Intelligent bus travel control method based on demand dynamic response

Abstract

The invention discloses an intelligent bus travel control method based on demand dynamic response, which comprises the following steps of: 1, preparing data, collecting and arranging travel demand information of passengers, and acquiring real-time position information and operation states of all online vehicles; 2, an initial route is generated for any no-load online vehicle, a Dijkstra algorithm is used for calculating the route of each online vehicle from the current position to the starting point positions of orders of all passengers, the optimal route is selected as the initial route according to the total travel time and the total travel distance of the routes, and a subsequent station sequence of the corresponding online vehicle is generated; 3, judging whether the starting point position of a new order is on the driving route of the corresponding online vehicle carrying the passengers or not when any one online vehicle carrying the passengers receives the new order in the driving process, and generating a new driving route according to different conditions; 4, processing special conditions; and 5, outputting a result. According to the invention, intelligent public transport vehicle scheduling is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent public transportation travel control, and in particular to an intelligent public transportation travel control method based on dynamic demand response. Background Art

[0002] Demand-responsive smart bus is a technology that dynamically adjusts bus services based on passenger demand. Unlike the traditional bus model with fixed routes and schedules, the demand-responsive system uses real-time data and algorithm optimization to flexibly dispatch vehicles and routes to improve operational efficiency and passenger satisfaction.

[0003] Compared with the traditional bus model with fixed routes and timetables, demand-responsive smart buses have the following advantages: the routes and stops of the vehicles are dynamically generated according to the passengers' reservation needs, the pick-up and drop-off method adopts instant pick-up and drop-off, and the service time is longer, which can meet the flexibility of passengers' travel needs; at the same time, the vehicle only runs when there is a travel demand, which can effectively reduce the cost of bus operation, and is particularly suitable for urban suburbs, peripheral new areas, residential areas or commercial and office areas with significant tidal characteristics.

[0004] Therefore, how to generate a travel demand response plan that meets the constraints through an intelligent algorithm based on passenger travel demand and vehicle operating status, and generate a vehicle scheduling plan based on the current operating vehicle status has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of the above-mentioned defects of the prior art, the present invention provides an intelligent public transportation travel control method based on dynamic demand response, the purpose of which is to output a travel demand response plan based on travel demand information, vehicle location information, and vehicle operation information, after comprehensively considering travel demand responsiveness and response cost, to achieve the scheduling of intelligent public transportation vehicles.

[0006] To achieve the above object, the present invention discloses an intelligent public transportation travel control method based on dynamic demand response, comprising the following steps:

[0007] Step 1: Data preparation, specifically: collecting and organizing passengers' travel demand information, and obtaining the real-time location information and operation status of all online vehicles;

[0008] Step 2: Generate an initial route for any unloaded online vehicle, specifically: use Dijkstra algorithm to calculate the path of each online vehicle from the current position to the starting position of all the passengers' orders, and select the optimal path as the initial route according to the total travel time and total travel distance of the path, and generate a subsequent station sequence for the corresponding online vehicle;

[0009] Step 3: When a new order is received while any online vehicle that has carried passengers is on the way, determine whether the starting point of the new order is on the travel route of the corresponding online vehicle that has carried passengers;

[0010] If the starting point of the new order is on the driving route of the corresponding online vehicle that has carried passengers, a stop is added to the driving route of the corresponding online vehicle that has carried passengers, that is, the starting point of the new order is used as the new driving route;

[0011] If the starting point of the new order is not on the driving route of the corresponding online vehicle that has carried passengers, generate multiple modified route plans, and select the best modified route plan as the new driving route;

[0012] Step 4: Handling special situations;

[0013] Step 5: Output the results.

[0014] Preferably, the passenger's travel demand information includes a departure place, a destination and an expected departure time;

[0015] The operating status includes the current number of passengers, the number of remaining seats and the battery power.

[0016] Preferably, step 2 is as follows:

[0017] Step 2.1, when receiving a batch of the orders, for any unloaded online vehicle, the Dijkstra algorithm is used to calculate the path from the corresponding online vehicle to the starting position of each order according to the current location and the starting positions of all the orders;

[0018] Step 2.2: Calculate the total travel time and the total travel distance of each path, and select the path with the shortest total travel time and the shortest total travel distance as the initial route.

[0019] More preferably, the calculation formula of the total travel time is as follows:

[0020]

[0021] Among them, T total is the total travel time;

[0022] t(i,i+1) is the travel time from station i to station i+1;

[0023] The total travel distance is calculated as follows:

[0024]

[0025] Among them, D total is the total travel distance;

[0026] d(i,i+1) is the driving distance from station i to station i+1.

[0027] Preferably, the steps of generating multiple modified route plans and selecting the best modified route plan are as follows:

[0028] Step 3.1, generating a plurality of modified route plans, specifically: using the Dijkstra algorithm to calculate the shortest path from the corresponding online vehicle that has carried passengers to the starting position of the new order, and generating a plurality of the modified route plans;

[0029] Step 3.2, evaluating all the modified route plans, and calculating the total travel time, empty trip rate and passenger satisfaction of each of the modified route plans;

[0030] Step 3.3: The modified route plan with the lowest total travel time, the lowest empty driving rate and the highest passenger satisfaction is taken as the optimal modified route plan.

[0031] More preferably, the calculation formula of the empty driving rate is as follows:

[0032]

[0033] Among them, R empty is the empty driving rate;

[0034] d(i,i+1) is the driving distance from station i to station i+1;

[0035] load(i,i+1) is the passenger load ratio from station i to station i+1;

[0036] The calculation formula of the passenger satisfaction is as follows:

[0037]

[0038] Among them, S passenger for said passenger satisfaction;

[0039] w k is the weight of passenger k;

[0040] t arrivalk is the actual arrival time of the corresponding online vehicle;

[0041] t expected,k is the expected arrival time of passenger k;

[0042] m is the number of passengers;

[0043] The evaluation formula of the optimal modified line plan is as follows:

[0044] F=αT total +βR empty +γS passenger ;

[0045] Among them, α, β and γ are the importance weight values ​​of the total travel time, the empty driving rate and the passenger satisfaction, respectively.

[0046] Preferably, in step 4, if any of the online vehicles is fully loaded, has low battery or malfunctions, it is unable to serve the dynamically added new order and is scheduled to the remaining online vehicles, and another online vehicle that is closest and has available seats is given priority.

[0047] Preferably, a subsequent site sequence and travel path corresponding to the online vehicle are generated, and the estimated arrival time and waiting time of each passenger are calculated to generate an intelligent vehicle scheduling solution.

[0048] Beneficial effects of the present invention:

[0049] The present invention is based on travel demand information, vehicle location information, and vehicle operation information, and after comprehensively considering travel demand responsiveness and response cost, outputs a travel demand response plan to achieve the scheduling of intelligent public transportation vehicles.

[0050] The application of the present invention can solve the problem of mismatch and lag in response to travel demand in the traditional bus route fixed-route and fixed-schedule operation mode.

[0051] The present invention takes into account the operating costs of travel demand response, provides a comprehensive consideration method in terms of travel demand responsiveness and response cost, and provides technical support for smart public transportation operations to improve service quality and reduce costs and increase efficiency.

[0052] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The following is a flowchart showing an implementation of an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Example

[0055] like Figure 1 As shown, the intelligent public transportation travel control method based on dynamic demand response includes the following steps:

[0056] Step 1: Data preparation, specifically: collecting and organizing passengers' travel demand information, and obtaining the real-time location information and operation status of all online vehicles;

[0057] Step 2: Generate an initial route for any unloaded online vehicle, specifically: use Dijkstra algorithm to calculate the path of each online vehicle from the current position to the starting position of all passengers' orders, and select the optimal path as the initial route according to the total travel time and total travel distance of the path, and generate the subsequent station sequence of the corresponding online vehicle;

[0058] In actual applications, the current position of an empty online vehicle is (x1, y1), and the path of the starting position of the passenger's order is (x2, y2), (x3, y3)... (x n ,y n ), the subsequent station sequence of the corresponding online vehicle is [starting point, (x2, y2), (x3, y3), ..., (x n ,y n )].

[0059] Step 3: When a new order is received while any online vehicle carrying passengers is on the way, determine whether the starting point of the new order is on the driving route of the corresponding online vehicle carrying passengers;

[0060] In practical applications, if the starting point of a new order is (x4, y4), check whether (x4, y4) is in the subsequent station sequence of the corresponding online vehicle [starting point, (x2, y2), (x3, y3), ..., (x n ,y n )]superior.

[0061] If the starting point of the new order is on the driving route of the corresponding online vehicle that has already carried passengers, a stop is added to the driving route of the corresponding online vehicle that has already carried passengers, that is, the starting point of the new order is used as the new driving route;

[0062] In practical applications, in the subsequent station sequence of the original corresponding online vehicle [starting point, (x2, y2), (x3, y3), ..., (x n ,y n )], and add the starting point position (x4,y4) of the new order, becoming [starting point, (x2,y2), (x4,y4), (x3,y3), ..., (x n ,y n )].

[0063] If the starting point of the new order is not on the driving route of the corresponding online vehicle that has carried passengers, multiple modified route plans are generated, and the best modified route plan is selected as the new driving route;

[0064] Step 4: Handling special situations;

[0065] Step 5: Output the results.

[0066] In some embodiments, the passenger's travel demand information includes a departure point, a destination, and an expected departure time;

[0067] Operational status includes the current number of passengers, number of remaining seats and battery charge.

[0068] In some embodiments, step 2 is as follows:

[0069] Step 2.1, when receiving a batch of orders, for any unloaded online vehicle, the Dijkstra algorithm is used to calculate the path from the corresponding online vehicle to the starting point of each order based on the current location and the starting point of all orders;

[0070] Step 2.2: Calculate the total travel time and total travel distance of each path, and select the path with the shortest total travel time and the shortest total travel distance as the initial route.

[0071] In some embodiments, the total travel time is calculated as follows:

[0072]

[0073] Among them, T total is the total travel time;

[0074] t(i,i+1) is the travel time from station i to station i+1;

[0075] The total travel distance is calculated as follows:

[0076]

[0077] Among them, D total is the total travel distance;

[0078] d(i,i+1) is the driving distance from station i to station i+1.

[0079] In some embodiments, the steps of generating multiple modified route plans and selecting the best modified route plan are as follows:

[0080] Step 3.1, generating multiple modified route plans, specifically: using the Dijkstra algorithm to calculate the shortest path from the corresponding online vehicle that has carried passengers to the starting point of the new order, and generating multiple modified route plans;

[0081] In practical applications, if the subsequent station sequence of the original corresponding online vehicle is [starting point, (x2, y2), (x3, y3), ..., (x n ,y n )];

[0082] Then the modified route plan 1 is generated as: [starting point, (x2, y2), (x4, y4), (x3, y3), ..., (x n ,y n )];

[0083] The modified route plan 2 is generated as follows: [starting point, (x2, y2), (x3, y3), (x4, y4), ..., (x n ,y n )]:

[0084] ……;

[0085] Step 3.2, evaluate all modified route plans, and calculate the total travel time, empty trip rate and passenger satisfaction of each modified route plan;

[0086] Step 3.3: The modified route plan with the lowest total travel time, the lowest empty driving rate and the highest passenger satisfaction is taken as the optimal modified route plan.

[0087] In some embodiments, the calculation formula of the empty driving rate is as follows:

[0088]

[0089] Among them, R empty is the empty driving rate;

[0090] d(i,i+1) is the driving distance from station i to station i+1;

[0091] load(i,i+1) is the passenger load ratio from station i to station i+1;

[0092] The calculation formula for passenger satisfaction is as follows:

[0093]

[0094] Among them, S passenger For passenger satisfaction;

[0095] w k is the weight of passenger k;

[0096] t arrivalk is the actual arrival time of the corresponding online vehicle;

[0097] t expected,k is the expected arrival time of passenger k;

[0098] m is the number of passengers;

[0099] The evaluation formula for the optimal modified route plan is as follows:

[0100] F=αT total +βR empty +γS passenger ;

[0101] Among them, α, β and γ are the importance weights of total travel time, empty rate and passenger satisfaction respectively.

[0102] In some embodiments, in step 4, if any online vehicle is fully loaded, has low battery or fails, it is unable to serve the dynamically added new order and is scheduled to other online vehicles, and another online vehicle that is closest and has available seats is given priority.

[0103] In some embodiments, in step 5, a subsequent station sequence and travel path of the corresponding online vehicle are generated, and the estimated arrival time and waiting time of each passenger are calculated to generate an intelligent vehicle scheduling solution.

[0104] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. Intelligent public transportation travel control method based on dynamic demand response; characterized in that: The steps include: Step 1: Data preparation, specifically: collecting and organizing passengers' travel demand information, and obtaining the real-time location information and operation status of all online vehicles; Step 2: Generate an initial route for any unloaded online vehicle, specifically: use Dijkstra algorithm to calculate the path of each online vehicle from the current position to the starting position of all the passengers' orders, and select the optimal path as the initial route according to the total travel time and total travel distance of the path, and generate a subsequent station sequence for the corresponding online vehicle; Step 3: When a new order is received while any online vehicle that has carried passengers is on the way, determine whether the starting point of the new order is on the travel route of the corresponding online vehicle that has carried passengers; If the starting point of the new order is on the driving route of the corresponding online vehicle that has carried passengers, a stop is added to the driving route of the corresponding online vehicle that has carried passengers, that is, the starting point of the new order is used as the new driving route; If the starting point of the new order is not on the driving route of the corresponding online vehicle that has carried passengers, generate multiple modified route plans, and select the best modified route plan as the new driving route; Step 4: Handling special situations; Step 5: Output the results.

2. The intelligent public transportation travel control method based on dynamic demand response according to claim 1 is characterized in that: The passenger's travel demand information includes the departure place, destination and expected departure time; The operating status includes the current number of passengers, the number of remaining seats and the battery power.

3. The intelligent public transportation travel control method based on dynamic demand response according to claim 1 is characterized in that: Step 2 is as follows: Step 2.1, when receiving a batch of the orders, for any unloaded online vehicle, the Dijkstra algorithm is used to calculate the path from the corresponding online vehicle to the starting position of each order according to the current location and the starting positions of all the orders; Step 2.2: Calculate the total travel time and the total travel distance of each path, and select the path with the shortest total travel time and the shortest total travel distance as the initial route.

4. The intelligent public transportation travel control method based on dynamic demand response according to claim 3 is characterized in that: The total travel time is calculated as follows: Among them, T total is the total travel time; t(i,i+1) is the travel time from station i to station i+1; The total travel distance is calculated as follows: Among them, D total is the total travel distance; d(i,i+1) is the driving distance from station i to station i+1.

5. The intelligent public transportation travel control method based on dynamic demand response according to claim 1 is characterized in that: The steps of generating multiple modified route plans and selecting the best modified route plan are as follows: Step 3.1, generating a plurality of modified route plans, specifically: using the Dijkstra algorithm to calculate the shortest path from the corresponding online vehicle that has carried passengers to the starting position of the new order, and generating a plurality of the modified route plans; Step 3.2, evaluating all the modified route plans, and calculating the total travel time, empty trip rate and passenger satisfaction of each of the modified route plans; Step 3.3: The modified route plan with the lowest total travel time, the lowest empty driving rate and the highest passenger satisfaction is taken as the optimal modified route plan.

6. The intelligent public transportation travel control method based on dynamic demand response according to claim 5 is characterized in that: The calculation formula of the empty driving rate is as follows: Among them, R empty is the empty driving rate; d(i,i+1) is the driving distance from station i to station i+1; load(i,i+1) is the passenger load ratio from station i to station i+1; The calculation formula of the passenger satisfaction is as follows: Among them, S passenger for said passenger satisfaction; w k is the weight of passenger k; t arrivalk is the actual arrival time of the corresponding online vehicle; t expected,k is the expected arrival time of passenger k; m is the number of passengers; The evaluation formula of the optimal modified line plan is as follows: F=αT total +βR empty +γS passenger ; Among them, α, β and γ are the importance weight values ​​of the total travel time, the empty driving rate and the passenger satisfaction, respectively.

7. The intelligent public transportation travel control method based on dynamic demand response according to claim 1 is characterized in that: In step 4, if any of the online vehicles is fully loaded, has low battery or malfunctions, it is unable to serve the dynamically added new order and is scheduled to the remaining online vehicles, and another online vehicle that is closest and has available seats is given priority.

8. The intelligent public transportation travel control method based on dynamic demand response according to claim 1 is characterized in that: In step 5, a subsequent station sequence and a travel path corresponding to the online vehicle are generated, and the estimated arrival time and waiting time of each passenger are calculated to generate an intelligent vehicle scheduling solution.

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