Intelligent management system for passenger transport route

Abstract

The invention discloses an intelligent management system for a passenger transport route, and relates to the intelligent management system for the passenger transport route, which comprises a vehicle monitoring module, a passenger information module, a road condition monitoring module, a supply and demand matching module, a road condition analysis module and a dynamic prediction module, the vehicle monitoring module collects vehicle position information, the number of remaining seats, energy consumption data and passenger transport types in real time, the passenger transport types comprise a carpooling mode and an exclusive mode, dynamic matching is carried out on passenger transport vehicle information and passenger information, road condition information, the taxi taking position busy degree and the vehicle position busy degree are associated, a vehicle allocation value V is calculated, and the passenger transport vehicle allocation value V is obtained. And if V is greater than 0.4 and less than 0.7, a driver of the vehicle is directly reminded to drive to an adjacent taxi-taking information busy area, if V is greater than 0.4 and less than 0.7, a customer is reminded to drive to an adjacent taxi-taking information normal and / or busy area, and if V is greater than 0.7, the vehicle is directly reminded to drive normally and receive an order, and the vehicle is dynamically deployed in time, so that the supply and demand matching efficiency and the dynamic scheduling capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of route planning management systems, and in particular to an intelligent passenger route management system. Background Art

[0002] With the rapid development of modern transportation technology and the diversification of passenger travel needs, intelligent passenger route management systems have emerged. The system integrates a variety of technical resources, such as Internet technology, big data and cloud computing, map positioning and navigation technology, etc., to provide passengers with a more personalized and convenient travel experience, while also providing passenger transport companies with more efficient and intelligent operation and management methods.

[0003] The existing passenger route planning and management system still has many shortcomings. First, the lack of real-time dynamic scheduling capabilities has become a key factor restricting transportation efficiency and service quality. When faced with sudden road conditions (such as traffic congestion, traffic accidents) and temporary changes in demand, the traditional system mainly relies on manual adjustments, with a slow response speed and unable to meet the rapidly changing market demand. This not only affects the travel experience of passengers, but also increases the operating costs of passenger transport companies.

[0004] Inefficient supply-demand matching and serious waste of resources are another major challenge facing the existing system. Since fixed routes cannot dynamically respond to real-time changes in passenger flow, the vehicle idle rate is high during non-peak hours, and there may be insufficient capacity during holidays or commuting peaks, which wastes precious transportation resources and reduces overall operational efficiency. Summary of the invention

[0005] In view of the above situation, the present invention dynamically matches passenger vehicle information and passenger information, and associates road condition information, taxi location busyness, and vehicle busyness to calculate the vehicle allocation value, and dynamically allocates vehicles in a timely manner, thereby improving the supply and demand matching efficiency and dynamic scheduling capabilities.

[0006] The technical solution is to include a vehicle monitoring module, a passenger information module, a road condition monitoring module, a supply and demand matching module, a road condition analysis module, a dynamic prediction module and a dispatching control module. The vehicle monitoring module collects vehicle location information, the number of remaining seats, energy consumption data, and passenger transport types in real time. The passenger transport types include carpooling mode and exclusive mode. The passenger information module collects passenger taxi information, including the number of passengers, travel route information and taxi type, the taxi type includes carpooling mode and exclusive mode, and the travel route information includes departure place, destination and travel time window; The road condition monitoring module collects traffic congestion conditions A, road construction information B, and weather information C; The supply and demand matching module locates the vehicle position signal and the passenger departure location information through the Beidou navigation system, and calculates the distance between the vehicle position and the passenger departure location through the Beidou navigation system; Then calculate the seat matching, taxi type matching, distance matching and energy consumption data matching, and finally calculate the supply and demand matching, supply and demand matching = (0.3 × seat matching + 0.3 × taxi type matching + 0.4 × distance matching) × energy consumption data matching; The traffic condition analysis module extracts the historical traffic congestion status of the passenger route according to the passenger's departure place and destination, and extracts the corresponding historical traffic congestion status HA of the passenger route according to the travel time window, extracts the road construction information B and weather information C of the corresponding road section, calculates the matching value W of each road section, and finally calculates the matching value ZW of the entire passenger travel route; The dynamic prediction module extracts the historical driving path information of the vehicle with the highest supply-demand matching degree, then screens out all overlapping routes of the historical driving path information and the recommended travel route, calculates the distance L of all overlapping routes, extracts the accident rate S of the vehicles on the overlapping routes, calculates the vehicle matching value N, N=L×(1-S), and finally calculates the comprehensive vehicle matching value M, M=supply-demand matching degree×0.4+N×0.3+ZW×0.3; The deployment control module receives the information sent by the dynamic prediction module, calculates the vehicle deployment value V based on the passenger's taxi location information and the vehicle location information, and dynamically deploys passenger vehicles in real time based on the vehicle deployment value V.

[0007] Furthermore, the dispatch control module divides the 3km area of ​​the taxi location into three levels: busy, normal, and idle according to the passenger taxi information, and the real-time location of the vehicle is also divided into three levels: busy, normal, and idle; Calculate the vehicle dispatch value V, V=M×0.4+Q×0.3+R×0.3. If the passenger taxi area is busy, Q is 1; if it is normal, Q is 0.6; if it is idle, Q is 0. If the vehicle location area is busy, R is 0; if it is normal, R is 0.6; if it is idle, R is 1. For vehicles with V less than 0.4, the driver is directly reminded to drive to the nearby area with busy taxi information. For vehicles with V greater than 0.4 and less than 0.7, the customer is reminded to drive to the nearby area with normal and / or busy taxi information. For vehicles with V greater than 0.7, the vehicle is directly reminded to drive normally to accept orders.

[0008] Furthermore, the supply-demand matching module calculates the seat matching degree, where the seat matching degree = the number of remaining seats in the vehicle / the number of passengers, wherein when the number of remaining seats in the vehicle is less than the number of passengers, the seat matching degree is 0; The taxi type matching degree = 1 or 0. If the taxi type is consistent with the passenger transport type provided by the vehicle, the taxi type matching degree is 1, otherwise, it is 0; Distance matching degree = 1 ÷ (the distance between the vehicle location and the passenger's departure location + 0.001), where 0.001 prevents the denominator from being 0; Energy consumption data matching degree = 1 or 0. When the distance traveled by the vehicle with more than 20% of the remaining power is greater than the distance of the passenger's travel route, it is 1, otherwise it is 0.

[0009] Furthermore, the traffic condition analysis module extracts the historical traffic congestion status of the passenger route according to the passenger's departure place and destination, and extracts the corresponding historical traffic congestion status HA of the passenger route according to the travel time window. The historical vehicle flow data of each road section is divided into three levels: congested, normal, and idle; At the same time, the road construction information B and weather information C of the corresponding road section are extracted. The road construction information is divided into three situations: interruption, restricted traffic, and normal. The weather information is divided into three levels: severe, slightly affected, and normal. Calculate the matching value W for each road section, W = (HA × 0.6 + C × 0.4) × B, where the historical vehicle flow data is congested, HA is 0, normal, HA is 0.6, idle, HA is 1, road construction information is interrupted, B is 0, restricted, 0.4, normal, B is 1, weather information is bad, C is 0.1, slightly affected, C is 0.4, normal, C is 1; Finally, the matching value ZW of the entire passenger travel route is calculated. ZW is the sum of the segment matching values ​​W of each passenger travel route. The passenger travel route with the highest matching value ZW is selected as the final recommended travel route.

[0010] Furthermore, the dispatching control module also reserves spare vehicles. When passengers make reservations for customized vehicles in advance, the supply and demand vehicles are directly matched in advance. After the supply and demand vehicles are determined, the vehicles will no longer be changed dynamically. When the supply and demand vehicles are temporarily unable to work, spare vehicles will be provided in time to serve the customers who made advance reservations.

[0011] Furthermore, the deployment control module divides the 3-kilometer area between the taxi location and the real-time location of the vehicle into three levels: busy, normal, and idle. If the number of taxi orders within the 3-kilometer area between the taxi location and the real-time location of the vehicle is greater than 1.2 times the number of orders received, and the vehicles are in short supply, it is judged as a busy level; if the number of taxi orders is less than 1.2 times the number of orders received and greater than 0.9 times the number of orders received, it is judged as a normal level; and if the number of taxi orders is less than 0.9 times the number of orders received, it is judged as an idle level.

[0012] Furthermore, the navigation system used in the management system is the Beidou navigation system, and the signal transmission between the vehicle monitoring module, passenger information module, road condition monitoring module, supply and demand matching module, road condition analysis module, dynamic prediction module and dispatching control module adopts the 5G signal transmission method.

[0013] Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art: 1. By dynamically matching passenger vehicle information and passenger information and associating road condition information, since multiple vehicles will match multiple passengers, in order to further accurately distinguish which vehicle is more suitable for passengers, route matching is introduced, and the supply and demand matching degree and vehicle matching value N are associated. In addition, the busyness of the taxi location and the busyness of the vehicle location are further introduced to more accurately calculate the vehicle allocation value V, thereby improving the supply and demand matching efficiency and dynamic scheduling capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flow chart of an intelligent passenger route management system. DETAILED DESCRIPTION

[0015] The above and other technical contents, features and effects of the present invention are described in detail below with reference to the attached Figure 1 The detailed description of the embodiments will clearly show that the structural contents mentioned in the following embodiments are all based on the drawings in the specification.

[0016] In order to solve the supply and demand matching efficiency and dynamic scheduling capabilities, it includes a vehicle monitoring module, a passenger information module, a road condition monitoring module, a supply and demand matching module, a road condition analysis module, a dynamic prediction module and a dispatch control module. The vehicle monitoring module collects vehicle location information, the number of remaining seats, energy consumption data, and passenger transport types in real time. The passenger transport types include carpooling mode and exclusive mode. The passenger information module collects passenger taxi information, including the number of passengers, travel route information and taxi type, the taxi type includes carpooling mode and exclusive mode, and the travel route information includes departure place, destination and travel time window; The road condition monitoring module collects traffic congestion conditions A, road construction information B, and weather information C; The supply and demand matching module locates the vehicle position signal and the passenger departure location information through the Beidou navigation system, and calculates the distance between the vehicle position and the passenger departure location through the Beidou navigation system; Then calculate the seat matching, taxi type matching, distance matching and energy consumption data matching, and finally calculate the supply and demand matching, supply and demand matching = (0.3 × seat matching + 0.3 × taxi type matching + 0.4 × distance matching) × energy consumption data matching; The traffic condition analysis module extracts the historical traffic congestion status of the passenger path according to the passenger's departure place and destination, and extracts the corresponding historical traffic congestion status HA of the passenger path according to the travel time window, and extracts the road construction information B and weather information C of the corresponding road section, and calculates the matching value W of each road section; Since multiple vehicles will match multiple passengers, in order to further accurately distinguish which vehicle is more suitable for passengers, route matching is introduced to associate the supply-demand matching degree with the vehicle matching value N. Under the same conditions, drivers who are more familiar with the route are given priority to match the corresponding route passengers. The dynamic prediction module extracts the historical driving path information of the vehicle with the highest supply-demand matching degree, then screens out all overlapping routes of the historical driving path information and the recommended travel routes, calculates the distance L of all overlapping routes, extracts the accident rate S of the vehicles on the overlapping routes, calculates the vehicle matching value N, N=L×(1-S), and calculates the vehicle comprehensive matching value M, M=supply-demand matching degree×0.4+N×0.3+ZW×0.3.

[0017] Furthermore, it also includes a dispatching control module, which divides the 3-kilometer taxi-hailing area into three levels: busy, normal, and idle according to the passenger taxi-hailing information, and the real-time location of the vehicle is also divided into three levels: busy, normal, and idle; Calculate the vehicle dispatch value V, V=M×0.4+Q×0.3+R×0.3. If the passenger taxi area is busy, Q is 1; if it is normal, Q is 0.6; if it is idle, Q is 0. If the vehicle location area is busy, R is 0; if it is normal, R is 0.6; if it is idle, R is 1. For vehicles with V less than 0.4, the driver is directly reminded to drive to the nearby area with busy taxi information. For vehicles with V greater than 0.4 and less than 0.7, the customer is reminded to drive to the nearby area with normal and / or busy taxi information. For vehicles with V greater than 0.7, the vehicle is directly reminded to drive normally to accept orders.

[0018] Furthermore, the supply-demand matching module calculates the seat matching degree, where the seat matching degree = the number of remaining seats in the vehicle / the number of passengers, wherein when the number of remaining seats in the vehicle is less than the number of passengers, the seat matching degree is 0; The taxi type matching degree = 1 or 0. If the taxi type is consistent with the passenger transport type provided by the vehicle, the taxi type matching degree is 1, otherwise, it is 0; Distance matching degree = 1 ÷ (the distance between the vehicle location and the passenger's departure location + 0.001), where 0.001 prevents the denominator from being 0; Energy consumption data matching degree = 1 or 0. When the distance traveled by the vehicle with more than 20% of the remaining power is greater than the distance of the passenger's travel route, it is 1, otherwise it is 0.

[0019] Furthermore, the traffic condition analysis module extracts the historical traffic congestion status of the passenger route according to the passenger's departure place and destination, and extracts the corresponding historical traffic congestion status HA of the passenger route according to the travel time window. The historical vehicle flow data of each road section is divided into three levels: congested, normal, and idle; At the same time, the road construction information B and weather information C of the corresponding road section are extracted. The road construction information is divided into three situations: interruption, restricted traffic, and normal. The weather information is divided into three levels: severe, slightly affected, and normal. Calculate the matching value W for each road section, W = (HA × 0.6 + C × 0.4) × B, where the historical vehicle flow data is congested, HA is 0, normal, HA is 0.6, idle, HA is 1, road construction information is interrupted, B is 0, restricted, 0.4, normal, B is 1, weather information is bad, C is 0.1, slightly affected, C is 0.4, normal, C is 1; Finally, the matching value ZW of the entire passenger travel route is calculated. ZW is the sum of the segment matching values ​​W of each passenger travel route. The passenger travel route with the highest matching value ZW is selected as the final recommended travel route.

[0020] Furthermore, the dispatching control module also reserves spare vehicles. When passengers make reservations for customized vehicles in advance, the supply and demand vehicles are directly matched in advance. After the supply and demand vehicles are determined, the vehicles will no longer be changed dynamically. When the supply and demand vehicles are temporarily unable to work, spare vehicles will be provided in time to serve the customers who made advance reservations.

[0021] The deployment control module divides the 3-kilometer area between the taxi location and the real-time location of the vehicle into three levels: busy, normal, and idle. If the number of taxi orders within the 3-kilometer area between the taxi location and the real-time location of the vehicle is greater than 1.2 times the number of orders, and the vehicles are in short supply, it is judged as a busy level; if the number of taxi orders is less than 1.2 times the number of orders and greater than 0.9 times the number of orders, it is judged as a normal level; if the number of taxi orders is less than 0.9 times the number of orders, it is judged as an idle level.

[0022] Furthermore, the navigation system used in the management system is the Beidou navigation system, and the signal transmission between the vehicle monitoring module, passenger information module, road condition monitoring module, supply and demand matching module, road condition analysis module, dynamic prediction module and dispatching control module adopts the 5G signal transmission method.

[0023] When the present invention is used specifically, on the basis of the prior art, the vehicle monitoring module collects vehicle location information, number of remaining seats, energy consumption data, and passenger transport type in real time, and the passenger transport type includes a carpooling mode and an exclusive mode; The passenger information module collects passenger taxi information, including the number of passengers, travel route information and taxi type, the taxi type includes carpooling mode and exclusive mode, and the travel route information includes departure place, destination and travel time window; The road condition monitoring module collects traffic congestion conditions A, road construction information B, and weather information C; The supply and demand matching module locates the vehicle position signal and the passenger departure location information through the Beidou navigation system, and calculates the distance between the vehicle position and the passenger departure location through the Beidou navigation system; Then calculate the seat matching, taxi type matching, distance matching and energy consumption data matching, and finally calculate the supply and demand matching, supply and demand matching = (0.3 × seat matching + 0.3 × taxi type matching + 0.4 × distance matching) × energy consumption data matching; The traffic condition analysis module extracts the historical traffic congestion status of the passenger path according to the passenger's departure place and destination, and extracts the corresponding historical traffic congestion status HA of the passenger path according to the travel time window, and extracts the road construction information B and weather information C of the corresponding road section, and calculates the matching value W of each road section; The dynamic prediction module extracts the historical driving path information of the vehicle with the highest supply-demand matching degree, then screens out all overlapping routes of the historical driving path information and the recommended travel routes, calculates the distance L of all overlapping routes, extracts the accident rate S of the vehicles on the overlapping routes, calculates the vehicle matching value N, N=L×(1-S), and calculates the vehicle comprehensive matching value M, M=supply-demand matching degree×0.4+N×0.3+ZW×0.3.

[0024] Furthermore, it also includes a dispatching control module, which divides the 3-kilometer taxi-hailing area into three levels: busy, normal, and idle according to the passenger taxi-hailing information, and the real-time location of the vehicle is also divided into three levels: busy, normal, and idle; Calculate the vehicle dispatch value V, V=M×0.4+Q×0.3+R×0.3. If the passenger taxi area is busy, Q is 1; if it is normal, Q is 0.6; if it is idle, Q is 0. If the vehicle location area is busy, R is 0; if it is normal, R is 0.6; if it is idle, R is 1. For vehicles with a V value less than 0.4, the driver is directly reminded to drive to the nearby area with busy taxi information. For vehicles with a V value greater than 0.4 and less than 0.7, the customer is reminded to drive to the nearby area with normal or busy taxi information. For vehicles with a V value greater than 0.7, the driver is directly reminded to drive normally to receive orders. The above methods are used to dynamically allocate vehicles in a timely manner, improving the efficiency of supply and demand matching and the dynamic scheduling capability.

[0025] The above is a further detailed description of the present invention in combination with a specific implementation method, and it cannot be determined that the specific implementation of the present invention is limited to this; for technical personnel in the technical field to which the present invention belongs and related technical fields, based on the technical solution of the present invention, the expansion and replacement of operating methods and data should all fall within the protection scope of the present invention.

Claims

1. An intelligent passenger route management system, characterized in that: It includes a vehicle monitoring module, a passenger information module, a road condition monitoring module, a supply and demand matching module, a road condition analysis module, a dynamic prediction module and a dispatch control module. The vehicle monitoring module collects vehicle location information, the number of remaining seats, energy consumption data, and passenger transport types in real time. The passenger transport types include carpooling mode and exclusive mode. The passenger information module collects passenger taxi information, including the number of passengers, travel route information and taxi type, the taxi type includes carpooling mode and exclusive mode, and the travel route information includes departure place, destination and travel time window; The road condition monitoring module collects traffic congestion conditions A, road construction information B, and weather information C; The supply-demand matching module calculates the seat matching degree, the taxi type matching degree, the distance matching degree and the energy consumption data matching degree, and then calculates the supply-demand matching degree, the supply-demand matching degree = (0.3×seat matching degree + 0.3×taxis type matching degree + 0.4×distance matching degree)×energy consumption data matching degree; The traffic condition analysis module extracts the historical traffic congestion condition HA of the corresponding passenger route according to the travel time window, extracts the road construction information B and weather information C of the corresponding road section, calculates the matching value W of each road section, and finally calculates the matching value ZW of the entire passenger travel route; The dynamic prediction module extracts the historical driving path information of the vehicle with the highest supply-demand matching degree, then screens out all overlapping routes of the historical driving path information and the recommended travel route, calculates the distance L of all overlapping routes, extracts the accident rate S of the vehicles on the overlapping routes, calculates the vehicle matching value N, N=L×(1-S), and finally calculates the comprehensive vehicle matching value M, M=supply-demand matching degree×0.4+N×0.3+ZW×0.3; The deployment control module receives the information sent by the dynamic prediction module, calculates the vehicle deployment value V based on the passenger's taxi location information and the vehicle location information, and dynamically deploys passenger vehicles in real time based on the vehicle deployment value V.

2. According to claim 1, an intelligent passenger route management system is characterized in that: The dispatch control module divides the 3km area of ​​the taxi location into three levels: busy, normal, and idle according to the passenger taxi information, and the real-time location of the vehicle is also divided into three levels: busy, normal, and idle; Calculate the vehicle dispatch value V, V=M×0.4+Q×0.3+R×0.

3. If the passenger taxi area is busy, Q is 1; if it is normal, Q is 0.6; if it is idle, Q is 0. If the vehicle location area is busy, R is 0; if it is normal, R is 0.6; if it is idle, R is 1. For vehicles with V less than 0.4, the driver is directly reminded to drive to the nearby area with busy taxi information. For vehicles with V greater than 0.4 and less than 0.7, the customer is reminded to drive to the nearby area with normal and / or busy taxi information. For vehicles with V greater than 0.7, the vehicle is directly reminded to drive normally to accept orders.

3. According to claim 1, the intelligent passenger route management system is characterized in that: The supply and demand matching module locates the vehicle position signal and the passenger departure location information through the Beidou navigation system, and calculates the distance between the vehicle position and the passenger departure location through the Beidou navigation system, and calculates the seat matching degree, where the seat matching degree = the number of remaining seats in the vehicle / the number of passengers, wherein when the number of remaining seats in the vehicle is less than the number of passengers, the seat matching degree is 0; The taxi type matching degree = 1 or 0. If the taxi type is consistent with the passenger transport type provided by the vehicle, the taxi type matching degree is 1, otherwise, it is 0; Distance matching degree = 1 ÷ (the distance between the vehicle location and the passenger's departure location + 0.001), where 0.001 prevents the denominator from being 0; Energy consumption data matching degree = 1 or 0. When the distance traveled by the vehicle with more than 20% of the remaining power is greater than the distance of the passenger's travel route, it is 1, otherwise it is 0.

4. According to claim 1, the intelligent passenger route management system is characterized in that: The traffic condition analysis module extracts the historical traffic congestion status of the passenger route according to the passenger's departure place and destination, and extracts the corresponding historical traffic congestion status HA of the passenger route according to the travel time window. The historical vehicle flow data of each road section is divided into three levels: congested, normal, and idle; At the same time, the road construction information B and weather information C of the corresponding road section are extracted. The road construction information is divided into three situations: interruption, restricted traffic, and normal. The weather information is divided into three levels: severe, slightly affected, and normal. Calculate the matching value W for each road section, W = (HA × 0.6 + C × 0.4) × B, where the historical vehicle flow data is congested, HA is 0, normal, HA is 0.6, idle, HA is 1, road construction information is interrupted, B is 0, restricted, 0.4, normal, B is 1, weather information is bad, C is 0.1, slightly affected, C is 0.4, normal, C is 1; Finally, the matching value ZW of the entire passenger travel route is calculated. ZW is the sum of the segment matching values ​​W of each passenger travel route. The passenger travel route with the highest matching value ZW is selected as the final recommended travel route.

5. According to claim 2, the intelligent passenger route management system is characterized in that: The dispatching control module also reserves spare vehicles. When passengers make reservations for customized vehicles in advance, the supply and demand vehicles are directly matched in advance. After the supply and demand vehicles are determined, the vehicles will no longer be changed dynamically. When the supply and demand vehicles are temporarily unable to work, spare vehicles will be provided in time to serve the customers who made the reservations in advance.

6. The intelligent passenger route management system according to claim 2, characterized in that: The deployment control module divides the 3-kilometer area between the taxi location and the real-time location of the vehicle into three levels: busy, normal, and idle. If the number of taxi orders within the 3-kilometer area between the taxi location and the real-time location of the vehicle is greater than 1.2 times the number of orders, and the vehicles are in short supply, it is judged as a busy level; if the number of taxi orders is less than 1.2 times the number of orders and greater than 0.9 times the number of orders, it is judged as a normal level; and if the number of taxi orders is less than 0.9 times the number of orders, it is judged as an idle level.

7. An intelligent passenger route management system according to any one of claims 1 to 6, characterized in that: The navigation system used in this management system is the Beidou navigation system. The signal transmission between the vehicle monitoring module, passenger information module, road condition monitoring module, supply and demand matching module, road condition analysis module, dynamic prediction module and dispatching control module adopts the 5G signal transmission method.

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