structure, organization and operation of Phase 2, the city prioritization process for the US market, and how the autonomous vehicle volume opportunities are calculated each year for a given city or a geographic area in accordance with at least one embodiment.

[0027] FIG. 3 illustrates a model input for Phase 2, the city prioritization process for the US market in accordance with at least one embodiment.

[0028] FIGS. 4A-B illustrate model output for Phase 2, the city prioritization process for the US market based on the inputs shown in FIG. 3, as well as cumulative opportunity over the 9-year period in accordance with at least one embodiment.

[0029] FIG. 5 illustrates the modeling factors and process for the analysis of city or geographic area readiness for the autonomous vehicle deployment in accordance with at least one embodiment.

[0030] FIG. 6 illustrates a project delivery plans and an estimated delivery of autonomous vehicles for a city or geographic area based on the urban metropolitan statistical area core population in accordance with at least one embodiment of the present invention.

DETAILED DESCRIPTION

[0031] The description of multiple phases of the automated Readiness Model evaluation in accordance with one or more examples is provided below with reference to FIGS. 1-6. The examples of various analysis and results generated utilizing the Readiness Model evaluation embodiment(s) of the present invention are provided in Appendix A.

[0032] Phase 1City Selection

[0033] In the first phase (Phase 1), a computerized system utilizes one or more processors that execute compute instructions (stored in computer memory) to model and automatically evaluate the Readiness Model for all metropolitan statistical areas (MSAs) on a binary basis (go/no-go criteria). In at least one embodiment, there are three main criteria that define if a city (or geographic area) should be further evaluated and go through the prioritization and market dynamics phases. As illustrated in FIGS. 1 and 5, the evaluated constrains that are evaluated by the computer processor executing the computer instruction that implement the Readiness Model (in accordance with one or more inventions) may include automated evaluation and analysis of: (1) weather constraints (12 in FIGS. 1 and 520 in FIG. 5); (2) population size constraints (13 in FIGS. 1 and 530 in FIG. 5); and (3) speed limits constraints (11 in FIGS. 1 and 510 in FIG. 5).

[0034] (1) Weatherearly versions of autonomous vehicles are assumed to not be able to function (or too risky to operate) on days of heavy rain, heavy snow, and fog due to lidar constraints. To account for this, in one or more embodiments, the Readiness Model evaluates only the cities or geographic locations with high percentage (ex. 90%+) days without heavy rain, heavy snow, or fog, as classified by Weather Source. Thus, only the locations that are determined to have a large number of days without rain, snow or fog are considered further in the automated analysis. In one or more embodiments, the geographic locations with poor weather conditions are cut off from the analysis and are rejected or placed at the lower priority and feasibility for AV deployment. This automated cut-off (or constraint) allows for more efficient analysis and processing of multiple locations that are better suited for the AV deployment in terms of weather conditions.

[0035] (2) Population Size and Densityin order to have a viable business opportunity, in accordance with one or more embodiments, only cities with greater than X (for example, 7,500) people per square mile and over Y (for example, 50,000 total) population within Z-mile (for example, 5-mile) radius of town hall may be further considered in the modeling and further processing. In some embodiment, the data can be taken from the 2010 or later U.S. Census Bureau publications. This automated cut-off (or constraint) allows for more efficient analysis and processing of multiple locations that have the necessary population and population density that are better suited for the AV deployment particularly, in processing and evaluation of multiple geographic locations.

[0036] (3) Travel/Maximum Speeddue to lidar or legal constraints, early autonomous vehicles cannot travel safely faster, or not permitted to travel faster, than at the speed of 45 MPH. In at least one embodiment, only cities or geographic areas that are further considered in the analysis are areas where the 90.sup.th percentile speed limit within Metropolitan Statistical Area (MSA) is not greater than 45 MPH. In one embodiment, the vehicle trace data and speed limit data can be taken from Zubie for March 2016 (or later) and used for this analysis. This automated cut-off (or constraint) allows for more efficient analysis and processing of multiple locations that have lower speed limits that can accommodate the lidar, legal or other constraints on the utilization and AV deployment, and avoid the analysis of high speed geographic areas that are poorly suited for AV deployment, where the AV speed is limited to 45 MPH.

[0037] The application of the weather, population and speed limit constraints to a list of 375 U.S. Metropolitan Statistical Areas (MSAs) is shown in FIGS. 1 and 5. By applying the speed constraints 11 (also shown as 510 in FIG. 5) the list of 375 MSA may be reduced by 100. Application of the subsequent weather constraints 12 (also shown as 520 in FIG. 5) would reduce the number of possible MSA to 46, and the application of the population constraints 13 (also shown as 530 in FIG. 5) may further reduce the list of MSA to 32.

[0038] Phase 2City Prioritization

[0039] Once relevant cities (or geographic areas) have been selected (or have passed the automatic application of constraints and cut off limits) in phase 1, the Readiness Model evaluates and processes the selected geographic locations though the prioritization phase (Phase 2). As shown in FIGS. 2 and 5, the following aspects of a city's vehicle volumes 22 are incorporated and automatically evaluated (ex., setting for 2016): (a) taxis; (b) TaaS vehicles; (c) food delivery; (d) grocery delivery; (e) limousines; (f) last minute delivery vehicles; and (g) shuttle buses.

[0040] (a) Taxis (22 in FIGS. 2 and 560 in FIG. 5)registered taxis in the city's urban core are included in the volume determination of the prioritization phase. Data regarding number of active taxis in an area can be obtained from the local taxi and limousine commission.

[0041] (b) TaaS (22 in FIGS. 2 and 550 in FIG. 5)registered transportation-as-a-service vehicles (i.e. Uber, Lyft, etc.). Data obtained from the local taxi and limousine commission, where available. If this data was not readily available, a regression based on available data for other cities was used. Each city was classified as either saturated with TaaS vehicles (Uber or Lyft has operated in that city for 5+ years) or unsaturated with TaaS vehicles. A regression formula is built for each type of city, correlating population size with number of TaaS vehicles. Accordingly, the city's population without available data was put through the regression and an assumed TaaS volume was assigned.

[0042] (c) Food Delivery (21 in FIG. 2)number of vehicles delivering food from restaurants. Based on prior reports on the industry, the analysis may assume that each restaurant has certain number (for example, 3) delivery vehicles, and food delivery-based vendors, like Domino's, have more (for example, 4) delivery vehicles. Using registered restaurant data from individual city databases, certain volumes can be assumed and used in calculations for each city or geographic area.

[0043] (d) Grocery Delivery (21 in FIG. 2)number of vehicles delivering groceries from a supermarket. Based on data available from grocery delivery services Postmates and Instacart, it can be assumed that each supermarket supplies ten vehicles. (Different set numbers can also be used when supported by historical data). Using registered business license data from individual city databases, certain volumes can be assumed and used in calculations for each city or geographic area.

[0044] (e) Limousines (22 in FIG. 2)registered limousine/black cars in the city's urban core. Data can be obtained from the local taxi and limousine commission.

[0045] (f) Last Mile Delivery vehicles (21 in FIG. 2)local delivery vehicles in an urban core. These vehicles are designed for final delivery of packages to consumers and businesses. Based on available information for 5 cities, a regression was built to find the correlation between delivery vehicle volumes and registered local delivery drivers from the Bureau of Labor Statistics for the MSA. Once the regression equation was built, each MSA's registered local delivery drivers metric was used to assume the volume of delivery vehicles.

[0046] (g) Shuttle Buses (22 in FIG. 2)vehicle volumes for van-like shuttles used to move people around a city. Based on population size and density, the number of shuttles per city was assumed.

[0047] In at least one embodiment, the volumes used in the determination can be taken for the year 2016. The Readiness Model assesses volumes over a 9-year period and therefore requires growth rates for each individual use case.

[0048] Referring to FIGS. 2 and 5, in at lest one embodiment, the growth rates 23 in FIG. 2 for each type of vehicle may be calculated as follows:

[0049] a) Taxis (shown as 564 in FIG. 5)historical growth rates of taxis in each city, for example, using 2006-2016 as the time horizon for analysis;

[0050] b) TaaS (shown as 554 in FIG. 5)assumed growth rates varied by length of time a TaaS service had been present in a given city. For example, if a service (i.e. Uber) had been present for 5+ years, the growth rates can be assumed to be 7% based on the historical data. If the city did not have a TaaS service or had it for fewer than 5 years, the growth rate can be assumed to be 20% yoy.

[0051] c) Food Deliverythe growth rate of the food delivery services can be utilized by the system in one or more embodiments. For example, the automated analysis can utilize the growth rate based on corresponding growth rates of Seamless, Eat24, and Postmates services.

[0052] d) Grocery Deliverythe growth rate of the grocery delivery services for supermarkets and online supermarket purchasing platforms can be utilized by the system in one or more embodiments. For example, the automated analysis can utilize the growth rate based on corresponding growth rates of Instacart and Whole Foods. The calculation and use of the projected Food and Grocery Delivery Growth rate 574 is shown in FIG. 5.

[0053] e) Limousinehistorical growth rates of limousines in each city, using 2006-2016 as the time horizon for analysis.

[0054] f) Last Mile Delivery vehicleshistorical e-commerce growth rates were the basis for the assumed growth rate of last mile delivery.

[0055] g) Shuttle Bushistorical growth rates of shuttles in each city, using 2006-2016 as the time horizon for analysis.

[0056] Each of these volume opportunities may then multiplied by an annual turnover rate 24 in FIG. 2 for each type of vehicle (the number of vehicles replaced each year from the fleet). In one example, the following rates have been utilized in the automated calculations:

[0057] a) Taxis (shown as 562 in FIG. 5)historical turnover rates from New York City, Los Angeles can be used as proxies for all city (and areas) assumptions.

[0058] b) TaaS (shown as 552 in FIG. 5)turnover rate mirrored taxis, given that annual vehicle miles travelled is relatively consistent for both groups.

[0059] c) Food Deliverycan be based on average vehicle miles travelled by one or more Dominos delivery vehicles.

[0060] d) Grocery Deliverycan be based on an average vehicle miles travelled by one or more Dominos delivery vehicles. Use of the projected Food and Grocery Delivery turnover rate is shown as 574 in FIG. 5.

[0061] e) LimousineIBIS World limo report set average lifespan for a limousine at 3 years, and this estimate may be used in calculations.

[0062] f) Last Mile Delivery vehiclesblended average of lifespan for delivery truck (for example, from the UPS data about delivery trucks) and delivery van (for example, the DHL data about delivery vans).

[0063] g) Shuttle Busin one example, the same turnover rate was assumed as for limousines.

[0064] All autonomous vehicles may be assumed to have a 4-year lifespan and re-plated accordingly.

[0065] Finally, the Readiness Model examined individual partners in each of the use case areas for delivery and ride hailing. For example, the following partners and their corresponding shares may be considered in the Readiness Model in accordance with at least one embodiment: [0066] UPSmarket share of last mile delivery across US, APAC, and EU [0067] FedExmarket share of last mile delivery across US, APAC, and EU [0068] DHLmarket share of last mile delivery across US, APAC, and EU [0069] Amazonassumptions validated by Amazon reports [0070] Other Regional Deliveryremainder of vehicles left on road [0071] Ubermarket share across regions (Uber was substituted by regional players in APAC and EU, such as Didi and Kareem) [0072] Dominosassumed 4 cars per store in each city [0073] Whole Foodsassumed 10 cars per store in each city

[0074] As shown in FIG. 2, the output of this phase may be the volume potentials (addressable autonomous vehicle opportunity) across each of these use case areas by year and cumulatively over a 9-year period.

[0075] Phase 3City Market Dynamics

[0076] In some embodiments, other additional indicators may be incorporated into the model and used in the automated calculations, to provide nuance for the opportunity in Phase 3 of the City Readiness Model. These indicators were based primarily in the following three categories: (1) Economic indicators (shown as 590 in FIG. 5); (2) City Transportation indicators (shown as 592 in FIG. 5) and (3) Number of operators (city operator metrics) for ride hail and delivery companies (shown as 594 in FIG. 5).

[0077] (1) Economic Indicators (590)given that autonomous vehicles will come at a price premium, ensuring a city has a strong economy will be important. The following factors may be further considered and included in the model as part of the Economic Indicators (590), in accordance with at least one embodiment:

[0078] a. Luxury Goods sales. The reason to include this factor in the model for AV vehicles is because luxury users tend to be early adopters of new technology and are willing to pay for convenience.

[0079] b. Local Shopping sales. The reason to include this factor in the model for AV vehicles is because most delivery will occur in local situations so strong local sales are important precursors for autonomous delivery services.

[0080] (2) City Transportation (592)a city with higher needs for delivery and ride hailing fleets will be more likely to adopt autonomous vehicles. The following factors may be further considered and included in the model in accordance with at least one embodiment as part of the Citi Transportation (592):

[0081] a. VMT travelledthe more vehicle miles traveled in a city, the more movement is available to be replaced by autonomous vehicles.

[0082] b. Public Transport sharelower public transportation use means more individuals using ride hail services today and more likely to use new services.

[0083] c. Added Travel Time from Congestiongiven autonomous vehicle benefits of congestion reduction, the more time added to commutes and delivery from congestion, the higher the appetite for autonomous vehicle fleets.

[0084] (3) City Operator Metrics or the number of operators for ride hail and delivery companies (594) is also included in the model for AV vehicles in at least one embodiment. Typically, more operators means lower number of vehicles per operator. The following factors may also be considered and included in the model as part of the Citi Operator Metrics (594):

[0085] a. Number of taxi companiesmore concentrated competitive dynamics means partnering with a single operator will ensure higher volumes.

[0086] b. Number of local delivery companiesmore concentrated competitive dynamics means partnering with a single operator will ensure higher volumes.

[0087] The model automatically evaluates the above-referenced data and constraining factors. Upon completion of the third phase of analysis, the output with a detailed description of the opportunity size for a city and a given combination of cities as they are activated in each year may be provided on a display screen or transmitted to a mobile device or computer processor.

[0088] Referring to FIG. 3, the table on the left is the assumed rollout that can be altered by the user to determine which city or area is activated in a given year and thus changing the total number of autonomous vehicles required to meet opportunity demand. The AV Lifespan inputs allow the user to input modified data and change the duration of how long an autonomous vehicle's assumed lifespan would be. The user interface for allowing user selection and modification is shown in FIG. 3. The modification and updated data are automatically processed and evaluated by the computer processor that executes computer instructions in accordance with at least one embodiment. The modified data is processed through and by the model set forth below and may modify the results and the readiness evaluation base on the entered changes.

[0089] Referring to FIGS. 4A and B, the graphs show both new annual opportunity (FIG. 4A) for cars 410 and transit 420 based on the inputs indicated in FIG. 3, as well as cumulative opportunity (FIG. 4B) for cars 415 and 425, over the 9-year period. They are broken out by car vs. transit in this embodiment as follows:

[0090] a) Car [0091] Food Delivery [0092] Taxi [0093] TaaS [0094] Grocery Delivery [0095] Limousine

[0096] b) Transit [0097] Transit [0098] Last Mile Delivery [0099] Shuttle Bus

[0100] An example of the results of a modeling and automated processing for utilization of autonomous vehicles in the U.S. in accordance with at least one embodiment (where 375 different cities were evaluated), in EU (29 different cities evaluated) and AP (27 different cities evaluated) is provided in Appendix A. It also lists the top cities that are best suited for the autonomous vehicle adoption based on the model evaluation in accordance with at least one embodiment. Various sources that may be utilized for the data, and the assumptions, based on the indicated data sources, are used in at least one model for the US, EP and AP cities and locations evaluations for utilization of autonomous vehicles, as listed and indicated in Appendix A. As set forth in the Assumptions examples described in Appendix A, the assumptions may be different form different countries and different cities, and the assumptions and calculations may be separated for the vehicles that move people, and vehicles that move goods.

[0101] It will be understood by those skilled in the art that each of the above steps or elements of the system will comprise computer-implemented aspects, performed by one or more of the computer components described herein. For example, any or all of the steps of collection, evaluation, processing and modeling of the frustration factors and data may be performed electronically. In at least one exemplary embodiment, all steps may be performed electronicallyeither by general or special purpose processors implemented in one or more computer systems such as those described herein.

[0102] It will be further understood and appreciated by one of ordinary skill in the art that the specific embodiments and examples of the present disclosure are presented for illustrative purposes only, and are not intended to limit the scope of the disclosure in any way.

[0103] Accordingly, it will be understood that various embodiments of the present system described herein are generally implemented as a special purpose or general-purpose computer including various computer hardware as discussed in greater detail below. Embodiments within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media which can be accessed by a general purpose or special purpose computer, or downloadable through communication networks. By way of example, and not limitation, such computer-readable media can comprise physical storage media such as RAM, ROM, flash memory, EEPROM, CD-ROM, DVD, or other optical disk storage, magnetic disk storage or other magnetic storage devices, any type of removable non-volatile memories such as secure digital (SD), flash memory, memory stick etc., or any other medium which can be used to carry or store computer program code in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer, or a mobile device.

[0104] When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such a connection is properly termed and considered a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media. Computer-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device such as a mobile device processor to perform one specific function or a group of functions.

[0105] Those skilled in the art will understand the features and aspects of a suitable computing environment in which aspects of the invention may be implemented. Although not required, the inventions are described in the general context of computer-executable instructions, such as program modules or engines, as described earlier, being executed by computers in networked environments. Such program modules are often reflected and illustrated by flow charts, sequence diagrams, exemplary displays, and other techniques used by those skilled in the art to communicate how to make and use such computer program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types, within the computer. Computer-executable instructions, associated data structures, and program modules represent examples of the program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represent examples of corresponding acts for implementing the functions described in such steps.

[0106] Those skilled in the art will also appreciate that the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, networked PCs, minicomputers, mainframe computers, and the like. The invention is practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination of hardwired or wireless links) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0107] An exemplary system for implementing the inventions, which is not illustrated, includes a general purpose computing device in the form of a conventional computer, including a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit. The computer will typically include one or more magnetic hard disk drives (also called data stores or data storage or other names) for reading from and writing to. The drives and their associated computer-readable media provide nonvolatile storage of computer-executable instructions, data structures, program modules, and other data for the computer. Although the exemplary environment described herein employs a magnetic hard disk, a removable magnetic disk, removable optical disks, other types of computer readable media for storing data can be used, including magnetic cassettes, flash memory cards, digital video disks (DVDs), Bernoulli cartridges, RAMs, ROMs, and the like.

[0108] Computer program code that implements most of the functionality described herein typically comprises one or more program modules may be stored on the hard disk or other storage medium. This program code, as is known to those skilled in the art, usually includes an operating system, one or more application programs, other program modules, and program data. A user may enter commands and information into the computer through keyboard, pointing device, a script containing computer program code written in a scripting language or other input devices (not shown), such as a microphone, etc. These and other input devices are often connected to the processing unit through known electrical, optical, or wireless connections.

[0109] The main computer that effects many aspects of the inventions will typically operate in a networked environment using logical connections to one or more remote computers or data sources, which are described further below. Remote computers may be another personal computer, a server, a router, a network PC, a peer device or other common network node, and typically include many or all of the elements described above relative to the main computer system in which the inventions are embodied. The logical connections between computers include a local area network (LAN), a wide area network (WAN), and wireless LANs (WLAN) that are presented here by way of example and not limitation. Such networking environments are commonplace in office-wide or enterprise-wide computer networks, intranets and the Internet.

[0110] When used in a LAN or WLAN networking environment, the main computer system implementing aspects of the invention is connected to the local network through a network interface or adapter. When used in a WAN or WLAN networking environment, the computer may include a modem, a wireless link, or other means for establishing communications over the wide area network, such as the Internet. In a networked environment, program modules depicted relative to the computer, or portions thereof, may be stored in a remote memory storage device. It will be appreciated that the network connections described or shown are exemplary and other means of establishing communications over wide area networks or the Internet may be used.

[0111] Calculations and evaluations described herein, and equivalents, are, in an embodiment, performed entirely electronically. Other components and combinations of components may also be used to support processing data or other calculations described herein as will be evident to one of skill in the art. A computer server may facilitate communication of data from a storage device to and from processor(s), and communications to computers. The processor may optionally include or communicate with local or networked computer storage which may be used to store temporary or other information. The applicable software can be installed locally on a computer, processor and/or centrally supported (processed on the server) for facilitating calculations and applications.

[0112] In view of the foregoing detailed description of preferred embodiments of the present invention, it readily will be understood by those persons skilled in the art that the present invention is susceptible to broad utility and application. While various aspects have been described in the context of a preferred embodiment, additional aspects, features, and methodologies of the present invention will be readily discernible from the description herein, by those of ordinary skill in the art. Many embodiments and adaptations of the present invention other than those herein described, as well as many variations, modifications, and equivalent arrangements and methodologies, will be apparent from or reasonably suggested by the present invention and the foregoing description thereof, without departing from the substance or scope of the present invention. Furthermore, any sequence(s) and/or temporal order of steps of various processes described and claimed herein are those considered to be the best mode contemplated for carrying out the present invention.

[0113] It should also be understood that, although steps of various processes may be shown and described as being in a preferred sequence or temporal order, the steps of any such processes are not limited to being carried out in any particular sequence or order, absent a specific indication of such to achieve a particular intended result. In most cases, the steps of such processes may be carried out in a variety of different sequences and orders, while still falling within the scope of the present inventions. In addition, some steps may be carried out simultaneously.

[0114] The foregoing description of the exemplary embodiments has been presented only for the purposes of illustration and description and is not intended to be exhaustive or to limit the inventions to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching.

[0115] The embodiments were chosen and described in order to explain the principles of the inventions and their practical application so as to enable others skilled in the art to utilize the inventions and various embodiments and with various modifications as are suited to the particular use contemplated. Alternative embodiments will become apparent to those skilled in the art to which the present inventions pertain without departing from their spirit and scope. Accordingly, the scope of the present inventions is defined by the appended claims rather than the foregoing description and the exemplary embodiments described therein.

[0116] While certain exemplary aspects and embodiments have been described herein, many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, exemplary aspects and embodiments set forth herein are intended to be illustrative, not limiting. Various modifications may be made without departing from the spirit and scope of the disclosure.

Appendix A

Examples of Resulting Studies and Modeling Based on at Least One Embodiment

[0117]