Travel route planning method and device and storage medium

Abstract

The invention discloses a travel route planning method, planning equipment and a storage medium, and the method comprises the steps: obtaining the travel demand information of a user, generating a plurality of candidate travel routes meeting the travel demand information of the user, and according to the predicted travel environment information and predicted travel time consumption corresponding to each initially planned candidate travel route, determining the travel demand information of the user; and carrying out route optimization processing, and determining an optimal travel route in the plurality of candidate travel routes. Therefore, the feasibility of the candidate travel routes can be evaluated by combining the predicted travel environment information and the predicted travel time consumption corresponding to the candidate travel routes, so that the optimal travel route can be determined from the plurality of candidate travel routes, and the travel environment information and the travel time consumption can be considered during route planning; therefore, the planned optimal travel route is more reasonable, and the accuracy and reliability of the optimal travel route are improved.

Description

Technical Field

[0001] This application belongs to the technical field of route planning, and particularly relates to a method, device, and storage medium for planning a travel route. Background Art

[0002] Currently, there are many navigation software that can implement the planning of travel routes. In daily life, users are accustomed to using software to plan travel routes before traveling and determine the departure time according to the travel duration required by the travel routes planned by the software. However, due to the complex and changeable factors affecting travel such as traffic conditions and weather, the travel routes may change in real time. When the travel route changes, the travel duration will also change accordingly. Therefore, users often face problems such as difficulty in controlling travel time, making the reliability of travel routes insufficient.

[0003] Therefore, there is an urgent need for a method to be able to plan a more reliable travel route. Summary of the Invention

[0004] The embodiments of this application provide a method, device, and storage medium for planning a travel route, which can plan a more reliable and effective travel route.

[0005] In a first aspect, the embodiments of this application provide a method for planning a travel route, the method including:

[0006] Obtain the travel demand information of the user, where the travel demand information includes the departure location and the destination location;

[0007] In response to the travel demand information, plan and generate multiple candidate travel routes from the departure location to the destination location;

[0008] For each candidate travel route among the multiple candidate travel routes, predict the predicted travel environment information of the candidate travel route in the future time period, and, predict the predicted travel time on the candidate travel route, where the predicted travel environment information is information associated with environmental factors that affect the user's travel;

[0009] Perform route optimization processing according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes, and determine the optimal travel route among the multiple candidate travel routes.

[0010] In some embodiments, the above-mentioned predicting the predicted travel environment information of the candidate travel route in the future time period includes: obtaining the real-time travel environment information and historical travel environment information corresponding to the candidate travel route; inputting the real-time travel environment information and historical travel environment information into the first prediction model, and predicting the predicted travel environment information of the candidate travel route in the future time period.

[0011] In some embodiments, in the first prediction model, the weight of real-time travel environment information is higher than the weight of historical travel environment information.

[0012] In some embodiments, the predicted travel environment information includes at least one of the following:

[0013] Predicted traffic flow information;

[0014] Predicted weather information;

[0015] Predicted sudden traffic accident information;

[0016] Predicted road construction information.

[0017] In some embodiments, the predicted travel time on a candidate travel route includes: obtaining at least one piece of historical travel trajectory data of the user, where each piece of historical travel trajectory data includes a historical travel route and a historical travel time; among the at least one piece of historical travel trajectory data, determining target historical travel trajectory data whose included historical travel route matches the candidate travel route; and based on the historical travel time of the target historical travel trajectory data, determining the predicted travel time on the candidate travel route.

[0018] In some embodiments, the historical travel trajectory data further includes historical travel environment information. Determining target historical travel trajectory data whose included historical travel route matches the candidate travel route among the at least one piece of historical travel trajectory data includes: respectively matching the candidate travel route and the corresponding travel environment information with each piece of historical travel trajectory data, and determining target historical travel trajectory data among the at least one piece of historical travel trajectory data, where the historical travel route of the target historical travel trajectory data matches the candidate travel route, and the historical travel environment information of the target historical travel trajectory data matches the travel environment information corresponding to the candidate travel route.

[0019] In some embodiments, the historical travel trajectory data further includes historical vehicle state information, and the historical vehicle state information of the target historical travel trajectory data matches the real-time vehicle state information of the user.

[0020] In some embodiments, before determining the optimal travel route among multiple candidate travel routes according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes and performing route optimization processing, it further includes: determining the travel portrait of the user according to the historical travel trajectory data of the user; determining the optimal travel route among the multiple candidate travel routes according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes and performing route optimization processing, including: taking the predicted travel environment information and predicted travel time as optimization objectives and the travel portrait as an optimization constraint, and determining the optimal travel route among the multiple candidate travel routes.

[0021] In some embodiments, after determining the optimal travel route among multiple candidate travel routes, the following steps are further included: generating travel advice information associated with the optimal travel route; outputting the travel advice information so as to advise the user to travel based on the optimal travel route.

[0022] In some embodiments, the above-mentioned travel advice information includes the departure time; after generating the travel advice information associated with the optimal travel route, the following steps are further included: obtaining the travel distance of the optimal travel route; determining the advance time corresponding to the optimal travel route according to the travel distance; determining the reminder time based on the departure time and the advance time; outputting the reminder time to remind the user to travel according to the optimal travel route at the reminder time.

[0023] In a second aspect, an embodiment of the present application further provides a travel route planning device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the travel route planning method described in any item of the first aspect is implemented.

[0024] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the travel route planning method described in any item of the first aspect is implemented.

[0025] The travel route planning method, planning device and storage medium of the embodiments of the present application perform route optimization processing by according to the predicted travel environment information and predicted travel time corresponding to each initially planned candidate travel route, and determine the optimal travel route among multiple candidate travel routes. In this way, the present application can evaluate the feasibility of candidate travel routes by combining the predicted travel environment information and predicted travel time corresponding to the candidate travel routes, so as to determine the optimal travel route from multiple candidate travel routes, and can consider the travel environment information and travel time when planning the route, thereby making the planned optimal travel route more reasonable, and further improving the accuracy and reliability of the optimal travel route. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0027] Figure 1 is a flowchart of a travel route planning method provided by the present application;

[0028] Figure 2 is a flowchart of obtaining predicted travel environment information provided by the present application;

[0029] Figure 3 is a schematic flow chart for obtaining predicted travel time provided by this application;

[0030] Figure 4 is a flow chart of an optimization algorithm provided by an embodiment of this application;

[0031] Figure 5 is a schematic structural diagram of a travel route planning device provided by an embodiment of this application;

[0032] Figure 6 is a schematic structural diagram of a travel route planning device provided by an embodiment of this application. Detailed implementation manners

[0033] The features and exemplary embodiments of various aspects of this application will be described in detail below. For the purpose of making the objectives, technical solutions and advantages of this application more clear and understandable, the following further describes this application in detail in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application, rather than to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of this application by showing examples of this application.

[0034] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without more limitations, elements defined by the statement "including..." do not exclude the existence of additional identical elements in the process, method, article or device including the said elements.

[0035] An embodiment of this application provides a travel route planning method. The travel route planning method can be applied to a travel route planning device. The travel route planning device can be a body controller, a vehicle terminal, a cloud server, or other servers or vehicles externally connected to the vehicle, etc., or can also be an electronic device including a body controller, a vehicle terminal, a cloud server, or other servers or vehicles externally connected to the vehicle. The travel route planning method is executed by the above-mentioned travel route planning device.

[0036] For ease of understanding, a specific description of the method for planning a travel route is given here, with reference to Figure 1 , such as Figure 1 FIG. Figure 1 is a schematic flowchart of a method for planning a travel route provided by an embodiment of the present application. As shown in Figure 1 , the method for planning the travel route is specifically as shown in steps S101-S104:

[0037] Step S101, obtain the travel demand information of the user.

[0038] Step S102, in response to the travel demand information, plan and generate multiple candidate travel routes from the departure location to the destination location.

[0039] Step S103, for each candidate travel route among the multiple candidate travel routes, predict the predicted travel environment information of the candidate travel route in the future time period, and, predict the predicted travel time on the candidate travel route.

[0040] Step S104, perform route optimization processing according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes, and determine the optimal travel route among the multiple candidate travel routes.

[0041] In step S101, the above-mentioned obtaining of the travel demand information of the user may be determined according to the received travel demand operation of the user in the travel route planning device. The travel demand operation may include at least one of voice operation, touch operation, and air gesture operation, etc.; or, the travel route planning device may be communicatively connected to the user terminal used by the user. The user terminal may be, for example, a mobile phone, a tablet, a computer, etc. used by the user. The travel route planning device obtains the travel plan of the user from the user terminal of the user. For example, it may be to read the travel plan of the user from the travel memo of the user terminal, or, the user may input the travel demand information on the user terminal interface, and the travel route planning device realizes obtaining the travel demand information of the user by receiving the travel demand information output from the user terminal. Among them, the user terminal provides an input interface and supports at least one of multiple input methods such as voice input and map point selection. The user terminal may also be a smart speaker.

[0042] The above-mentioned travel demand information includes the departure location and the destination location. The departure location can be understood as the location where the user gets on the vehicle, and the destination location can be understood as the destination to be reached. The travel demand information may also include information such as the travel mode (self-driving, public transportation, etc.) and the expected departure time.

[0043] In step S102, in response to the travel demand information, planning and generating multiple candidate travel routes from the departure location to the destination location may be that the travel route planning device plans and generates travel routes from the departure location to the destination location passing through different road segments as multiple candidate travel routes through at least one preset path planning algorithm.

[0044] The above-mentioned preset path algorithms may include at least one of the A* algorithm, Dijkstra algorithm, D algorithm (Dynamic A Algorithm), Rapidly-exploring Random Tree (RRT) algorithm, and Dynamic Programming algorithm, etc.

[0045] The above-mentioned road segment can be understood as a section of road or path in physical space, which can be a street, a highway, a small path, or any other form of actual path.

[0046] In step S103, for each candidate travel route among the multiple candidate travel routes, the predicted travel environment information of the candidate travel route in the future time period can be predicted through the first preset model.

[0047] The above-mentioned predicted travel environment information is information associated with environmental factors that affect the user's travel.

[0048] In one implementation, the above-mentioned future time period may be the time period from the expected departure time in the travel demand information to the first preset moment, where the first preset moment is greater than the departure time. In another implementation, the above-mentioned future time period may also be the time period from the second preset moment to the first preset moment, where the second preset moment is less than the departure time.

[0049] In step S104, through the optimization algorithm, route optimization processing is realized according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes, and the optimal travel route is determined among the multiple candidate travel routes.

[0050] Optimization is achieved through an optimization algorithm, with the goal of predicting travel environment information and predicted travel time. The optimal travel route is determined by presetting reasonable optimization parameters (such as population size, crossover probability, mutation probability, etc. in the genetic algorithm). The optimization algorithm can be a constrained optimization algorithm (such as Lagrange multiplier method, penalty function method, interior point method), a multi-objective optimization algorithm (such as NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOEA / D (Multi-Objective Evolutionary Algorithm Based on Decomposition), SPEA2 (Strength Pareto Evolutionary Algorithm 2)), a global optimization algorithm (such as Bayesian optimization, DIRECT (Dividing Rectangles), CMA-ES (Covariance Matrix Adaptation Evolution Strategy)), a heuristic optimization algorithm (such as genetic algorithm, particle swarm optimization, ant colony algorithm, simulated annealing, differential evolution), etc.

[0051] When adopting a multi-objective optimization algorithm, factors such as the degree of influence of travel environment information and travel costs can also be comprehensively considered to recommend the best travel route for users.

[0052] In one implementation, the parameters in the optimization algorithm can be adjusted during the use of the optimization algorithm, such as the population size, crossover probability, mutation probability, etc. in the genetic algorithm, to improve the convergence speed and solution quality of the algorithm.

[0053] In the embodiments of the present application, route optimization processing is performed according to the predicted travel environment information and predicted travel time corresponding to each candidate travel route in the initial plan, and the optimal travel route is determined among multiple candidate travel routes. In this way, the present application can combine the predicted travel environment information and predicted travel time corresponding to the candidate travel routes to evaluate the feasibility of the candidate travel routes, so as to determine the optimal travel route among multiple candidate travel routes, and can consider the travel environment information and travel time when planning the route, thereby making the planned optimal travel route more reasonable, and further improving the accuracy and reliability of the optimal travel route.

[0054] Refer to Figure 2 , Figure 2 which is a schematic flowchart of the process for obtaining predicted travel environment information provided by the embodiments of the present application.

[0055] In some embodiments, the above step S103 may further include but is not limited to the following steps:

[0056] Obtain real-time travel environment information and historical travel environment information corresponding to the candidate travel route.

[0057] The travel environment information may include at least one of traffic flow information, weather information, road construction information, sudden traffic accident information, and public transportation operation information.

[0058] The traffic flow information may include information such as traffic volume, vehicle speed, and congestion level of each road section. The weather information may include temperature, humidity, rainfall, snowfall, visibility, etc. The road construction information may include construction records such as construction sections, construction time, and construction impact scope. The public transportation operation information may include operation information of public transportation such as buses and subways, such as departure time, arrival time, and passenger capacity.

[0059] In some embodiments, the above-mentioned predicted travel environment information includes at least one of predicted traffic flow information, predicted weather information, predicted sudden traffic accident information, and predicted road construction information. The above-mentioned real-time travel environment information includes at least one of real-time traffic flow information, real-time weather information, real-time sudden traffic accident information, and real-time road construction information.

[0060] In this embodiment, since traffic flow information, weather information, sudden traffic accident information, and road construction information are all information with a greater impact on the travel route, therefore, obtaining predicted travel environment information containing the above information can better guide the planning of the travel route in the current situation and improve the effectiveness of travel route planning.

[0061] In this embodiment, the current real-time travel environment information can be obtained in real time through navigation traffic data, in-vehicle GPS devices, mobile communication base stations, etc.

[0062] Input the real-time travel environment information and historical travel environment information into the first prediction model to predict the predicted travel environment information of the candidate travel route in the future time period.

[0063] The first prediction model is used to predict travel environment information. The first prediction model can be a time series model or a deep learning model.

[0064] The model structure of the first prediction model can adopt deep learning model structures such as long short-term memory network (LSTM). The model training can use historical travel environment information to train the model to optimize the model parameters and improve the prediction accuracy. The model evaluation can evaluate the performance of the model through methods such as cross-validation to ensure the prediction accuracy of the model in different time periods and different road sections.

[0065] For example, for traffic flow information, a traffic flow prediction model can be constructed based on historical traffic flow information and real-time traffic flow information. The traffic flow prediction model is used to predict the traffic flow change trends of each road section at different time periods, providing a basis for route evaluation.

[0066] In some embodiments, when inputting real-time travel environment information and historical travel environment information into the first prediction model, the first prediction model can also perform the following steps:

[0067] Data preprocessing can be performed on the historical travel environment information and real-time travel environment information. Specifically, the collected real-time travel environment information and historical travel environment information are cleaned, fused, and standardized. This is to achieve cleaning abnormal data and noise data, such as removing data points with extremely high speeds, fusing data from different sources, and establishing a unified data format and timestamp for subsequent analysis.

[0068] Feature engineering can also be used to extract key features and construct a comprehensive feature vector. Specifically, features related to route evaluation are extracted from the fused data, such as road section congestion features, weather impact features, construction impact features, etc. The features are dimensionally reduced, and redundant features are removed to improve the computational efficiency and accuracy of the model.

[0069] In this embodiment, the change trend of travel environment information is obtained through real-time travel environment information and historical travel environment information to effectively predict travel environment information.

[0070] In some embodiments, in the first prediction model of step S103 above, reasonable weights are set for real-time travel environment information and historical travel environment information to make them play an important role in decision-making. In one implementation, the weight of real-time travel environment information is higher than that of historical travel environment information. For example, in the first prediction model, the weight of historical travel environment information can be set to 0.3, while the weight of real-time travel environment information is 0.7; or, the weight of historical travel environment information can also be set to 0.5, while the weight of real-time travel environment information is 0.5, and so on.

[0071] In this embodiment, reasonable weights are set for real-time travel environment information and historical travel environment information, and the weight of real-time travel environment information is higher than that of historical travel environment information to fully utilize real-time travel environment information to guide the current travel.

[0072] Refer to Figure 3 , Figure 3 which is a schematic flowchart of the process for obtaining the predicted travel time provided by the embodiments of the present application.

[0073] In some embodiments, step S103 above may include but is not limited to the following steps:

[0074] Obtain at least one piece of historical travel trajectory data of the user.

[0075] Among them, each piece of historical travel trajectory data in the at least one piece of historical travel trajectory data of the above user includes a historical travel route and a historical travel time consumption.

[0076] In one implementation manner, the above historical travel trajectory data may further include information such as historical travel time, driving speed, and stop points.

[0077] In one implementation manner, the historical travel trajectory data of the user can be recorded by the GPS device of the user terminal or the travel route planning device, so that the travel route planning device can obtain the historical travel trajectory data of the user.

[0078] Among the at least one piece of historical travel trajectory data, determine the target historical travel trajectory data whose included historical travel route matches the candidate travel route.

[0079] In this embodiment, by matching the candidate travel route with each piece of historical travel trajectory data in the above at least one piece of historical travel trajectory data respectively, the target historical travel trajectory data matching the candidate travel route is obtained, where the historical travel route included in the target historical travel trajectory data matches the candidate travel route.

[0080] Based on the historical travel time consumption of the target historical travel trajectory data, determine the predicted travel time consumption on the candidate travel route.

[0081] In one implementation manner, a second prediction model is established. The second prediction model is used to predict the travel time consumption. Input at least one piece of historical travel trajectory data of the user and the candidate travel route into the second prediction model, and obtain the predicted travel time consumption output by the second prediction model. The second prediction model can adopt a travel time consumption estimation method based on the target historical travel trajectory data, find the target historical travel trajectory data similar to the candidate travel route from the historical travel trajectory data, and use the historical travel time consumption of the target historical travel trajectory data to estimate the predicted travel time consumption of the current candidate travel route.

[0082] The model structure of the second prediction model can be a deep neural network (DNN). The model training uses the historical travel trajectory data and real-time travel environment information to train the model to optimize the model parameters and improve the accuracy of travel time consumption estimation, and then conducts model evaluation to evaluate the performance of the model under different travel conditions to ensure that the model can accurately estimate the travel time consumption of different routes.

[0083] In this embodiment, since the target historical travel trajectory data matches the candidate travel route, the similarity between the historical travel route in the target historical travel trajectory data and the candidate travel route is relatively high. Therefore, based on the historical travel time of the target historical travel trajectory data, the predicted travel time corresponding to the candidate travel route can be effectively determined.

[0084] In some embodiments, the above historical travel trajectory data may further include historical travel environment information.

[0085] In some embodiments, the steps of determining the target historical travel trajectory data may further include, but are not limited to, the following steps:

[0086] Match the candidate travel route and the corresponding travel environment information with each historical travel trajectory data respectively, and determine the target historical travel trajectory data from at least one historical travel trajectory data.

[0087] Among them, the travel environment information corresponding to the candidate travel route may be predicted travel environment information or real-time travel environment information. The historical travel route of the target historical travel trajectory data matches the candidate travel route, and the historical travel environment information of the target historical travel trajectory data matches the travel environment information corresponding to the candidate travel route.

[0088] In one implementation manner, the matching method may be: obtain the similarity between the candidate travel route and the corresponding travel environment information and each historical travel trajectory data respectively, compare the similarity with a preset threshold (such as 80%), and determine the historical travel trajectory data corresponding to the target similarity as the target historical travel trajectory data, where the target similarity is greater than the preset threshold. Among them, if there are multiple target similarities, the historical travel trajectory data corresponding to the maximum target similarity among the multiple target similarities may be determined as the target historical travel trajectory data.

[0089] In one implementation manner, input at least one historical travel trajectory data of the user, the candidate travel route, and the corresponding travel environment information into the second prediction model, and obtain the predicted travel time output by the second prediction model.

[0090] In this embodiment, taking the travel environment information as another condition for matching can improve the matching degree between the target historical travel trajectory data and the candidate travel route. Based on the similar travel environment information and historical travel route, the predicted travel time corresponding to the candidate travel route can be determined more effectively.

[0091] In some embodiments, the above historical travel trajectory data further includes historical vehicle state information. Taking the historical vehicle state information as another condition for matching, the historical vehicle state information of the target historical travel trajectory data matches the real-time vehicle state information of the user.

[0092] The operating state of the vehicle can be monitored in real time by using vehicle state sensors. Additionally, by leveraging Internet of Things technology, the real-time vehicle state information detected by the vehicle state sensors can be transmitted to the travel route planning device to obtain more comprehensive real-time information.

[0093] In this embodiment, using the vehicle state information as another matching condition can further improve the matching degree between the target historical travel trajectory data and the candidate travel routes, and further enhance the effectiveness of determining the predicted travel time.

[0094] In some embodiments, before performing step S104, it may further include but is not limited to the following steps:

[0095] Determine the travel portrait of the user based on the user's historical travel trajectory data.

[0096] The above-mentioned travel portrait includes at least one of travel preferences and travel patterns. The travel pattern can be understood as the travel tool used, such as self-driving, taking the subway, taking the bus, etc. Travel preferences can be the user's travel preferences and habits at different time periods and under different weather conditions. For example, it is found that the user is more inclined to choose a detour to avoid waterlogged sections on rainy days.

[0097] In one implementation manner, a machine learning algorithm can be used to determine the travel portrait of the user from the user's historical travel trajectory data.

[0098] In some embodiments, the above-mentioned step S104 may include but is not limited to the following steps:

[0099] Taking the predicted travel environment information and the predicted travel time as the optimization objectives, and the travel portrait as the optimization constraint, determine the optimal travel route among multiple candidate travel routes.

[0100] In one implementation manner, a hybrid optimization algorithm combining a genetic algorithm and a particle swarm optimization algorithm can be selected to achieve taking the predicted travel environment information and the predicted travel time as the optimization objectives, and the travel portrait as the optimization constraint, and determine the optimal travel route among multiple candidate travel routes to improve the efficiency and accuracy of optimization.

[0101] In one implementation manner, the embodiments of the present application provide a process for determining the optimal travel route using an optimization algorithm. Refer to Figure 4 , Figure 4 which is the flow chart of the optimization algorithm provided by the embodiments of the present application.

[0102] Taking the predicted travel environment information and the predicted travel time as the optimization objectives, and the travel portrait as the optimization constraint.

[0103] The optimization process is to determine the optimized travel route corresponding to each candidate travel route with the predicted travel environment information and predicted travel time as the optimization objectives and the travel profile as the optimization constraint.

[0104] Then, obtain the evaluation indicators of each optimized travel route, such as the predicted travel time, the degree of influence by the travel environment, etc. According to the evaluation indicators of each optimized travel route, recommend the optimal travel route for the user. Specifically, for each optimized travel route, the evaluation indicators of the optimized travel route can be calculated by weighted summation to obtain the corresponding target value, and the optimized travel route corresponding to the minimum target value among the target values is determined as the optimal travel route to obtain the evaluation result.

[0105] In this embodiment, the travel environment information and travel time predicted by the deep learning model are used as key parameters, and the user's travel preferences and historical travel patterns are incorporated as constraint conditions for route optimization, so that the recommended travel route better meets the actual needs of the user.

[0106] In some embodiments, after performing step S104, it may further include but is not limited to the following steps:

[0107] Generate travel advice information associated with the optimal travel route.

[0108] Generate detailed travel advice based on the optimal travel route obtained by the optimization algorithm. The above travel advice information may include information such as the departure time, the predicted travel time, the route direction, the main intersections or stations passed through, etc. The travel advice information can be prompted in at least one way such as a route map combining text and pictures, voice broadcast, etc.

[0109] Output the travel advice information so that the user is recommended to travel based on the optimal travel route.

[0110] The travel advice information can be output to the user's user terminal for display. The user can view the travel advice information through the user terminal, or the travel advice information can also be displayed through the travel route planning device. The travel advice information can also include alternative travel routes.

[0111] In this embodiment, the user is prompted through the travel advice information, so that the user is more clear about the specific travel plan and the user experience is increased.

[0112] In some embodiments, after generating the travel advice information associated with the optimal travel route, it may further include but is not limited to the following steps:

[0113] Obtain the travel distance of the optimal travel route.

[0114] The travel distance can be calculated by obtaining the distances of each road in the optimal travel route through the Internet of Things technology.

[0115] Determine the advance time corresponding to the optimal travel route according to the travel distance.

[0116] The above advance time is the duration advanced before the departure time. The advance time can be set to different values according to the length of the travel distance corresponding to the optimal travel route. For example, for short-distance travel (such as travel distance less than the first preset distance), the advance time can be 10 minutes, and for long-distance travel (such as travel distance greater than or equal to the first preset distance), the advance time can be 30 minutes, ensuring that users have enough time to prepare for travel.

[0117] In one implementation, preset the correspondence between travel distance and advance time, and determine the corresponding advance time from the correspondence according to the travel distance of the optimal travel route.

[0118] In another implementation, the advance time can be determined by setting a target calculation method, and the target calculation method represents the functional relationship between the advance time and the travel distance, such as calculation methods like the direct proportionality formula.

[0119] Based on the departure time and the advance time, determine the reminder time.

[0120] For example, the reminder time can be expressed as:

[0121] T 提醒 =T 出行 -ΔT;

[0122] where, T 提醒 is the reminder time, T 出行 is the departure time, and ΔT is the advance time.

[0123] Output the reminder time to remind the user to travel according to the optimal travel route at the reminder time.

[0124] In this embodiment, the reminder time can be output to the user terminal used by the user, and the user terminal can send a reminder message to remind the user to travel according to the optimal travel route at the reminder time. For example, "The best departure time for your travel this morning is 7:30. It is recommended that you go out 10 minutes in advance to avoid missing the best travel time."

[0125] When it reaches the reminder time, the user terminal sends a reminder message to remind the user to travel according to the optimal travel route.

[0126] Among them, the user can set travel reminder preferences, such as how long in advance to receive reminders and reminder methods (mobile phone notifications, smart speaker voice, etc.).

[0127] In this embodiment, by calculating the reminder time to send a reminder message to the user, it is possible to prevent the user from missing the best travel time.

[0128] During the user's journey, real-time data can be continuously monitored. If any abnormal situation is found on the originally scheduled route, adjusted travel suggestions will be promptly pushed to the user. The user can also feedback their travel experience according to the actual situation. For example, if they encounter unexpected congested sections, etc., the collected feedback information can be used for subsequent optimization.

[0129] In the embodiments of the present application, through accurate route evaluation and optimization, users can effectively avoid congested sections, areas affected by bad weather, and construction sections, and the prediction error of travel time is significantly reduced, which can increase the probability of arriving at the destination on time.

[0130] In addition, the provided optimal travel route comprehensively considers historical information and real-time information, reducing the probability of users frequently changing routes during the journey, and effectively reducing the troubles and time losses caused by route changes.

[0131] Secondly, personalized travel suggestions meet the diverse needs of users. Information prompts and real-time adjustments during the journey make users' travel more secure and comfortable, enhancing the overall travel experience. The collaborative reminder function of mobile devices and home intelligent devices enables users to obtain travel information more conveniently and avoid missing the best travel time due to forgetting to check their mobile phones.

[0132] Finally, it shortens the travel time and reduces the travel cost. For users, it can arrange work and life more efficiently. For urban traffic, it helps to relieve traffic congestion, improve the utilization rate of road resources, and promote the development of intelligent transportation.

[0133] Figure 5 The structural schematic diagram of the travel route planning device provided by the embodiments of the present application is shown. As Figure 5 shown, the travel route planning device 500 includes:

[0134] An information acquisition module 501, configured to acquire the travel demand information of the user, where the travel demand information includes the departure location and the destination location.

[0135] A route planning module 502, configured to plan and generate multiple candidate travel routes from the departure location to the destination location in response to the travel demand information.

[0136] An information prediction module 503, configured to, for each of the multiple candidate travel routes, predict the predicted travel environment information of the candidate travel route in a future time period, and predict the predicted travel time on the candidate travel route, where the predicted travel environment information is information associated with environmental factors that affect the user's travel.

[0137] A route selection module 504 is configured to perform route optimization processing based on the predicted travel environment information and predicted travel time corresponding to multiple candidate travel routes, and determine an optimal travel route among the multiple candidate travel routes.

[0138] In some embodiments, the above information prediction module 503 may specifically be configured to: obtain real-time travel environment information and historical travel environment information corresponding to a candidate travel route; input the real-time travel environment information and the historical travel environment information into a first prediction model to predict the predicted travel environment information of the candidate travel route in a future time period.

[0139] In some embodiments, in the first prediction model, the weight of the real-time travel environment information is higher than the weight of the historical travel environment information.

[0140] In some embodiments, the predicted travel environment information includes at least one of predicted traffic flow information, predicted weather information, predicted sudden traffic accident information, and predicted road construction information.

[0141] In some embodiments, the above information prediction module 503 may specifically be configured to: obtain at least one historical travel trajectory data of a user, each historical travel trajectory data including a historical travel route and a historical travel time; determine, among the at least one historical travel trajectory data, target historical travel trajectory data whose included historical travel route matches the candidate travel route; and determine the predicted travel time on the candidate travel route based on the historical travel time of the target historical travel trajectory data.

[0142] In some embodiments, the historical travel trajectory data further includes historical travel environment information, and the above information prediction module 503 may specifically be configured to: match the candidate travel route and the corresponding travel environment information with each historical travel trajectory data respectively, and determine target historical travel trajectory data among the at least one historical travel trajectory data, where the historical travel route of the target historical travel trajectory data matches the candidate travel route, and the historical travel environment information of the target historical travel trajectory data matches the travel environment information corresponding to the candidate travel route.

[0143] In some embodiments, the historical travel trajectory data further includes historical vehicle state information, and the historical vehicle state information of the above target historical travel trajectory data matches the real-time vehicle state information of the user.

[0144] In some embodiments, the above route selection module 504 may further specifically be configured to: determine a travel portrait of the user according to the historical travel trajectory data of the user; and determine an optimal travel route among multiple candidate travel routes with the predicted travel environment information and predicted travel time as optimization objectives and the travel portrait as an optimization constraint.

[0145] In some embodiments, the travel route planning device 500 further includes a travel advice module 505. The travel advice module 505 is configured to: generate travel advice information associated with the optimal travel route; output the travel advice information to enable the recommended user to travel based on the optimal travel route.

[0146] In some embodiments, the travel advice module 505 is specifically further configured to: obtain the travel distance of the optimal travel route; determine the advance time corresponding to the optimal travel route according to the travel distance; determine the reminder time based on the departure time and the advance time; output the reminder time to remind the user to travel according to the optimal travel route at the reminder time.

[0147] The travel route planning device 500 provided in the embodiments of the present application can execute the technical solutions shown in the above method embodiments, and the implementation principles and beneficial effects are similar, and will not be described in detail here.

[0148] The embodiments of the present application provide a travel route planning device. The planning device includes: a processor and a memory storing computer program instructions. When the processor executes the computer program instructions, the travel route planning method in the above embodiments is implemented.

[0149] Figure 6 The hardware structure diagram of the travel route planning device provided in the embodiments of the present application is shown.

[0150] The travel route planning device may include a processor 601 and a memory 602 storing computer program instructions.

[0151] Specifically, the above-mentioned processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0152] The memory 602 may include a mass storage for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In a suitable case, the memory 602 may include a removable or non-removable (or fixed) medium. In a suitable case, the memory 602 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, the memory 602 is a non-volatile solid state memory.

[0153] In some embodiments, the memory 602 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0154] The processor 601 realizes the planning method of any one of the travel routes in the above embodiments by reading and executing the computer program instructions stored in the memory 602.

[0155] In one example, the travel route planning device may further include a communication interface 603 and a bus 610. Among them, as Figure 6 shown, the processor 601, the memory 602, and the communication interface 603 are connected through the bus 610 and complete communication with each other.

[0156] The communication interface 603 is mainly used to implement communication between various modules, devices, units, and / or devices in the embodiments of the present application.

[0157] The bus 610 includes hardware, software, or both, and couples the components of the travel route planning device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses or a combination of two or more of these. In a suitable case, the bus 610 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0158] The travel route planning device can execute the travel route planning method in the embodiments of the present application, thereby implementing the travel route planning method and device described in combination with Figures 1 to 5 description.

[0159] In addition, in combination with the method for planning a travel route in the above embodiments, an embodiment of the present application also provides a computer storage medium for implementation. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, the method for planning a travel route in the above embodiments is implemented.

[0160] In combination with the method for planning a travel route in the above embodiments, an embodiment of the present application also provides a computer program product. When the instructions in the computer program product are executed by a processor of a travel route planning device, the travel route planning device implements the method for planning a travel route in the above embodiments.

[0161] In combination with the travel route planning device in the above embodiments, an embodiment of the present application also provides a vehicle, which includes the travel route planning device in the above embodiments.

[0162] It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application.

[0163] It should also be noted that the functional blocks shown in the above structural block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. A "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0164] It also needs to be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps. That is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0165] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0166] As described above, the above is only the specific implementation manner of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and these modifications or substitutions should all be covered within the protection scope of the present application.

Claims

1. A method for planning a travel route, characterized in that, The method includes: Obtaining travel demand information of a user, where the travel demand information includes a departure location and a destination location; In response to the travel demand information, planning and generating multiple candidate travel routes from the departure location to the destination location; For each candidate travel route among the multiple candidate travel routes, predicting the predicted travel environment information of the candidate travel route in a future time period, and predicting the predicted travel time on the candidate travel route, where the predicted travel environment information is information associated with environmental factors that affect the user's travel; Performing route optimization processing according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes, and determining an optimal travel route among the multiple candidate travel routes.

2. The method according to claim 1, characterized in that, The predicting the predicted travel environment information of the candidate travel route in a future time period includes: Obtaining real-time travel environment information and historical travel environment information corresponding to the candidate travel route; Inputting the real-time travel environment information and the historical travel environment information into a first prediction model to predict the predicted travel environment information of the candidate travel route in a future time period.

3. The method according to claim 2, wherein In the first prediction model, the weight of the real-time travel environment information is higher than the weight of the historical travel environment information.

4. The method according to claim 1, wherein The predicted travel environment information includes at least one of the following: Predicted traffic flow information; Predicted weather information; Predicted sudden traffic accident information; Predicted road construction information.

5. The method according to claim 1, wherein The predicting the predicted travel time on the candidate travel route includes: Obtaining at least one piece of historical travel trajectory data of the user, and each piece of historical travel trajectory data includes a historical travel route and a historical travel time; Among the at least one piece of historical travel trajectory data, determining target historical travel trajectory data whose included historical travel route matches the candidate travel route; Based on the historical travel time of the target historical travel trajectory data, determining the predicted travel time on the candidate travel route.

6. The method according to claim 5, characterized in that, The historical travel trajectory data further includes historical travel environment information. Among the at least one piece of historical travel trajectory data, determining the target historical travel trajectory data whose included historical travel route matches the candidate travel route includes: Matching the candidate travel route and the corresponding travel environment information with each piece of historical travel trajectory data respectively, and determining target historical travel trajectory data among the at least one piece of historical travel trajectory data, where the historical travel route of the target historical travel trajectory data matches the candidate travel route, and the historical travel environment information of the target historical travel trajectory data matches the travel environment information corresponding to the candidate travel route.

7. The method according to claim 6, wherein The historical travel trajectory data further includes historical vehicle state information, and the historical vehicle state information of the target historical travel trajectory data matches the real-time vehicle state information of the user.

8. The method according to claim 1, characterized in that, Before performing route optimization processing according to the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes and determining an optimal travel route among the multiple candidate travel routes, it further includes: Determine the travel profile of the user based on the user's historical travel trajectory data; The route optimization process based on the predicted travel environment information and predicted travel time corresponding to the multiple candidate travel routes to determine the optimal travel route among the multiple candidate travel routes includes: Determine the optimal travel route among the multiple candidate travel routes with the predicted travel environment information and the predicted travel time as the optimization objectives and the travel profile as the optimization constraint.

9. The method according to claim 1, characterized in that, After determining the optimal travel route among the multiple candidate travel routes, it further includes: Generate travel advice information associated with the optimal travel route; Output the travel advice information so as to recommend that the user travel based on the optimal travel route.

10. The method according to claim 9, characterized in that The travel advice information includes the departure time; After generating the travel advice information associated with the optimal travel route, it further includes: Obtain the travel distance of the optimal travel route; Determine the advance time corresponding to the optimal travel route according to the travel distance; Determine the reminder time based on the departure time and the advance time; Output the reminder time to remind the user to travel according to the optimal travel route at the reminder time.

11. A device for planning a travel route, characterized in that, The planning device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the travel route planning method according to any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by the processor, they implement the travel route planning method according to any one of claims 1-10.

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