CN111813881A

CN111813881A - Method, apparatus, device and storage medium for travel information processing

Info

Publication number: CN111813881A
Application number: CN202010524350.3A
Authority: CN
Inventors: 傅昆; 王征
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-06-10
Filing date: 2020-06-10
Publication date: 2020-10-23

Abstract

According to an embodiment of the present disclosure, a method, an apparatus, a device, and a storage medium for trip information processing are provided. The method proposed herein comprises: determining a plurality of road segments associated with a trip; determining position codes for a plurality of road segments, one position code indicating an order in which a respective one of the plurality of road segments is to be traversed in the journey; and determining a projected elapsed time for the trip based on the traffic attributes and the location codes for the plurality of road segments, the traffic attributes indicating at least a current traffic state for the plurality of road segments. In light of the fact that the present disclosure makes it possible to more accurately determine the expected time consumption of a trip.

Description

Method, apparatus, device and storage medium for travel information processing

Technical Field

Implementations of the present disclosure relate to the field of intelligent transportation, and more particularly, to a method, apparatus, and computer storage medium for travel information processing.

Background

In the field of electronic maps and navigation, the time taken by a mobile subject (a vehicle or a pedestrian, etc.) from a starting point to an end point is a very important technical index, and describes the relevant time cost of traveling. In the field of intelligent transportation, this is also known as the determination of an Estimated Time of Arrival (ETA).

In an intelligent travel scenario, each particular trip will correspond to a particular path from the start point to the end point. The route may be a route from the driver to the boarding point of the passenger or a route to the destination of the passenger after the passenger is received. The projected time-consuming nature of the trip can be used for a number of aspects of intelligent transportation. For example, the projected elapsed time may be used to estimate travel remaining time, to estimate time to reach an end, to estimate cost of travel, or to schedule vehicles, etc. Therefore, how to more accurately determine the expected time consumption of the trip is called the current focus of attention.

Disclosure of Invention

The embodiment of the disclosure provides a scheme for processing travel information.

In a first aspect of the present disclosure, a method for trip information processing is provided. The method comprises the following steps: determining a plurality of road segments associated with a trip; determining position codes for a plurality of road segments, one position code indicating an order in which a respective one of the plurality of road segments is to be traversed in the journey; and determining a projected elapsed time for the trip based on the traffic attributes and the location codes for the plurality of road segments, the traffic attributes indicating at least a current traffic state for the plurality of road segments.

In a second aspect of the present disclosure, an apparatus for trip information processing is provided. The device includes: a road segment determination module configured to determine a plurality of road segments associated with a trip; a position code determination module configured to determine position codes for a plurality of road segments, one position code indicating an order in which a respective one of the plurality of road segments was passed through in a trip; and a time-of-flight determination module configured to determine a projected time-of-flight based on the traffic attributes and the location codes of the plurality of road segments, the traffic attributes indicating at least a current traffic state of the plurality of road segments.

In a third aspect of the present disclosure, there is provided an electronic device comprising: a memory and a processor; wherein the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to the first aspect of the disclosure.

In a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon one or more computer instructions, wherein the one or more computer instructions are executed by a processor to implement a method according to the first aspect of the present disclosure.

According to various embodiments of the present disclosure, the projected time consumption of a trip may be more accurately determined, thereby providing a more accurate reference to a participant (e.g., driver or passenger) of the trip.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

FIG. 1 illustrates a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;

FIG. 2 illustrates a schematic diagram of determining projected elapsed time for different trips, according to some embodiments of the present disclosure;

FIG. 3 illustrates a flow diagram of an example trip information processing method according to some embodiments of the present disclosure;

FIG. 4 illustrates a flow diagram of an example method of determining a feature vector with position encoded information, in accordance with some embodiments of the present disclosure;

FIG. 5 illustrates a schematic diagram of determining a projected time consumption using a model according to some embodiments of the present disclosure;

FIG. 6 shows a schematic block diagram of an apparatus for information processing according to some embodiments of the present disclosure; and

FIG. 7 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

In describing embodiments of the present disclosure, the terms "include" and its derivatives should be interpreted as being inclusive, i.e., "including but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

Referring initially to FIG. 1, a schematic diagram of an environment 100 is schematically illustrated in which an application according to an exemplary implementation of the present disclosure may be used.

Applications that enable users to call vehicle services online are known and may be referred to as "taxi calling software" or "taxi calling applications". As shown in FIG. 1, environment 100 includes a terminal device 160 configured to present a user interface 110 as shown in FIG. 1. At the user interface 110, a user (e.g., a driver or passenger) may be presented with a start point 120, an end point 130 of a trip, and a path 140 from the start point 120 to the end point 130.

The terminal device 160 may, for example, acquire the expected time consumption of the trip. It should be appreciated that the terminal device 160 may determine the expected elapsed time based on the start point 120, the end point 130 of the start trip, and the travel route 140. Alternatively, the terminal device 160 may provide information relating to the formation to the server 170, and the server 170 determines the expected elapsed time and sends the expected elapsed time from the server 170 to the terminal device 160.

After acquiring the projected elapsed time of the trip, the terminal device 160 may provide the user with additional information to enable the user to better understand the status of the trip. For example, the terminal device 160 may present the prompt information 150-1 relating to the estimated time of arrival ETA so that the user may intuitively know the estimated end time of the trip. In one specific example scenario, the passenger has reserved a vehicle, for example, to make a particular trip. At this point, the prompt 150-1 may be used to prompt the passenger that the estimated time of arrival for the trip is 15: 00.

In another example, the terminal device 160 may also present prompt information 150-2 relating to the expected time consumption of the trip, for example, so that the user may intuitively know the expected time required for the trip. In one particular example scenario, a user (e.g., a driver) may specify a start point (e.g., start point 120) and an end point (e.g., end point 130) of a trip via terminal device 160. At this point, the reminder message 150-2 may be used to remind the passenger that the predicted elapsed time for the trip from the start point 120 to the end point 130 is 30 minutes.

In yet another example, the terminal device 160 may also present prompt information 150-3 relating to the estimated price of the trip, for example, so that the user may intuitively understand the cost expected to be required for the trip. It should be appreciated that the cost information may be calculated by the terminal device 160 or the server 170 based on the expected time consumption of the trip. In one particular example scenario, a user (e.g., a passenger) may reserve a trip from a specified starting point 120 to a specified ending point 130 using a terminal device 160. At this point, the prompt 150-3 may be used to prompt the passenger for the projected cost of the trip as XX dollars.

In yet another example, the terminal device 160 may also present prompt information 150-4 with the remaining time of the trip, for example, so that the user may intuitively understand the expected remaining time of the trip in progress. In one particular example scenario, a user (e.g., a driver) has started a particular trip to the endpoint 130. At this time, the terminal device 160 or the server 170 may, for example, acquire the real-time location of the user and update the expected elapsed time of the trip based on the real-time location as a new starting point (e.g., the starting point 120). The reminder information 150-4 may be updated in real time or periodically to present the user with the expected remaining time of the trip.

In one known class of approaches, machine learning models (e.g., gbdt (gradient base decision tree), fm (navigation machine), and rnn (current Neural network), etc.) may be utilized to estimate the projected elapsed time of a trip. For example, after a path 140 from a start point 120 to an end point 130 is obtained by a path planning service, known solutions may divide the path 140 into a plurality of end-to-end segments (also referred to as links). A road segment may refer to a segment of a path having a relatively short length and having a direction. According to some known approaches, the path planning service may generate a plurality of road segments directly from the start point and the end point to indicate a path from the start point to the end point.

The inputs to the machine learning model may include, for example, a route 140, which may include a plurality of road segments, each of which may include corresponding traffic attributes, such as congestion conditions, speed limits, number of lanes, whether to charge, and other personalized information (driver ID, weather, day of week, month, etc.), and other personalized information. According to known approaches, the machine learning model may utilize traffic attributes of road segments and other personalized information (e.g., driver information, passenger information, weather information, and the like, which are not relevant to the road segment) to determine the projected elapsed time for the trip.

However, in such a machine learning model, the traffic attributes of the road segment input into the machine learning model include both static attributes (e.g., the number of lanes) and dynamic attributes (e.g., congestion conditions) of the road segment. In some known solutions, the dynamic properties of the road segment will not change in a very short time (e.g., 2 minutes). In other words, during the process of machine learning models, the dynamic attributes of such road segments will always be converted to the same feature vectors for estimating the expected elapsed time. However, such a processing manner is not reasonable.

FIG. 2 illustrates a schematic diagram 200 of determining projected time consumption for different trips, according to some embodiments of the present disclosure. As shown in fig. 2, the machine learning model needs to estimate the expected elapsed time for two trips at the same time, where the starting point of one trip (for convenience of description, hereinafter referred to as the first trip) is 120 and the end point is 130, and the starting point of the other trip (for convenience of description, hereinafter referred to as the second trip) is 210 and the end point is 220. As shown in fig. 2, both the first trip and the second trip are required to travel through the segment 220 from the location 230 to the location 130 (i.e., the end 130 of the first trip).

According to a known approach, the traffic attributes of the road segment 220 will be converted into the same feature vector for determining the expected time consumption of two different trips. However, the effect of the road segment 220 on the two different strokes is actually different. For the first trip, the section 220 is at the rear section of the trip, and the traffic condition of the section 220 may be greatly changed when the user actually travels through the section 220. In contrast, for the second trip, where the segment 220 is the middle front segment of the trip, the user may, for example, quickly traverse the segment 220, at which time the traffic conditions of the segment 220 will, for example, not change significantly. Therefore, the same road segment 220 should have different effects on the estimated time of different trips. However, the known solutions do not effectively distinguish between the above situations, which results in that the predicted time consumption of the trip will be inaccurate in some cases.

According to various embodiments of the present disclosure, a scheme of trip information processing is provided. In embodiments of the present disclosure, a plurality of road segments associated with a trip may be determined by a path planning service. The system may then determine position codes for the plurality of road segments, wherein one position code indicates an order in which a respective one of the plurality of road segments was traversed during the trip. For example, when a road segment is the first road segment to be passed through in the course, its position code may be determined to be 1, for example. Subsequently, based on the traffic attributes and the location codes for the plurality of road segments, the system may determine an expected time consumption for the trip. In this manner, by introducing position coding, embodiments of the present disclosure may distinguish the effects of the same route segment on different trips, thereby more accurately determining the predicted time consumption of the trips.

Fig. 3 illustrates a flowchart of an example method 300 of trip information processing, according to some embodiments of the present disclosure. The method 300 may be implemented, for example, at the terminal device 160 and/or the server 170 of fig. 1. For convenience of description, the method 300 is described below with the server 170 as an example.

At block 302, the server 170 determines a plurality of road segments associated with the trip. In some embodiments, the server 170 may determine a plurality of road segments associated with the trip, for example, from the trip information received from the terminal device 160.

In some embodiments, the user may reserve a trip using the terminal device 160. Specifically, the user can specify the start point 120, the end point 130 of the reserved trip, the start time of the trip, and the like through the terminal device 160. The terminal device 160 may, for example, transmit travel information including the start point 120, intermediate stop points, and/or end point 130 specified by the user (e.g., passenger) to the server 170. In some embodiments, the trip information may also include, for example, time information for the trip, e.g., with a specified trip start time, etc. Further, the server 170 may process the travel information using the path generation service to determine a plurality of segments corresponding to the travel.

In other embodiments, for an ongoing trip (e.g., after the driver picks up the passenger and starts the trip), the terminal device 160 may, for example, periodically send the current location including the user (e.g., passenger) to the server 170. The server 170 may determine a plurality of road segments associated with the ongoing trip based on, for example, the current location (to serve as a new starting point) and previous trip information (e.g., an end of the trip and a travel route).

In some embodiments, server 170 may determine a corresponding plurality of road segments based on route 140 from start point 120 to end point 130. For example, route 140(TR) may be represented, for example, as m locations:

TR＝[W₁,W₂,…,W_i…,W_m],(1≤i≤m) (1)

wherein TR indicates a route, m indicates the number of positions included in the route TR, W_iThe geographical information of the position i in the route TR is pointed, the arrangement order of the m positions corresponds to the direction of the route TR, and the geographical information of the position i may include longitude-latitude coordinates of the position i.

Based on the location included in route 140, server 170 may determine a plurality of road segments, for example, by looking up a pre-stored road segment table. In this manner, route 140 may be represented, for example, as a sequence of multiple road segments:

at block 304, the server 170 determines position codes for the plurality of road segments, wherein one position code indicates an order in which a respective one of the plurality of road segments was traversed during the trip. It should be appreciated that the position code may characterize the position of the road segment in the sequence of road segments. Taking the formula (2) as an example,

the position code of (a) can be determined as 1,

may be determined as i. It should be understood that the above specific position-coding values are merely illustrative, and other values capable of indicating order may be adopted as the position-coding.

Exemplarily, for the first journey from the start 120 to the end 130 shown in fig. 2, it comprises for example 6 road segments. The start and end points of each road segment (except for start point 120 and end point 130) are shown in figure 2 by open circles. In this trip, the server 170 may determine, for example, that the position code of the segment 220 is 6, which indicates that the segment 220 is the 6 th traversed segment in the first trip. For the second journey from the start 210 to the end 220 in fig. 2, it comprises, for example, 4 road segments. The position code of the link 220 in the second trip is 3, which indicates that the link 220 is the 3 rd passed link in the second trip.

At block 306, the server 170 determines an expected time of the trip based on the traffic attributes and the location codes for the plurality of road segments, the weighted traffic attributes indicating at least a current traffic state for the plurality of road segments. In some embodiments, the server 170 may determine the expected time consumption by synthetically considering both the traffic attributes and the location codes for the road segments. For example, the server 170 may use a combination of both location coding and traffic attributes as inputs to a machine learning model to determine the projected time consumption of a trip.

In some embodiments, the server 170 may adjust the feature vector corresponding to the traffic attribute based on the location code to generate the feature vector with the location code information as an input to the machine learning model. Specifically, the server 170 may generate a feature vector with position-coded information based on the traffic attributes and position codes of the plurality of road segments.

A specific process of generating the feature vector with the position-coding information will be described below with reference to fig. 4 and 5. Fig. 4 illustrates a flow diagram of an example method 400 of determining position-coded feature vectors, in accordance with some embodiments of the present disclosure.

As shown in fig. 4, at block 402, the server 170 may generate an initial feature vector based on the traffic attributes. In some embodiments, the server 170 may obtain the traffic attribute corresponding to the road segment, for example, by querying a pre-stored road segment table. In some embodiments, the traffic attributes may include static attributes and dynamic attributes. Static attributes may refer to factors that rarely change over time, with less impact on ETA. Static characteristics may include, but are not limited to, length of road segments, number of lanes, speed limits, road level, road tolls, etc., or any combination thereof. Dynamic attributes may refer to factors that change over time, having a greater impact on ETA. The dynamic attributes may include, but are not limited to, a current time, a congestion level, a traffic light latency for intersections included in the road segment, and the like, or any combination thereof.

In some embodiments, the server 170 may generate the initial feature vectors using, for example, a time-consuming predictive model. FIG. 5 illustrates a diagram 500 for determining a projected time using a model according to an embodiment of the disclosure. As shown in FIG. 5, the time-consuming predictive model 520 may receive the traffic attributes 510 and process the traffic attributes 510 using the input layer 530 to generate an initial feature vector 535 having N dimensions, where each dimension feature may be represented as a feature 535-1, 535-2, …, 535-N.

With continued reference to fig. 4, at block 404, the server 170 may determine weight coefficients associated with different dimensions of the initial feature vector based on the position code. As shown in fig. 5, the time-consuming prediction model 520 may comprise a weighting layer 540 configured to base the received position code 515 and determine a weight coefficient for each dimension in the initial feature vector 535.

In some embodiments, the server 170 may represent the weighting coefficients as a function of position coding and dimension. Specifically, the server 170 may determine a plurality of periodic functions corresponding to different dimensions, and having different periods with respect to the variable (position code). Illustratively, the periodic function may be a trigonometric function, including a sine function, a cosine function, and the like. As a specific example, the periodic function may be expressed as:

where pos represents the position code of the road segment, D represents the dimensions in the initial feature vector, and D represents the total number of dimensions in the initial feature vector.

For example, in the example of fig. 5, if the total number of dimensions N of the initial feature vector 535 is 2, the periodic function corresponding to the first-dimension feature 535-1 may be expressed as:

the periodic function corresponding to the second-dimension feature 535-2 may be expressed, for example, as:

it can be seen that the two periodic functions determined based on such a manner have different periods of the relative variable pos. The period of the periodic function (4) is relatively small, which means that the weight of the first-dimension feature 535-1 varies to a large extent with the position-encoded pos. In contrast, the period of periodic function (5) is relatively large, which means that the weight of the first-dimension feature 535-1 varies to a lesser extent with position-encoded pos.

The server 170 may then determine the weight coefficients based on the position code and the periodic function. In connection with the example of fig. 2, where the traffic information for a road segment 220 is utilized to determine the expected time consumption for a first trip, the position code for the road segment 220 will be determined to be 6. At this time, the weight coefficient corresponding to the first-dimensional feature 535-1 may be determined as cos (6/100), and the weight coefficient corresponding to the second-dimensional feature 535-2 may be determined as cos (6/1000).

At block 406, the server 170 may apply the weight coefficients to the initial feature vector to obtain a feature vector with position-coding information. As shown in fig. 5, the time-consuming prediction model 520 may weight the corresponding dimension in the initial feature vector 535 with the determined weight coefficient by weighting 540.

For example, the first-dimension features 535-1 of the initial feature vector would be multiplied by a corresponding weight coefficient (e.g., cos (6/100)) to obtain corresponding weighted features 545-1. Accordingly, the second-dimensional features 535-2 of the initial feature vector will be multiplied by a corresponding weight coefficient (e.g., cos (6/100)) to obtain corresponding weighted features 545-2. That is, the initial feature vector 535 is converted into a feature vector 545 with position-coding information, each dimension of which is represented as features 545-1, 545-2, …, 545-N, respectively. It should be understood that the above specific periodic function forms and parameter values are illustrative only and are not intended to be limiting of the present disclosure.

It should be understood that the above weighted procedure can also be expressed as equation (6):

where x represents the initial feature vector 535 and PE represents a mask vector composed of weights corresponding to different dimensions (e.g., [ cos (6/100), cos (6/100)])，

Representing the feature vector 545 with the position-coding information.

By setting different periodic functions for position encoding for features of different dimensions, a feature vector 545 with position encoding information can be obtained. In this manner, the time-consuming prediction model 520 may further divide the initial feature vector 535 into an intermediate feature representation that is more affected by position-coded pos and an intermediate feature representation that is less affected by pos.

Compared with the method that the position code is directly used as the input of the model, the method further considers the relevance of the position code and the specific characteristic in the traffic attribute, and the input attribute is distinguished into the intermediate characteristic representation with large influence of the position code and the intermediate characteristic representation with small influence of the position code in the characteristic processing stage. For example, position coding may have a greater influence on the dynamic properties of a road segment, while having no or only a minor influence on the static properties. In this way, the embodiment of the disclosure can construct more accurate feature vectors, thereby improving the accuracy of the time-consuming prediction model.

Further, the server 170 may process the feature vectors with position-coding information using a time-consuming prediction model to determine the expected time consumption. With continued reference to the example of fig. 5, the feature vectors 545 with position-coding information may be provided, for example, to an intermediate processing layer 550 of the time-consuming prediction model 520, and the expected time-consuming 570 is output via an output layer 560.

The time-consuming prediction model 520 is illustrated in FIG. 5 as a deep neural network. Deep neural networks have a hierarchical architecture, with each processing layer (also referred to as a network layer) having one or more processing units (also referred to as processing nodes, neurons, or filters) that process inputs based on corresponding parameters. In a deep neural network, the output of the previous layer after processing is the input of the next layer, where the first layer in the architecture receives the network input for processing, and the output of the last layer is provided as the network output. The parameters used by all processing units of time consuming prediction model 520 for processing constitute a set of parameters of time consuming prediction model 520. The specific values of such parameter sets need to be determined by a training process.

It should be understood that the architecture of the time-consuming prediction model 520 shown in FIG. 5, and the number of processing layers and processing units therein, are illustrative and not limiting. The time-consuming prediction model 520 may be designed with other suitable architectures and/or suitable number of processing layers, each of which may have a suitable number of processing units, as desired.

In some embodiments, the time-consuming prediction model 520 may be trained based on historical trips, for example. Specifically, the time-consuming prediction model 520 may obtain traffic attributes of a plurality of road segments corresponding to a set of historical trips and position-coded information corresponding to the plurality of road segments. During the training process, the convergence condition may be achieved by adjusting the parameter values of the time-consuming predictive model 520. The convergence condition may be, for example, such that the predicted time consumption of the time consumption prediction model 520 for the historical trip is close to the corresponding actual time consumption.

Based on the determination method of the estimated time consumption discussed above, embodiments of the present disclosure can consider not only the traffic properties of the road segment itself, but also the position of the road segment in the trip (which is represented, for example, as a position code). Based on the mode, the embodiment of the disclosure can effectively distinguish the corresponding feature vectors of the same road section in different travels, thereby improving the accuracy of predicting time consumption.

In some embodiments, the server 170 may also determine an expected time of arrival for the trip based on the expected elapsed time and transmit the expected time of arrival to the terminal device 160 associated with the trip. For example, in some scenarios discussed with reference to fig. 1, the user may be more interested in the estimated time of arrival ETA, at which point the server 170 may determine the estimated time of arrival based on the current time of day and the estimated elapsed time and send that time to the terminal device 160 for presentation.

In some embodiments, the projected time of arrival determined by server 170 may be used, for example, for scheduling of vehicles in a transit travel platform in addition to the scenario discussed with reference to fig. 1. When scheduling vehicles for a plurality of users in an area, it is not usually simple to schedule the closest vehicle for the user, but for example to select a scheduling manner that minimizes the total expected time consumption required for the plurality of vehicles to the corresponding passengers. It should be appreciated that the projected time-consumption of the trip or the projected time of arrival ETA is an important indicator in intelligent transportation travel, and may be used in any other suitable aspect. By improving the accuracy of the estimated time consumption, embodiments of the present disclosure may, for example, improve the efficiency of vehicle dispatch, help users (passengers or drivers) schedule more reasonably, and the like.

It should be appreciated that while the expected time consumption determination method implemented in accordance with the present disclosure is discussed above with reference to server 170, the above method may also be performed by terminal device 160 utilizing local resources and/or remote resources (e.g., a route segment table maintained by the server). The description will not be repeated here.

Embodiments of the present disclosure also provide corresponding apparatuses for implementing the above methods or processes. Fig. 6 illustrates a schematic block diagram of an apparatus 600 for trip management according to some embodiments of the present disclosure.

As shown in fig. 6, the apparatus 600 may include a road segment determination module 610 configured to determine a plurality of road segments associated with a trip. Furthermore, the apparatus 600 further comprises a position code determination module 620 configured to determine position codes for the plurality of road segments, one position code indicating an order in which a respective one of the plurality of road segments is to be passed through in the journey. The apparatus 600 further comprises a time-consumption determination module 630 configured to determine a projected time-consumption for the trip based on traffic attributes and the location codes of the plurality of road segments, the traffic attributes indicating at least a current traffic state of the plurality of road segments.

In some embodiments, the elapsed time determination module 630 includes: the characteristic vector generation module is configured to generate characteristic vectors with position coding information based on the traffic attributes and the position codes of the road sections; and a time-consuming prediction module configured to determine a predicted time-consuming based on the feature vector with the position-coding information.

In some embodiments, the feature vector generation module comprises: an initial feature vector determination module configured to generate an initial feature vector based on the traffic attribute; a weight coefficient determination module configured to determine weight coefficients associated with different dimensions of the initial feature vector based on the position encoding; a weighting module configured to apply a weighting coefficient to the initial feature vector to obtain a feature vector with position-coding information.

In some embodiments, the weight coefficient determination module comprises: a periodic function determination module configured to determine periodic functions having different periods corresponding to different dimensions; and a weight coefficient calculation module configured to determine a weight coefficient based on the position code and the periodic function.

In some embodiments, the time-consuming prediction module comprises: a model processing module configured to process the feature vectors with the position-coding information using a time-consuming prediction model to determine a predicted time-consuming, wherein the time-consuming prediction model is trained based on segment information corresponding to a set of historical trips and actual time-consuming information.

In some embodiments, the apparatus 600 further comprises: an estimated time of arrival determination module configured to determine an estimated time of arrival for the trip based on the estimated elapsed time; and a transmitting module configured to transmit the expected time of arrival to a terminal device associated with the trip.

Fig. 7 illustrates a block diagram of a computing device/server 700 in which one or more embodiments of the present disclosure may be implemented. It should be understood that the computing device/server 700 illustrated in FIG. 7 is merely exemplary and should not be construed as limiting in any way the functionality and scope of the embodiments described herein.

As shown in fig. 7, computing device/server 700 is in the form of a general purpose computing device. Components of computing device/server 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. The processing unit 710 may be a real or virtual processor and may be capable of performing various processes according to programs stored in the memory 720. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of computing device/server 700.

Computing device/server 700 typically includes a number of computer storage media. Such media may be any available media that is accessible by computing device/server 700 and includes, but is not limited to, volatile and non-volatile media, removable and non-removable media. Memory 720 may be volatile memory (e.g., registers, cache, Random Access Memory (RAM)), non-volatile memory (e.g., Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory), or some combination thereof. Storage 730 may be a removable or non-removable medium and may include a machine-readable medium, such as a flash drive, a magnetic disk, or any other medium, which may be capable of being used to store information and/or data (e.g., training data for training) and which may be accessed within computing device/server 700.

Computing device/server 700 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 720 may include a computer program product 725 having one or more program modules configured to perform the various methods or acts of the various embodiments of the disclosure.

Communication unit 740 enables communication with other computing devices over a communication medium. Additionally, the functionality of the components of computing device/server 700 may be implemented in a single computing cluster or multiple computing machines capable of communicating over a communications connection. Thus, computing device/server 700 may operate in a networked environment using logical connections to one or more other servers, network Personal Computers (PCs), or another network node.

Input device 750 may be one or more input devices such as a mouse, keyboard, trackball, or the like. Output device 760 may be one or more output devices such as a display, speakers, printer, or the like. Computing device/server 700 may also communicate with one or more external devices (not shown), such as storage devices, display devices, etc., as desired through communication unit 740, with one or more devices that enable a user to interact with computing device/server 700, or with any device (e.g., network card, modem, etc.) that enables computing device/server 700 to communicate with one or more other computing devices. Such communication may be performed via input/output (I/O) interfaces (not shown).

According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the above-described method.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the implementations disclosed herein.

Claims

1. A method of trip information processing, comprising:

determining a plurality of road segments associated with a trip;

determining position codes for the plurality of road segments, one position code indicating an order in which a respective one of the plurality of road segments was passed through the journey; and

determining a projected time of the trip based on traffic attributes of the plurality of road segments and the location code, the traffic attributes indicating at least a current traffic state of the plurality of road segments.

2. The method of claim 1, wherein determining the projected elapsed time of the trip comprises:

generating a feature vector with position coding information based on the traffic attributes of the plurality of road segments and the position codes; and

determining the predicted elapsed time based on the feature vector with position-coding information.

3. The method of claim 2, wherein generating the feature vector with position-coding information comprises:

generating an initial feature vector based on the traffic attributes;

determining, based on the position encoding, weight coefficients associated with different dimensions of the initial feature vector;

applying the weight coefficients to the initial feature vector to obtain the feature vector with position-coding information.

4. The method of claim 3, wherein determining the weight coefficients comprises:

determining periodic functions with different periods corresponding to the different dimensions; and

determining the weight coefficients based on the position code and the periodic function.

5. The method of claim 2, wherein determining the projected elapsed time comprises:

processing the feature vector with position-coding information using a time-consuming prediction model to determine the predicted time-consuming,

wherein the time-consuming prediction model is trained based on road segment information corresponding to a set of historical trips and actual time-consuming information.

6. The method of claim 1, further comprising:

determining an estimated time of arrival of the trip based on the estimated elapsed time; and

transmitting the estimated time of arrival to a terminal device associated with the trip.

7. An apparatus for trip information processing, comprising:

a road segment determination module configured to determine a plurality of road segments associated with a trip;

a position code determination module configured to determine position codes for the plurality of road segments, one position code indicating an order in which a respective one of the plurality of road segments was passed in the journey; and

a time-consumption determination module configured to determine a projected time-consumption of the trip based on traffic attributes of the plurality of road segments and the location code, the traffic attributes indicating at least a current traffic state of the plurality of road segments.

8. The apparatus of claim 7, wherein the elapsed time determination module comprises:

a feature vector generation module configured to generate a feature vector with position code information based on the traffic attributes of the plurality of road segments and the position codes; and

a time-consuming prediction module configured to determine the predicted time-consuming based on the feature vector with position-coding information.

9. The apparatus of claim 8, wherein the feature vector generation module comprises:

an initial feature vector determination module configured to generate an initial feature vector based on the traffic attribute;

a weight coefficient determination module configured to determine weight coefficients associated with different dimensions of the initial feature vector based on the position encoding;

a weighting module configured to apply the weighting coefficients to the initial feature vector to obtain the feature vector with position-coding information.

10. The device of claim 9, wherein the weight coefficient determination module comprises:

a periodic function determination module configured to determine periodic functions having different periods corresponding to the different dimensions; and

a weight coefficient calculation module configured to determine the weight coefficient based on the position code and the periodic function.

11. The apparatus of claim 8, wherein the time-consuming prediction module comprises:

a model processing module configured to process the feature vector with position-coding information using a time-consuming prediction model to determine the predicted time-consuming,

12. The apparatus of claim 7, further comprising:

an estimated time of arrival determination module configured to determine an estimated time of arrival for the trip based on the estimated elapsed time; and

a transmitting module configured to transmit the estimated time of arrival to a terminal device associated with the trip.

13. An electronic device, comprising:

a memory and a processor;

wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1 to 6.

14. A computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method of any one of claims 1 to 6.