Application connecting broken-down vehicles with breakdown services

Abstract

My iOS and Android application connects broken-down vehicles with breakdown services based on time and distance (in kilometers). To do this, the algorithm calculates the correlation coefficient between time and distance (5), and the broken-down vehicle is quickly dispatched to the nearest breakdown service. The application considers various factors, such as time versus distance, and optimizes the best choice between these two options (4). The process involves both the user and the breakdown service provider creating an account, then entering their geolocation, and finally confirming the service. Both the user and the breakdown service provider then confirm the trip, and the fare is displayed.

Description

Title of the invention: Application for connecting broken-down vehicles with breakdown services

[0001] My iOS and Android application connects broken-down vehicles with breakdown services based on time and distance (km)

[0002] Technical field to which the invention relates:

[0003] The present invention relates to a mobile geolocation application on iOS and Android which connects vehicles that break down with breakdown services in the field of transport and towing of land vehicles. Detailed description

[0004] The technical field of the application relates to the field of transport and more specifically a vehicle breakdown service throughout the national territory. Previous technique

[0005] There is a free SOS Autoroute app: https: / / voyage.aprr.fr / sos-autoroute

[0006] Created in 2011, SOS Autoroute is an iOS and Android app that replicates the functionality of an emergency call box... but without leaving your vehicle! In case of a breakdown, accident, or difficulty during your journey, there's no need to abandon your car and walk to an emergency call box!

[0007] Operation:

[0008] When a vehicle breaks down; You can also best communicate your location: Highway 112 telephone number, indicating: • direction of traffic, • the reference point located on the central reservation or on the shoulder of the roadway; • For deaf / mute people, you can contact 114 by SMS.

[0009] In addition, breakdown, towing and vehicle recovery operations on highways are strictly regulated: they are carried out by companies subject to approval and the rates are regulated by the "Order of 12 July 2016 relating to the rate of breakdown assistance for light vehicles on highways and roads".

[0010] More specifically, when a vehicle breaks down on the motorway, it must contact the police, who will in turn contact an approved breakdown service as explained above.

[0011] And finally, contact your insurance company to complete the administrative formalities with the approved breakdown service provider.

[0012] In the other scenario where the vehicle breaks down on the road, he can contact the breakdown service of his choice without going through the police.

[0013] Furthermore, did you know that "Every 3 days, an emergency vehicle is involved in an accident on French highways? Our men and women in yellow ensure the safety of our customers 24 / 7 by responding to incidents in the heart of traffic... Therefore, we urge drivers to be extremely vigilant, to slow down, and to move aside if possible when approaching an emergency response zone." https: / / www.groupe.sanef.com / fr / actualite / campagne-s%C3%A9curit%C3%A9-routi%C3%A8re-pi % C3 % A9tons-sur autoroute#:- :text=With % 2065 % 20000 % 20pannes % 20relev % C3%A9es.d'acciden t%20mortel%20sur%20autoroute .

[0014] Disadvantages of the state of the art of prior research:

[0015] Specifically, in the research, it is noted that (The free SOS application) The problem with the Autoroute app is that it's little known, if at all, and not functional, as it suffers from various accessibility issues and is complex, imprecise, and inconcise. Using this app requires going through several steps, each as imprecise as the next (for example, you must first provide your location as accurately as possible: the A112 highway phone number, indicating the direction of travel). It functions more like a kiosk than an application.

[0016] The user who breaks down with his vehicle to date does not have a solution allowing him to ensure his safety and that of others because he has to go through various steps in order to have a breakdown service that will be operational to help him.

[0017] Purpose of the invention:

[0018] The object of the invention is also to mitigate the aforementioned negative externalities on the breakdown and towing transport market, and to provide an innovative and novel solution.

[0019] In particular, it is proposed to connect stakeholders (road users experiencing vehicle breakdowns and breakdown services) via the interconnection of the application (Utiliscar), thereby eliminating the need for constant police dispatches (unless it is a life-threatening emergency). By connecting directly to the platform (Utiliscar application), there is no longer any need to contact the insurance company, as everything will be centralized on the application, with close collaboration with insurers. Users will simply need to enter their first and last name and insurance number, and everything will be centralized.

[0020] With this application and its features, it will also be possible to mitigate hazards and risks. Even vehicles that download this application will be able to report broken-down vehicles on the road in order to prevent and ensure optimal safety and avoid accidents. The use of my application will create positive externalities because it will benefit everyone and be of general interest.

[0021] Detailed description of the embodiment of the invention:

[0022] Mobile geolocation application for iOS and Android in order to put Connecting different actors in the transport of broken-down vehicles, the application (UTLISCAR) uses numerical data such as time and distance to link these data. After analyzing the data, the application decides which broken-down vehicle to retrieve based on the results of the analysis, taking into account these two variables.

[0023] To achieve this, an algorithm will calculate the correlation coefficient between time and distance. The broken-down vehicle will be quickly taken care of by the nearest breakdown service. The application will thus take into account various aspects in terms of selection criteria between time and distance, and will also optimize the best choice between these two factors. The procedure is as follows: the user with the broken-down vehicle (car or motorcycle) and the breakdown service provider will create an account, then enter their geolocation, and finally confirm the service. The fare will then be displayed.

[0024] Furthermore, all these parameters will allow us to calculate the correlation coefficient: This coefficient will vary between -1 and -1. A value close to 1 indicates a strong positive correlation (the greater the distance, the longer the time), a value close to -1 indicates a negative correlation, and a value of 0 indicates little or no correlation. We will demonstrate in the diagrams and examples that the correlation coefficient between time and distance is always positive. This confirms the success of the functional implementation demonstration of the application.

[0025] The aforementioned objectives are essentially achieved when the interdependence of the parties results in a positive correlation between the two variables (time in kilometers and distance), which are the user experiencing a breakdown and the breakdown service provider. To this end, the various calculations below, with concrete examples, will allow for an understanding of the different variables on which I will rely to create an algorithm that will generate the final application of this invention.

[0026] Embodiment of the invention:

[0027] The invention will now be illustrated in more detail by some implementation examples, in no way intended to limit said invention in its scope and spirit.

[0028] Objective:

[0029] We want to verify whether the relationship between distance traveled and time is significant, which could indicate a method for selecting broken-down vehicles based on distance. This means that the nearest breakdown service will be geolocated on an iOS and Android application near the nearest broken-down vehicle and vice versa.

[0030] Before proceeding with this verification, we will first analyze other hypotheses based on time and the pecuniary aspect in order to have a broad overview of varied and diverse hypotheses.

[0031] Context:

[0032] You have five potentially broken-down vehicles with the following distances to be covered for recovery by the tow truck. You earn a certain amount for each broken-down vehicle depending on the distance.

[0033] Example 1:

[0034] Data:

[0035] Let us imagine the distances and associated gains as follows:

[0036] [Tables 1] Broken down vehicles Distance (in kilometers) Earnings (in euros) A 3 15 B 5 20 C 8 25 D 2 10 E 6 22

[0037] The graph, "as shown in [Fig. 1]", illustrates the relationship between the distance traveled to recover broken-down vehicles and the associated gain. The correlation coefficient is approximately 0.98, indicating a very strong positive correlation between distance and gain.

[0038] The following graph shows the relationship between the distance traveled to recover broken-down vehicles and the associated gain. The correlation coefficient is approximately 0.98, indicating a very strong positive correlation between distance and gain.

[0039] Conclusion:

[0040] This means that in this example, the greater the distance traveled, the greater the gain tends to be. This information can be useful if I favor longer routes for potentially significant gains, or if I prefer to focus on shorter, faster routes, depending on the strategy for maximizing time and gains.

[0041] Example 2:

[0042] “As can be seen in [Fig.2]”.

[0043] Context:

[0044] Vehicle A breaks down on the road. By activating its geolocation by opening the application, the breakdown service A located in the nearest radius will intervene to repair it. Therefore, breakdown service B will not be able to repair vehicle A because the application only connects vehicle A with the breakdown service closest to it.

[0045] Example 3:

[0046] “As can be seen in [Fig.3]”.

[0047] Meaning in context:

[0048] Estimate:

[0049] I will use this line to estimate the travel time for any other distance not measured directly. For example, if a broken-down vehicle is 10 kilometers away, I can estimate the travel time as Time = 1.43 x 10 + 4.16 = 18.46 minutes.

[0050] Optimization:

[0051] This information helps me make more informed decisions about which broken-down vehicle to take based on the distance to the nearest breakdown service. If I have strict time priorities, this line allows me to make quick and reliable estimates.

[0052] Here is the graph with the regression line drawn in red. The equation of this line is: Time” 1.43 x Distance 4~ 4.16

[0053] Interpretation of the equation:

[0054] Slope (1.43): Each additional kilometer adds approximately 1.43 minutes to the travel time.

[0055] Intercept (4,14): Even if the distance were 0 kilometers, the model predicts a minimum time of approximately 4.16 minutes. This may represent a fixed preparation or start-up time before travel.

[0056] I will now plot this line of best fit on the graph and provide the associated equation.

[0057] Practical use:

[0058] In this context, this line can be used to estimate the time required to reach a broken-down vehicle based on its distance from the nearest breakdown service. More precisely, it allows the equation of the line to be used to estimate the time for distances not present in my initial data.

[0059] What does the regression line mean in this context?

[0060] Inclination of the line (slope): This value indicates how time changes as a function of distance. If the slope is positive (which is the case here), it means that the greater the distance, the longer the time to reach the customer.

[0061] Intercept: This is the estimated time value when the distance is equal to zero. In reality, even if the distance is very short, there will always be a minimum travel time (due to preparation, starting, etc.).

[0062] Example 4:

[0063] Contextualization:

[0064] Plotting the affine regression line (also called the linear regression line) allows us to model the relationship between two variables (distance and time); This line shows the general trend of the data and can be used to make predictions.

[0065] The calculated correlation coefficient is approximately 0.95, indicating a very strong positive correlation between distance and time. This means that, in this example, the greater the distance, the longer the time required to reach the customer.

[0066] The graph, as shown in [Fig. 4], illustrates this relationship: each point represents a broken-down vehicle, with distance on the x-axis and time on the y-axis. You can see that the points follow an upward trend, which confirms the strong positive correlation.

[0067] Correlation analysis:

[0068] The application calculates the correlation between distance and time to see if these two variables are related (e.g., if a greater distance generally results in a longer time).

[0069] Correlation coefficient: This coefficient will vary between -1 and -1. A value close to 1 indicates a strong positive correlation (the greater the distance, the longer the time), a value close to -1 indicates a negative correlation, and a value of 0 indicates little or no correlation.

[0070] Explanation:

[0071] To calculate the correlation coefficient and create a graph illustrating the relationship between the two variables (distances and time), we need data; suppose you have the following data for five broken-down vehicles:

[0072] [Tables2] Vehicles out of service Distance (km) Time (min) A 5 12 B 8 18 C 3 7 D 12 20 E 7 14

[0073] Using these data to calculate the correlation coefficient and plot the graph, I have several criteria with which we can consider drawing an efficiency frontier to identify the choices that offer the best compromise between distance and time.

[0074] In the two-variable statistical study, as in this case where the application will optimize the time and distance to recover a broken-down vehicle, we can follow the following steps:

[0075] Data collection

[0076] Distance: The distance between the breakdown service and each potential broken-down vehicle.

[0077] Time: The estimated time to reach the disabled vehicle, which may depend on traffic, road conditions, etc.

[0078] Each vehicle that has broken down can be represented by a pair of values ​​(distance, time).

[0079] Conclusion:

[0080] After analyzing the data, the application decides which vehicle is broken down and which to recover based on the results of the analysis, taking into account the two variables: 1. Optimization: The application will minimize both distance and time. There may be a trade-off; for example, a customer who is closer in terms of distance may take longer to reach due to traffic. 2. Selection criterion: Based on the data, the application chooses the broken-down vehicle based on the shortest distance, the shortest time, or a weighted combination of the two (e.g., a weighted average).

[0081] The optimization and the selection criterion will allow for an efficient correlation between time and distance.

[0082] Example 5:

[0083] Diagram of a scenario

[0084] Example

[0085] You have several broken-down vehicles spread across the Lille metropolitan area. The objective is to choose the nearest breakdown service provider, optimizing both time and distance.

[0086] You will find attached an explanatory table of this scenario:

[0087] [Tables3] Vehicle broken down City Distance (km) Time (min) To Roubaix 10 20 B Tourcoing 10 22 C Villeneuve-d'Ascq 8 15 D Marcq-en-Barœul 7 18 E Lambersart 5 12

[0088] The objective is to minimize both the distance and the time between the broken-down vehicle and the nearest breakdown service.

[0089] Indeed, the following calculation demonstrates this:

[0090] Here is the calculation formula. Time = 1.34 x Distance 4-6.12

[0091] Slope (1.34): Each additional kilometer adds approximately 1.34 minutes to the travel time.

[0092] Intercept 6.12): even for a very short distance, the model predicts a minimum time of about 6.12 minutes, which may represent the start-up or preparation time of the breakdown service.

[0093] Analysis of results

[0094] Correlation coefficient (0.91):

[0095] The correlation coefficient between distance and time is 0.91, indicating a strong positive correlation. This means that, in this area, the greater the distance to be covered, the longer the time required to reach the broken-down vehicle.

[0096] We observe that Lambersart is the optimal choice.

[0097] However, if traffic or other factors influence travel time plus distance, the regression line can be used to estimate which choice might be most efficient in general.

[0098] Lambersart is the nearest town with a distance of 5 km and an estimated travel time of 12 minutes.

[0099] Villeneuve-d'Ascq and Marcq-en-Barœul are also variable options with distances of 8 km and 7 km and times of 15 minutes and 18 minutes.

Claims

Demands

1. ; The existing free SOS Autoroute app works as follows: You must communicate your location as clearly as possible: the highway emergency number 112, indicating: direction of travel, the landmark located on the central reservation or the shoulder of the road; - for deaf / mute individuals, you can contact 114 by SMS. The application (Utiliscar) is characterized in that, in particular, it proposes to connect stakeholders (road users who have broken down with their vehicles and breakdown services) via the interconnection of the iOS and Android application (Utiliscar) by connecting to it using numerical data such as time and distance. After analyzing the data, the application decides which broken-down vehicle to recover based on the results of the analysis, taking into account the two variables (time and distance in km).

2. Device according to claim 1: Characterized in that the optimization of the application minimizes both distance and time. There may be a trade-off; for example, a customer who is closer in terms of distance may take longer to reach due to traffic. The optimization allows for an efficient correlation between time and distance.

3. Device according to claim 2: Characterized in that, based on the data selection criterion, the application chooses the vehicle that has broken down based on the shortest distance, the shortest time, or a weighted combination of the two (e.g., a weighted average). The selection criterion allows for an efficient correlation between time and distance.

4. Device according to claim 3: Characterized in that the correlation coefficient: This coefficient will vary between -1 and -1. A value close to 1 indicates a strong positive correlation (the greater the distance, the longer the time), a value close to -1 indicates a negative correlation, and a value of 0 indicates little or no correlation. The entire demonstration in our examples confirms positive correlations between time and distance.

5. Device according to claim 4: Characterized in that the set of the aforementioned parameters to be adopted will see the use of the application such that the user who has broken down with their vehicle (car or motorcycle) and the breakdown service provider will create an account and then in a second stage enter their geolocation and will then have to confirm the trip.

Images