The present application does not claim priority from any patent application.
TECHNICAL FIELD
[002] The present disclosure in general relates to the field of determining a cause for
variance. More particularly, the present invention relates to a system and method for determining the cause for a variance in time.
BACKGROUND
[003] Navigation systems or applications plays vital role today in road trips by a
personal vehicle or fright vehicle. The Navigation application such as Google maps navigation, and widely used by almost every vehicle driver while he drives on road to a destination. It is to be noted that a key factor of navigation is the route, and a predicted travel time as determined by the navigation application. However, the predicted travel time may vary from an actual time required to complete the trip. It means that the navigation applications fail to accurately predict the travel time. Due to this, many people face lot of problems as they cannot reach on time because the actual time required for the trip is greater than the predicted travel time.
SUMMARY
[004] Before the present systems and methods for determining a cause for a variance
in time, is described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and method for determining a cause for a variance in time. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one implementation, a method for determining a cause for a variance in time
is illustrated. The method may comprise determining a trip travel time based on a plurality of data points. The plurality of data points may comprise a vehicle speed variance, weather

factor, a road condition, a traffic variance, and unforeseen events. Further, the method may comprise analyzing the trip travel time according to a predicted travel time. Furthermore, the method may comprise determining one or more causes for a variance in the trip travel time and the predicted travel time based on the analysis. The one or more causes of the variance may be determined when the variance between the trip travel time and the predicted travel time is above a threshold variance.
[006] In another implementation, a system for determining a cause for a variance in
time is illustrated. The system comprises a memory and a processor coupled to the memory, further the processor is configured to execute instructions stored in the memory. In one embodiment, the processor may execute instructions stored in the memory for determining a trip travel time based on a plurality of data points. The plurality of data points may comprise a vehicle speed variance, weather factor, a road condition, a traffic variance, and unforeseen events. Further, the processor may execute instructions stored in the memory for analyzing the trip travel time according to a predicted travel time. Furthermore, the processor may execute instructions stored in the memory for determining one or more causes for a variance in the trip travel time and the predicted travel time based on the analysis. The one or more causes of the variance may be determined when the variance between the trip travel time and the predicted travel time is above a threshold variance
BRIEF DESCRIPTION OF DRAWINGS
[007] The detailed description is described with reference to the accompanying
figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
[008] Figure 1 illustrates a network implementation of a system for determining a
cause for a variance in time, in accordance with an embodiment of the present subject matter.
[009] Figure 2 illustrates the system for determining the cause for the variance in time,
in accordance with an embodiment of the present subject matter.
[0010] Figure 3 illustrates a method for determining a cause for a variance in time, in accordance with an embodiment of the present subject matter.

DETAILED DESCRIPTION
[0011] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. The words "including", "comprising", "consisting", "containing", and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods for determining a cause for a variance in time are now described. The disclosed embodiments of the system and method for determining the cause for the variance in time are merely exemplary of the disclosure, which may be embodied in various forms.
[0012] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure for determining a cause for a variance in time is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0013] In one embodiment, a method for determining a case for a variance in time is disclosed. In the embodiment, a trip travel time is determined based on plurality of data points associated with a trip. The plurality of data points may comprise a vehicle speed variance, a weather factor, a road condition, a traffic variance, unforeseen events and the like. Upon determining the trip travel time, the trip travel time may be analysed according to a predicted travel time. Based on the analysis, one or more causes for the variance in the trip travel time and the predicted travel time may be determined. The one or more causes may be determined when the variance is above a threshold variance as defined.
[0014] Referring now to Figure 1, a network implementation 100 of a system 102 for determining a cause for a variance in time is disclosed. Although the present subject matter is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems,

such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented over a cloud network. Further, it will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2... 104-N, collectively referred to as user device 104 hereinafter, or applications residing on the user device 104. Examples of the user device 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user device 104 may be communicatively coupled to the system 102 through a network 106.
[0015] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0016] Referring now to figure 2, the system 102 for determining a cause for a variance in time is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[0017] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the user device 104. Further, the I/O interface 204 may enable the system 102 to communicate with other

computing devices, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0018] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0019] The modules 208 may include routines, programs, objects, components, data structures, and the like, which perform particular tasks, functions or implement particular abstract data types. In one implementation, the module 208 may include a time determination module 212, a analysing module 214, a cause determination module 216, and other modules 220. The other modules 220 may include programs or coded instructions that supplement applications and functions of the system 102.
[0020] The data 210, amongst other things, serve as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a repository 224, and other data 226. In one embodiment, the other data 226 may include data generated as a result of the execution of one or more modules in the other modules 222.
[0021] In one implementation, a user may access the system 102 via the I/O interface 204. The user may be registered using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information, providing input information or configuring the system 102.
[0022] In one embodiment, the time determination module 212 may determine a trip travel time associated with a trip. The trip travel time may be determined in a real time at an end of the trip. The trip travel time may be determined based on a plurality of data points associated with a travel route of the trip. The plurality of data points may comprise a vehicle speed variance, a weather factor, a road condition, a traffic variance, unforeseen events and the like. The plurality of data points may be captured during the trip.

[0023] Once the trip travel time is determined, the analysis module 214 may analyse the trip travel time according to a predicted travel time. In one aspect, an analysis algorithm such as a decision tree algorithm, a Naive Bayes algorithm, a K-means algorithms, and the like may be used for the analysis. The predicted travel time may be determined at a start of the trip. The predicted travel time may be determined based on predefined data points stored in a repository. The predefined data points may correspond to historical data. The historical data may comprise a historical speed variance, a historical road condition, a historical traffic variance and the like. The predicted travel time may be estimated based on a starting point and an end point associated with the trip.
[0024] In one embodiment, the analysis of the trip travel time may correspond to compute a variance between the trip travel time and the predicted travel time. The variance may correspond to a difference between the trip travel time and the predicted travel time.
[0025] Upon determining the variance, the cause determination module 216 may determine one or more causes for the variance. The one or more causes corresponds to the reasons for the difference between the trip travel time and the predicted travel time. The one or more causes may be determined when the variance is above a threshold variance. The threshold variance may be predefined.
[0026] In one example, construe the threshold variance as 5%. Further, if the variance between the trip travel time and the predicted travel time is above 5 %, then the one or more causes may be determined.
[0027] The one or more causes may be determined based on data analytics corresponding to analysis of a set of cause factors, a weightage associated with each cause factor, and the like. The set of cause factors may be responsible for the variance. The set of cause factors may correspond to, but not limited to, traffic factors, accidents, road condition factors, a travel route and the like. The weightage of each cause factor may be determined using a data analytics algorithm such as a decision Tree, a Naive Bayes, a Dimensional Reduction Algorithms, and the like. Further, a factor variance associated with each cause factor from the set of cause factors may be determined. The factor variance may be further used to determine the one or more causes.
[0028] Further, the one or more causes may comprise major causes and secondary causes responsible for the variance. In one aspect, the one or more causes may comprise an

increase in the traffic as compared to the predicted traffic, a change in the travel route and the like.
[0029] In one example, construe a route variance as 10%. In this case, the major cause may be 'driver didn't follow exact route and there was a route variance of 10%'. Further, a secondary cause may be 15% more traffic than accounted at the start of trip.
[0030] Once the one or more causes are determined, the cause determination module 216 may determine a report. The report may comprise the starting point of the trip, the end point of the trip, the one or more causes for the variance, the trip travel time, the predicted travel time, a weightage associated with each cause and the like. The report may be referred as a summary report.
[0031] In one example, construe a trip driver from home to airport. The predicted travel time at the start may be 40 minutes. Further, the trip travel time, at the end, may be 52 minutes. In this case, the variance of 12 minutes may be determined, and the variance is above the threshold variance. The report may comprise data such as a route change due to road closure: 7 minutes with weightage 60%, a driver speed on city road: lower than the considered standard value 3 minutes with weightage 20%, and increased traffic volume than expected: 2 minutes with weightage 20%.
[0032] Further, the cause determination module 216 may analyse the report, and driver data to compute an average driving speed associated with a driver of the trip. The driver data may correspond to historical data associated with one or more trips of the driver. The driver data may comprise a historical driving speed, a driver name, and the like. The average driving speed may be further used to generate a tag for the driver. In one aspect, the average driving speed may be compared with a threshold speed. Based on the comparison, the tag for the driver may be generated. The tag may indicate the driving of the driver. The tag may be one of a fast speed vehicle driver, a slow speed vehicle driver, a moderate speed vehicle driver, an aggressive driver and the like.
[0033] In one aspect, if the average driving speed is less than the threshold speed that is 10%) less than a standard speed, then the driver may be tagged as a slow speed vehicle diver.

[0034] Further, the cause determination module 216 may estimate an accurate trip travel time based on an analysis of the average driving speed in future. Furthermore, the cause determination module 216 may generate a personalized notification for the driver. The personalized notification may indicate the accurate trip travel time. The accurate trip travel time may be a personalized travel time estimation.
[0035] In one exemplary embodiment, construe a city road with medium traffic standard speed of a four wheeler car may be defined as 40 km/hr. Further, a driver's actual speed on average across multiple trips may be found to be 35 km/hr in the same condition. It may indicate that the driver's actual speed is more than 10% lesser than a standard speed. In this case, the driver may be tagged as a slow driver, and his actual average speed on city road with medium traffic will be considered as 35 km/hr in further travel time calculation to provide a more accurate and personalized travel time forecast. Likewise, the personalized travel time forecast may be done for the fast speed vehicle driver, the aggressive driver, and the like.
[0036] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0037] Some embodiments of the system and the method is configured to determine one or more causes for a variance in a travel time.
[0038] Some embodiments of the system and the method is configured to generate a personalized notification for a driver.
[0039] Referring now to figure 3, a method for determining a cause for a variance in time, is disclosed in accordance with an embodiment of the present subject matter. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, and the like, that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

[0040] The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented in the above described system 102.
[0041] At block 302, a trip travel time associated with a trip may be determined based on a plurality of data points. The plurality of data points may be associated with the trip. In one implementation, the time determination module 212 may determine the trip travel time.
[0042] At block 304, the trip travel time may be analysed according to a predicted travel time. In one implementation, the analysing module 214 may analyse the trip travel time.
[0043] At block 306, one or more causes for a variance between the trip travel time and the predicted travel time may be determined. In one implementation, the cause determination time 216 may determine the one or more causes. The one or more causes may be determined based on the analysis.
[0044] Although implementations for systems and methods for determining a cause for a variance in time have been described, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for determining the cause for the variance in time.

WE CLAIM:

1.A method for determining a cause for a variance in time, the method comprises:
determining, by a processor, a trip travel time based on a plurality of data points, wherein the plurality of data points comprise a vehicle speed variance, weather factor, a road condition, a traffic variance, and unforeseen events;
analyzing, by the processor, the trip travel time according to a predicted travel time; and
determining, by the processor, one or more causes for a variance in the trip travel time and the predicted travel time based on the analysis, wherein the one or more causes of the variance is determined when the variance between the trip travel time and the predicted travel time is above a threshold variance.
2. The method as claimed in claim 1, further comprises generating a report comprising the one or more causes for the variance, the trip travel time, the predicted trip time, and a weightage associated with each cause from the one or more causes.
3. The method as claimed in claim 1, further comprises computing an average driving speed associated with a driver of the trip based on an analysis of the report and driver data.
4. The method as claimed in claim 1, further comprises estimating an accurate trip travel time associated with the driver based on the average driving speed associated with the driver.
5. The method as claimed in claim 1, further comprises generating a personalized notification for the driver, wherein the notification indicates the accurate trip travel time.
6. The method as claimed in claim 1, wherein the plurality of data points is captured during the trip in real-time.
7. The method as claimed in claim 1, wherein the predicted travel time is computed at a start of the trip, wherein the predicted travel time is computed based on predefined data points stored in a repository.

8. A system for determining a cause for a variance in time, the system comprises:
a memory;
a processor coupled to the memory, wherein the processor is configured to execute instructions stored in the memory to:
determine a trip travel time based on a plurality of data points, wherein the plurality of data points comprise a vehicle speed variance, weather factor, a road condition, a traffic variance, and unforeseen events;
analyze the trip travel time according to a predicted travel time; and determine one or more causes for a variance in the trip travel time and the predicted travel time based on the analysis, wherein the one or more causes of the variance is determined when the variance between the trip travel time and the predicted travel time is above a threshold variance.
9. The system as claimed in claim 8, further configured to generate a report comprising the one or more causes for the variance, the trip travel time, the predicted trip time, and a weightage associated with each cause from the one or more causes.
10. The system as claimed in claim 8, further configured to compute an average driving speed associated with a driver of the trip based on an analysis of the report and driver data.
11. The system as claimed in claim 8, further configured to estimate an accurate trip travel time associated with the driver based on the average driving speed associated with the driver.
12. The system as claimed in claim 8, further configured to generate a personalized notification for the driver, wherein the notification indicates the accurate trip travel time.
13. The system as claimed in claim 8, wherein the plurality of data points is captured during the trip in real-time.
14. The system as claimed in claim 8, wherein the predicted travel time is computed at a start of the trip, wherein the predicted travel time is computed based on predefined data points stored in a repository.