CN115127569A - Personalized intermodal navigation method and system - Google Patents
Personalized intermodal navigation method and system Download PDFInfo
- Publication number
- CN115127569A CN115127569A CN202110335003.0A CN202110335003A CN115127569A CN 115127569 A CN115127569 A CN 115127569A CN 202110335003 A CN202110335003 A CN 202110335003A CN 115127569 A CN115127569 A CN 115127569A
- Authority
- CN
- China
- Prior art keywords
- user
- parameters
- path
- personalized
- travel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000004044 response Effects 0.000 claims abstract description 7
- 238000012546 transfer Methods 0.000 claims description 26
- 238000004088 simulation Methods 0.000 claims description 12
- 238000005457 optimization Methods 0.000 description 5
- 238000013480 data collection Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003032 molecular docking Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3484—Personalized, e.g. from learned user behaviour or user-defined profiles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3423—Multimodal routing, i.e. combining two or more modes of transportation, where the modes can be any of, e.g. driving, walking, cycling, public transport
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Social Psychology (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
The invention provides a personalized navigation method and a system, wherein the method comprises the following steps: receiving a user request for path planning from a user, wherein the user request comprises a path starting point, a path ending point and a starting time; in response to the user request, grading personalized parameters of the user to obtain corresponding weights of the parameters, wherein the personalized parameters comprise personal preference parameters and driving context parameters; simulating the whole traffic system network to obtain an original planned path from the path starting point to the path terminal point, wherein the planned path comprises a travel mode, a travel route and a departure time; and adjusting the original planned path based on the obtained corresponding weight of each parameter so as to recommend an optimal planned path to the user.
Description
Technical Field
The invention relates to the field of road traffic, in particular to a personalized intermodal navigation method and system.
Background
In recent years, with the development of navigation technology, there are various navigation systems for use in vehicles of users, and currently, a navigation system can determine a navigation path provided to a user according to the setting of a route starting point and an end point by the user and a navigation policy (for example, "high speed priority", "shortest distance", or "avoiding charging" or the like) set by the user. The navigation system can also provide optimized path recommendation for the user according to the calculated information such as the number of traffic lights, the speed limit and the like in the path. However, the search result and the planned route obtained by the navigation system for the same service requirement are the same, and personalized factors of the user, such as user preference, current real-time traffic information (such as traffic volume, congestion condition, etc.), weather condition, etc., are not considered when providing the route planning for the user.
Furthermore, current navigation systems can only give suggestions separately for different types of transportation means, for example, the navigation system can give several suggested routes for driving, several suggested routes for riding public transportation, or suggested routes for walking, riding, etc., but will not give suggestions that are most suitable for the user in consideration of these several means. Furthermore, current navigation systems only perform route planning for the individual setting of the starting point and the ending point of the route by the user, and do not perform route planning for the entire traffic system network from the perspective of system optimization.
In addition, many parking transfer (P + R) parking lots are currently being built, allowing people to have more choices when traveling by docking private cars with public transportation systems (e.g., parking lots near subway stations and taking public transportation to arrive at a destination faster), while currently existing navigation systems are unable to provide an intermodal route solution (i.e., a solution for parking transfer (P + R)) according to individual needs.
It would therefore be desirable to provide an improved personalized navigation system that can provide personalized globally optimal route planning based on user portrayal and current real-time changing road conditions, while taking into account intermodal route strategies.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
According to an aspect of the present invention, there is provided a personalized navigation method, the method comprising:
receiving a user request for path planning from a user, wherein the user request comprises a path starting point, a path ending point and a starting time;
in response to the user request, grading personalized parameters of the user to obtain corresponding weights of the parameters, wherein the personalized parameters comprise personal preference parameters and driving context parameters;
simulating the whole traffic system network to obtain an original planned path from the path starting point to the path terminal point, wherein the planned path comprises a travel mode, a travel route and a departure time; and
and adjusting the original planning path based on the obtained corresponding weight of each parameter so as to recommend an optimal planning path to the user.
According to one embodiment of the invention, the personal preference parameters include one or more of vehicle dependency, cost-effectiveness, and time-effectiveness.
According to a further embodiment of the invention, the driving context parameters comprise one or more of road conditions, weather conditions, road network type and traffic density.
According to a further embodiment of the present invention, the scoring the personalized parameters of the user to obtain the corresponding weight of each parameter further comprises:
and scoring the personalized parameters of the user by counting and analyzing the historical travel information of the user to obtain corresponding weights.
According to a further embodiment of the present invention, the travel mode includes a driving-only travel mode or a parking-to-public transportation P + R mode.
According to a further embodiment of the present invention, the adjusting the original planned path based on the obtained respective weights of the parameters further comprises:
and adjusting the original planned path based on the parameters corresponding to higher weights, wherein the higher the weight is, the greater the influence of the parameters corresponding to the weights on the optimal planned path of the user is.
According to a further embodiment of the invention, the method further comprises: and indicating the predicted travel time and road condition information of the optimal planned path to the user.
According to a further embodiment of the invention, the method further comprises: when a route for parking transfer is recommended to the user, a station and a schedule near a parking transfer point are indicated to the user to more facilitate the user's parking transfer.
According to another aspect of the present invention, there is provided a personalized navigation system, the system comprising:
a travel service platform configured to receive a user request for path planning from a user, wherein the user request includes a path start point, a path end point, and a departure time;
a scoring module configured to score personalized parameters of the user to obtain respective weights for the parameters in response to the user request, wherein the personalized parameters include personal preference parameters and driving context parameters;
a simulation module configured to simulate an entire transportation system network to obtain an original planned path from the path start point to the path end point, wherein the planned path includes a travel mode, a travel route, and a departure time; and
a recommendation module configured to adjust the original planned path based on the respective weights of the obtained parameters to recommend an optimal planned path to the user via the travel service platform.
According to one embodiment of the invention, the personal preference parameters comprise one or more of vehicle dependency, cost-effectiveness and time-effectiveness.
According to a further embodiment of the invention, the driving context parameters comprise one or more of road conditions, weather conditions, road network type and traffic density.
According to a further embodiment of the present invention, the scoring the personalized parameters of the user to obtain the corresponding weight of each parameter further comprises:
and scoring the personalized parameters of the user by counting and analyzing the historical travel information of the user to obtain corresponding weights.
According to a further embodiment of the present invention, the travel mode includes a driving-only travel mode or a parking-to-public transportation P + R mode.
According to a further embodiment of the present invention, the adjusting the original planned path based on the obtained respective weights of the parameters further comprises:
and adjusting the original planned path based on the parameters corresponding to higher weights, wherein the higher the weight is, the greater the influence of the parameters corresponding to the weights on the optimal planned path of the user is.
According to a further embodiment of the invention, the recommendation module is further configured to: when a route for parking transfer is recommended to the user, a station and a schedule near a parking transfer point are indicated to the user to more facilitate the user's parking transfer.
Aiming at the problems in the prior art, the invention provides a personalized intermodal navigation method and a personalized intermodal navigation system, and the method and the system at least have the following advantages:
1. creating a user portrait for each user, aiming at the same service requirement, obtaining different search results and different planned routes by different users due to different user preferences, and realizing globally optimal navigation route planning by simulating the whole traffic system network from the perspective of system optimization; and
2. under the condition that a destination can be reached more quickly through other transportation modes, transportation services of all categories such as buses, subways, walks, bikes and the like integrated by a MaaS (travel as a service) platform are utilized, and a solution (P + R) of an intermodal route is provided for a user according to the personalized requirements of the user, so that the user can travel more comfortably and quickly.
These and other features and advantages will become apparent upon reading the following detailed description and upon reference to the accompanying drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.
Drawings
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only some typical aspects of this invention and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.
Fig. 1 is an architectural diagram of a personalized intermodal navigation system according to one embodiment of the invention.
FIG. 2 is a schematic flow diagram of a personalized intermodal navigation method according to one embodiment of the invention.
Detailed Description
The present invention will be described in detail below with reference to the attached drawings, and the features of the present invention will be further apparent from the following detailed description.
Fig. 1 is an architectural diagram of a personalized intermodal navigation system 100 according to one embodiment of the invention. As shown in fig. 1, the personalized intermodal navigation system 100 may include at least a travel service platform 101, a data collection module 102, a data processing module 103, a prediction module 104, a simulation module 105, a scoring module 105, and a recommendation module 107. The system may be implemented in a mobile terminal (e.g., in the form of an applet) or in a vehicle.
The travel service platform 101 may receive user request information about path planning from a user, wherein the user request information includes a path start point, a path end point, and a departure time. In response to a navigation request from a user, data collection module 102 may perform real-time data collection, which may include data indicative of real-time traffic conditions (e.g., real-time traffic flow, congested road segments, etc.), and offline data collection, which may include data stored in a database, such as an offline map. The data processing module 103 may process the collected real-time and offline data to derive the planned plurality of navigation routes based on road network scenarios, traffic scenarios, and demand scenarios. The prediction module 104 may perform traffic prediction (e.g., road condition prediction, traffic flow prediction, etc.), travel time prediction, start and end (OD) demand prediction, etc. on the planned plurality of navigation routes. In some cases, prediction module 104 may build a prediction model based on a deep learning approach to predict traffic conditions for the navigation route, providing the user with route choices such as "shortest time", "least congested".
The present invention adds a scoring module 105, a simulation module 106, and a recommendation module 107 to enable globally optimal route recommendations by simulating for the entire traffic system network from a system optimization perspective and incorporating user portrayal.
The scoring module 105 may record personalized parameters of the user, including personal preference parameters and driving context parameters, and score each parameter to derive a corresponding weight. The personal preference parameters are used to identify the personal preferences of the driver including, but not limited to, vehicle dependency, cost efficiency, and time efficiency. The vehicle dependency refers to the degree of dependence of the user on driving travel, wherein the higher the vehicle dependency of the user, the less prone the user is to select a travel pattern of parking transfer (P + R). Cost-effectiveness means that users tend to select a lower cost travel mode (e.g., less toll-gate passes, etc.). Time-efficiency refers to the user's propensity to select travel patterns with shorter travel times (e.g., select less congested routes). Driving context parameters are used to convey information about the surrounding environment including, but not limited to, road conditions, weather conditions, road network type, and traffic density. Further, the scoring module 105 may score the personalized parameters of each user by counting and analyzing the historical travel information of each user to obtain a corresponding weight.
As an example, table 1 below shows the weights of the respective parameters statistically derived for the BMW owner and the non-BMW owner, respectively.
| BMW vehicle owner | non-BMW vehicle owner | |
| Dependence of vehicle | 6.6 | 0.2 |
| Time efficiency | 0.15 | 0.25 |
| Cost-effective | 0.15 | 0.25 |
| Road conditions | 0.05 | 0.15 |
| Weather conditions | 0.05 | 0.15 |
Table 1: weights for each parameter of BMW vehicle owners and non-BMW vehicle owners
As can be seen in table 1, the vehicle dependency of the BMW owner is weighted 6.6, while the time-efficiency, cost-efficiency, road and weather conditions are weighted lower, 0.15, 0.05 and 0.05, respectively. In contrast, the vehicle dependency of non-BMW owners is weighted only 0.2, while the time-efficiency, cost-efficiency, road and weather conditions are weighted higher than BMW owners, 0.25, 0.15, and 0.15, respectively. It follows that BMW owners prefer to select a travel mode that only drives for travel, with little regard to the influence of road conditions and weather conditions on travel, while non-BMW owners prefer to select a travel mode that has a shorter travel time and a lower travel cost, for example, during morning and evening peaks, parking and transferring public transportation are more time-efficient and cost-efficient, and therefore non-BMW owners prefer to select public transportation such as parking cars in parking lots at subway exits and transferring subways. In the case as described above, when the personalized navigation route is recommended for the BMW owner, a travel mode and a travel route for driving only to travel are more appropriate, and when the personalized navigation route is recommended for the non-BMW owner, a travel mode and a travel route having a shorter travel time and a lower travel cost are more appropriate.
The simulation module 106 may simulate the entire traffic system network, and simulate the route planning result from the starting point to the ending point of the route from the perspective of system optimization. As an example, a starting point and an ending point of a path of 40 ten thousand users may be obtained from a travel service platform and a simulation of a centralized route is performed, so that a globally optimal navigation route plan may be provided for the users.
Fig. 2 is a schematic flow diagram of a personalized intermodal navigation method 200 according to one embodiment of the invention. The method 200 begins at step 201, where the travel service platform 101 receives a user request for route planning, where the user request includes a route start point, a route end point, and a departure time.
In step 202, the scoring module 105 scores personalized parameters of the user to obtain corresponding weights of the parameters in response to the user request, wherein the personalized parameters include personal preference parameters and driving context parameters. The higher the weight of a parameter, the greater the influence of that parameter on the personalized navigation route of the user. As an example, the weight for the vehicle dependency of user a is high and the weights for the time-efficiency and cost-efficiency are low, and even in a case where the road conditions are comparatively congested during, for example, the morning and evening rush hour, user a is unlikely to select a travel mode for parking and transfer. As another example, the weight for vehicle dependency of user B is lower and the weight for time-efficiency and cost-efficiency is higher, then in case of traffic congestion, user B is more likely to select a travel pattern for parking transfer due to lower travel cost and shorter travel time.
At step 203, the simulation module 106 simulates for the entire transportation system network to obtain an original planned path from a path start point to a path end point, wherein the planned path includes information about a travel mode, which may for example include a driving-only travel mode and a parking transfer mode, a travel route and a departure time. Since the simulation process is performed on the whole traffic system network, a globally optimal path plan can be obtained from the perspective of system optimization.
In step 204, the recommending module 107 adjusts the original planned path obtained from the simulating module 106 based on the corresponding weights of the individual personalization parameters of the user obtained from the scoring module 105 to recommend an optimal planned path to the user, wherein the optimal planned path comprises an optimal travel mode, an optimal travel route and an optimal departure time. In addition, when the navigation route for parking transfer is recommended to the user, the recommendation module 107 may indicate a station and a schedule to the user to more conveniently make the parking transfer for the user. Recommendation module 107 may also indicate to the user the expected travel time, the expected congested road segments, etc. for the optimally planned path.
In one scenario, after receiving a navigation request of a user (e.g., from home to a company at 7 am), the personalized navigation system may obtain simulation results for the entire transportation system network to get an original planned path, and adjust the original planned path based on personalized features of the user (e.g., the user has low dependency on driving travel but pays more attention to travel overhead and travel time to spend less and avoid late), to recommend a suitable travel pattern (e.g., parking transfer), a suitable travel route (e.g., parking transfer point less to charge for parking transfer public transportation), and a suitable departure time (e.g., peak off is recommended in consideration of morning and evening peak time periods).
What has been described above includes examples of aspects of the claimed subject matter. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the claimed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the claimed subject matter are possible. Accordingly, the disclosed subject matter is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims.
Claims (15)
1. A method of personalized navigation, the method comprising:
receiving a user request for path planning from a user, wherein the user request comprises a path starting point, a path ending point and a starting time;
in response to the user request, grading personalized parameters of the user to obtain corresponding weights of the parameters, wherein the personalized parameters comprise personal preference parameters and driving context parameters;
simulating the whole transportation system network to obtain an original planned path from the path starting point to the path terminal point, wherein the planned path comprises a travel mode, a travel route and a departure time; and
and adjusting the original planning path based on the obtained corresponding weight of each parameter so as to recommend an optimal planning path to the user.
2. The method of claim 1, wherein the personal preference parameters include one or more of vehicle dependency, cost efficiency, and time efficiency.
3. The method of claim 1, wherein the driving context parameters comprise one or more of road conditions, weather conditions, road network type, and traffic density.
4. The method of claim 1, wherein scoring the personalized parameters of the user to obtain respective weights for each parameter further comprises:
and scoring the personalized parameters of the user by counting and analyzing the historical travel information of the user to obtain corresponding weights.
5. The method of claim 1, wherein the travel mode comprises a driving only travel mode or a parking to public transportation P + R mode.
6. The method of claim 1, wherein said adjusting the original planned path based on the respective weights of the obtained parameters further comprises:
and adjusting the original planned path based on the parameters corresponding to higher weights, wherein the higher the weight is, the greater the influence of the parameters corresponding to the weights on the optimal planned path of the user is.
7. The method of claim 1, further comprising:
and indicating the predicted travel time and road condition information of the optimal planned path to the user.
8. The method of claim 1, further comprising:
when a route for parking transfer is recommended to the user, a station and a schedule near a parking transfer point are indicated to the user to more facilitate the user's parking transfer.
9. A personalized navigation system, the system comprising:
a travel service platform configured to receive a user request for path planning from a user, wherein the user request includes a path start point, a path end point, and a departure time;
a scoring module configured to score personalized parameters of the user to obtain respective weights for the parameters in response to the user request, wherein the personalized parameters include personal preference parameters and driving context parameters;
a simulation module configured to simulate an entire transportation system network to obtain an original planned path from the path start point to the path end point, wherein the planned path includes a travel mode, a travel route, and a departure time; and
a recommendation module configured to adjust the original planned path based on the respective weights of the obtained parameters to recommend an optimal planned path to the user via the travel service platform.
10. The system of claim 9, wherein the personal preference parameters include one or more of vehicle dependency, cost efficiency, and time efficiency.
11. The system of claim 9, wherein the driving context parameters include one or more of road conditions, weather conditions, road network type, and traffic density.
12. The system of claim 9, wherein scoring the personalized parameters of the user to obtain respective weights for each parameter further comprises:
and scoring the personalized parameters of the user by counting and analyzing the historical travel information of the user to obtain corresponding weights.
13. The system of claim 9, wherein the travel mode includes a driving only travel mode or a parking transfer public transportation P + R mode.
14. The system of claim 9, wherein said adjusting the original planned path based on the respective weights of the obtained parameters further comprises:
and adjusting the original planned path based on the parameters corresponding to higher weights, wherein the higher the weight is, the greater the influence of the parameters corresponding to the weights on the optimal planned path of the user is.
15. The system of claim 9, the recommendation module further configured to:
when a route for parking transfer is recommended to the user, a station and a schedule near a parking transfer point are indicated to the user to more facilitate the user's parking transfer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110335003.0A CN115127569A (en) | 2021-03-29 | 2021-03-29 | Personalized intermodal navigation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110335003.0A CN115127569A (en) | 2021-03-29 | 2021-03-29 | Personalized intermodal navigation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115127569A true CN115127569A (en) | 2022-09-30 |
Family
ID=83375528
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110335003.0A Pending CN115127569A (en) | 2021-03-29 | 2021-03-29 | Personalized intermodal navigation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115127569A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116839616A (en) * | 2023-07-04 | 2023-10-03 | 深圳源谷科技有限公司 | Intelligent management system and method applying Beidou positioning technology |
| CN117809474A (en) * | 2023-11-21 | 2024-04-02 | 苏州科技大学 | Method, system, equipment and medium for generating parking transfer route |
-
2021
- 2021-03-29 CN CN202110335003.0A patent/CN115127569A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116839616A (en) * | 2023-07-04 | 2023-10-03 | 深圳源谷科技有限公司 | Intelligent management system and method applying Beidou positioning technology |
| CN116839616B (en) * | 2023-07-04 | 2024-05-07 | 深圳源谷科技有限公司 | Intelligent management system and method applying Beidou positioning technology |
| CN117809474A (en) * | 2023-11-21 | 2024-04-02 | 苏州科技大学 | Method, system, equipment and medium for generating parking transfer route |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10895461B2 (en) | Multi-day, multi-person, and multi-modal trip planning system | |
| CN108981736B (en) | Electric vehicle charging path optimization method based on user travel rule | |
| RU2572279C1 (en) | System for planning trip multimodal route | |
| US10969234B2 (en) | Multimodal vehicle routing system and method with vehicle parking | |
| CN103575285B (en) | Route planning device | |
| US7679531B2 (en) | System and method of optimizing a fixed-route transit network | |
| Hamadneh et al. | Impacts of shared autonomous vehicles on the travelers’ mobility | |
| CN106875734B (en) | A kind of method and device of push parking route | |
| CN102884401A (en) | Method and guidance-unit for guiding battery-operated transportation means to reconditioning stations | |
| CN104520912A (en) | Plugging prediction device, plugging prediction system, plugging prediction method and program | |
| CN108072381A (en) | A kind of method and device of path planning | |
| CN112665600A (en) | Path planning method and device, computer readable storage medium and processor | |
| CN107063278A (en) | A kind of Vehicular navigation system, air navigation aid and its vehicle | |
| CN104024800A (en) | Device, method and program for calculating accessible range | |
| CN103177562A (en) | Method and device for obtaining information of traffic condition prediction | |
| CN115127569A (en) | Personalized intermodal navigation method and system | |
| CN112542043A (en) | Method and system for identifying bus net coverage blind area based on mobile phone signaling and big data analysis | |
| CN109242202B (en) | Taxi recommendation method and system based on inter-regional passenger flow | |
| CN115547052A (en) | Dynamic demand response electric bus dispatching method for improving self-adaptive large-field algorithm | |
| CN111582527A (en) | Travel time estimation method and device, electronic equipment and storage medium | |
| CN113408833A (en) | Public traffic key area identification method and device and electronic equipment | |
| CN114742627B (en) | Matching method and system for windward orders | |
| CN112945253A (en) | Travel route recommendation method, system and device | |
| CN115049272A (en) | Electric bus dispatching method for charging intermediate station based on battery exchange | |
| CN113902209A (en) | Travel route recommendation method, edge server, cloud server, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |