A new energy bus leasing method based on the Internet of Things

Abstract

The invention discloses a new energy bus leasing method based on the Internet of Things, and relates to the technical field of vehicle leasing. The invention predicts demand concentration, identifies high-demand groups in adjacent areas, and simultaneously merges and plans routes of multiple groups to generate shared routes, thereby reducing empty driving and low-load phenomena of vehicles. Meanwhile, after introducing an adaptability index and a site priority mechanism, the invention responds to demand changes and traffic conditions on the route in real time, automatically adjusts the driving route and the sequence of sites, and provides efficient and convenient dispatching services when demand is high. The invention dynamically adjusts the rental price based on demand density, route cost and dispatching pressure, raises the price during peak hours to balance supply and demand, and provides discounts during low-demand hours to attract users to travel during off-peak hours, thereby avoiding excessive concentration of resources and improving overall profitability. Through intelligent dispatching and real-time information push, the travel experience of users is improved, the anxiety about itinerary changes is eliminated, and the transparency of travel services is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle leasing, and in particular to a new energy bus leasing method based on the Internet of Things. Background Art

[0002] In the leasing model of new energy buses, group travel, corporate charters and tourist groups generally choose leasing services with drivers. This method can not only effectively ensure travel safety, but also reduce the burden on users; however, in high-demand scenarios such as holidays, rental demand is mostly concentrated in specific areas or routes, and the departure time and location of multiple groups are similar. The existing vehicle dispatching plans usually dispatch vehicles according to the fixed routes, stations and scheduled time periods of a single team, which is difficult to flexibly meet the concentrated demand in the region. Such a "single group single line" dispatching mode leads to a large number of empty and low load situations, which reduces the utilization efficiency of driver and vehicle resources, and makes it impossible for the platform to fully utilize the bus capacity.

[0003] To address this limitation, some bus rental platforms deploy more vehicles and drivers to popular areas or routes during periods of high demand, and provide customized charter services for specific activities. At the same time, some platforms adjust rental prices according to holidays or off-seasons, and guide demand to non-peak periods through peak price increases and off-peak discounts, thereby improving the flexibility and responsiveness of the rental system. However, these measures still cannot effectively reduce the problems of empty and low loads.

[0004] First, the dispatch of vehicles and drivers is still mainly manual, and it is difficult to achieve automated optimization based on real-time demand, resulting in lags in responding to temporary demand changes and sudden peaks. Secondly, the dispatch model of a single route and station is difficult to integrate the needs of groups with similar travel routes, and there are still problems of empty driving and waste of resources. In addition, simple peak-peak surcharges and off-peak discounts are difficult to achieve accurate pricing, and it is easy for all rental needs to not be met during high-demand periods, and it is difficult to effectively attract customers during the off-season. Therefore, a new energy bus rental method based on the Internet of Things is urgently needed to solve such problems. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a new energy bus leasing method based on the Internet of Things to solve the problem that the existing bus leasing model has deficiencies in scheduling efficiency and pricing flexibility, and is difficult to improve vehicle utilization and platform revenue.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] The present invention provides a new energy bus leasing method based on the Internet of Things, which comprises:

[0009] Step S1, intelligent vehicle scheduling and demand forecasting,

[0010] Based on the Internet of Things technology, we collect rental order information in real time, combine historical data with real-time traffic conditions, predict the demand concentration area, that is, the demand concentration and regional peak hours, match the itineraries of multiple groups in adjacent areas according to the predicted demand concentration area, and generate a merged route;

[0011] Step S2: Dynamically adjust the driving route and the order of stops.

[0012] Based on the merged route generated in step S1, the driving route and the stop order are adjusted according to the real-time traffic conditions and demand fluctuations;

[0013] If a surge in demand or traffic congestion is predicted for a certain route, the dispatch plan will be adjusted, and the changed station sequence and route information will be pushed to the user end in real time;

[0014] Step S3, intelligent pricing based on demand dynamics,

[0015] Automatically adjust the rental price according to the combined route and dynamic dispatch plan generated in step S1 and step S2;

[0016] Increase rents during peak demand areas or time periods, and offer discounts during low demand or off-season periods.

[0017] Furthermore, the rental order information includes the number of passengers, departure point, destination and travel time.

[0018] Furthermore, in step S1, the method of predicting the demand concentration area is:

[0019] Combining historical order data and real-time traffic conditions, define demand concentration:

[0020] ,

[0021] in, Indicates at time From area To area The demand concentration forecast results are: Indicates the region from which historical order data is calculated To area In time The average demand, Indicates from the area To area In time The number of real-time demand requests, Indicates from the area To area In time Real-time traffic flow, and Represents the weight coefficient, which adjusts the influence of historical data and real-time data respectively;

[0022] Use the moving average method to predict the peak demand in the future and smooth the demand concentration:

[0023] ,

[0024] in, Indicates time The demand concentration forecast results are: represents the number of time periods in the smoothing window, An index representing the time lag, the forecast results use the peak demand period in the marked area to identify potential high-demand areas.

[0025] Furthermore, in step S1, the method of generating multiple group itineraries matching and merging routes is as follows:

[0026] According to the prediction results , identify adjacent high-demand areas and calculate the demand similarity between different areas:

[0027] ,

[0028] in, Indicates area With area The similarity of demand, Respectively, the regions and Region The number of demand-related regions, Indicates from the area To area In the future time The predicted demand concentration, Indicates from the area To area In the future time The predicted demand concentration is and The product capture area and The two-way demand intensity between Is the index, indicating the area Departure to different destinations, It is a region The total number of associated regions, Is the index, indicating the area Departure to different destinations, It is a region The total number of associated regions,

[0029] ,

[0030] From the area The sum of squares of all the demand concentrations from the start, measuring the region The overall travel demand intensity.

[0031] Furthermore, in step S1, for adjacent regions that have been matched successfully, a merged route is generated according to the demand similarity. Assuming that and Region If the two departing groups have similar itineraries, calculate the optimal merge distance:

[0032] ,

[0033] in, Indicates from the area and Region The combined route path distance, Indicates area With area Direct driving distance, Represents the adjustment coefficient, balancing the impact of path length and demand concentration on line merging, path distance As a weight reference for merging lines, the lower the weight, the higher the priority for merging.

[0034] Furthermore, in step S2, the driving route and the stop order are adjusted according to the real-time traffic conditions and demand fluctuations as follows:

[0035] Define the adaptability index to comprehensively evaluate the current route in time Efficiency:

[0036] ,

[0037] in, Indicates at time Down from area To area The line adaptability index, Indicates at time area To area The real-time demand Indicates from the area To area The road condition index is the better the road condition, the larger the index is. Indicates the current slave region To area The actual driving time, Represents the time cost coefficient, which adjusts the impact of travel time on the adaptability index; high A high value indicates that the line has a high demand match at the current moment and is suitable for maintaining the existing route. A low value indicates insufficient demand or congestion problems, which require re-optimization.

[0038] Furthermore, in step S2, based on the adaptability index, a threshold is preset. ,when The value is lower than the set threshold , optimize the current stop sequence and define the priority of each stop based on demand and distance:

[0039] ,

[0040] in, Indicates a stop In time The priority of Indicates time Next station The demand, Indicates area To site Distance, priority Stations with higher traffic volume will be given priority, so that the route can better meet the real-time demand.

[0041] Furthermore, in step S2, the method of adjusting the driving route and the stop order also includes:

[0042] Define the adjusted line cost function to evaluate the optimization effect: ,in, Indicates at time The adjusted total line cost, Indicates the total number of sites, represents the travel cost coefficient per unit distance, Indicates area To site The distance represents the time cost coefficient, Indicates at time From area To site The lower the adjusted route cost is, the more efficient the optimized route sequence is. After the optimization plan is generated, the new route and stop sequence are synchronized to the user end in real time.

[0043] Furthermore, in step S3, the rental price is adjusted in the following manner:

[0044] Defined in time The dynamic rental price is , the price is based on the base price As a benchmark, adjustments are made based on three factors: demand intensity, line cost, and dispatching pressure:

[0045] ,in, Indicates at time The final rental price, Indicates the basic rental price, set as the standard rental fee on weekdays, represents the adjustment coefficient, which controls the impact of demand intensity, line cost and dispatch pressure on price. Indicates at time The intensity of travel demand, ,in, Indicates at time The number of rental orders, Indicates the demand benchmark value, which is the average demand of historical orders. Normalize current demand for line cost and demand intensity The higher it is, the greater the demand is during the current period, and the corresponding rental price will increase.

[0046] Furthermore, in step S3, the method of adjusting the rental price also includes:

[0047] Defining Line Costs for:

[0048] ,

[0049] in, Indicates the total number of stations on the current merged line. represents the travel cost coefficient per unit distance, Indicates the site The distance to the next station, represents the unit no-load cost coefficient, Indicates the site Dead time to the next station;

[0050] Defining Scheduling Pressure Reflects the scheduling tension of vehicles and drivers:

[0051] ,

[0052] in, Indicates at time Number of vehicles available, represents the total number of vehicles, the standardized dispatch pressure, represents the weight coefficient, balancing the impact of the number of available vehicles and the remaining dispatch capacity, Indicates at time The actual number of passengers carried, Indicates the maximum passenger capacity of the vehicle and the dispatching pressure The higher the value, the tighter the current vehicle and driver resources are, and the rental price needs to be adjusted accordingly; the rental price is adjusted automatically , achieving adaptive upward adjustment to peak demand, while providing discounts during low demand, maximizing resource utilization efficiency and benefits.

[0053] The beneficial effects of the present invention are:

[0054] The present invention predicts the concentration of demand, effectively identifies high-demand groups in adjacent areas, and simultaneously merges and plans routes of multiple groups to generate shared routes, thereby reducing empty driving and low-load phenomena of vehicles, and improving the actual load rate and travel efficiency of vehicles. At the same time, after introducing the adaptability index and site priority mechanism, it responds to changes in demand and traffic conditions on the route in real time, automatically adjusts the driving route and site sequence, and provides efficient and convenient dispatching services when demand is high.

[0055] The present invention dynamically adjusts the rental price based on demand density, line cost and dispatching pressure, raises the price during peak hours to balance supply and demand, and provides discounts during low-demand hours to attract users to travel during off-peak hours, thereby avoiding excessive concentration of resources and improving overall profitability.

[0056] The present invention, through intelligent scheduling and real-time information push, synchronizes information such as itinerary adjustment and site sequence to the user end in advance, ensures that the user understands the changes in each itinerary optimization, improves the user's travel experience, eliminates the anxiety about itinerary changes, and enhances the transparency of travel services. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0058] Figure 1 The figure is a flow chart of a new energy bus leasing method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] Example 1, reference Figure 1 This embodiment provides a new energy bus leasing method based on the Internet of Things, comprising the following steps:

[0063] Step S1, intelligent vehicle scheduling and demand forecasting,

[0064] Based on the Internet of Things technology, we collect rental order information in real time, combine historical data with real-time traffic conditions, predict the demand concentration area, that is, the demand concentration and regional peak hours, match the itineraries of multiple groups in adjacent areas according to the predicted demand concentration area, and generate a merged route;

[0065] Rental order information includes the number of passengers, departure point, destination and travel time;

[0066] In step S1, the method of predicting the demand concentration area is:

[0067] Combining historical order data and real-time traffic conditions, define demand concentration:

[0068] ,

[0069] in, Indicates at time From area To area The demand concentration forecast results are: Indicates the region from which historical order data is calculated To area In time The average demand, Indicates from the area To area In time The number of real-time demand requests, Indicates from the area To area In time Real-time traffic flow, and Represents the weight coefficient, which adjusts the influence of historical data and real-time data respectively;

[0070] Use the moving average method to predict the peak demand in the future and smooth the demand concentration:

[0071] ,

[0072] in, Indicates time The demand concentration forecast results are: represents the number of time periods in the smoothing window, An index representing the time lag, the forecast results use the peak demand period in the marked area to identify potential high-demand areas.

[0073] In step S1, the method of generating multiple group itineraries matching and merging routes is as follows:

[0074] According to the prediction results , identify adjacent high-demand areas and calculate the demand similarity between different areas:

[0075] ,

[0076] in, Indicates area With area The similarity of demand, Respectively, the regions and Region The number of demand-related regions, Indicates from the area To area In the future time The predicted demand concentration, Indicates from the area To area In the future time The predicted demand concentration is and The product capture area and The two-way demand intensity between Is the index, indicating the area Departure to different destinations, It is a region The total number of associated regions, Is the index, indicating the area Departure to different destinations, It is a region The total number of associated regions,

[0077] ,

[0078] From the area The sum of squares of all the demand concentrations from the start, measuring the region The overall travel demand intensity.

[0079] In step S1, for adjacent regions that have been successfully matched, a merged route is generated based on the similarity of the requirements. and Region If the two departing groups have similar itineraries, calculate the optimal merge distance:

[0080] ,

[0081] in, Indicates from the area and Region The combined route path distance, Indicates area With area Direct driving distance, Represents the adjustment coefficient, balancing the impact of path length and demand concentration on line merging, path distance As a weight reference for merging lines, the lower the weight, the higher the priority for merging;

[0082] Specifically, we conduct demand concentration forecasts and multi-group merged route designs to enable high-demand groups in adjacent areas to share routes, reduce empty and low-load phenomena, significantly increase the actual load rate of vehicles, capture and analyze real-time demand data, accurately identify demand peaks in the future, and reasonably plan routes in advance. This will enable accurate allocation of resources during high-demand periods, effectively improving the allocation efficiency of vehicle resources during peak periods.

[0083] Step S2: Dynamically adjust the driving route and the order of stops.

[0084] Based on the merged route generated in step S1, the driving route and the stop order are adjusted according to the real-time traffic conditions and demand fluctuations;

[0085] If a surge in demand or traffic congestion is predicted for a certain route, the dispatch plan will be adjusted, and the changed station sequence and route information will be pushed to the user end in real time;

[0086] In step S2, the driving route and stop order are adjusted according to the real-time traffic conditions and demand fluctuations as follows:

[0087] Define the adaptability index to comprehensively evaluate the current route in time Efficiency:

[0088] ,

[0089] in, Indicates at time Down from area To area The line adaptability index, Indicates at time area To area The real-time demand Indicates from the area To area The road condition index is the better the road condition, the larger the index is. Indicates the current slave region To area The actual driving time, Represents the time cost coefficient, which adjusts the impact of travel time on the adaptability index; high A high value indicates that the line has a high demand match at the current moment and is suitable for maintaining the existing route. A low value indicates insufficient demand or congestion problems, which require re-optimization.

[0090] In step S2, based on the adaptability index, a threshold is preset ,when The value is lower than the set threshold , optimize the current stop sequence and define the priority of each stop based on demand and distance:

[0091] ,

[0092] in, Indicates a stop In time The priority of Indicates time Next station The demand, Indicates area To site Distance, priority High-speed stations are given priority, making the routes more in line with real-time demand;

[0093] Specifically, the adaptability index can grasp the efficiency of the route in time, conduct a comprehensive assessment of demand intensity, traffic flow and travel time, quickly identify inefficient routes, and optimize the sequence of stops and driving routes in real time to reduce delays and improve the overall experience of travel users.

[0094] In step S2, the method of adjusting the driving route and the stop order also includes:

[0095] Define the adjusted line cost function to evaluate the optimization effect: ,in, Indicates at time The adjusted total line cost, Indicates the total number of sites, represents the travel cost coefficient per unit distance, Indicates area To site The distance represents the time cost coefficient, Indicates at time From area To site The lower the adjusted route cost is, the more efficient the optimized route sequence is. After the optimization plan is generated, the new route and stop sequence are synchronized to the user end in real time.

[0096] Step S3, intelligent pricing based on demand dynamics,

[0097] Automatically adjust the rental price according to the combined route and dynamic dispatch plan generated in step S1 and step S2;

[0098] Increase rents in peak demand areas or time periods, and offer discounts during low demand or off-season periods;

[0099] In step S3, the rental price is adjusted in the following manner:

[0100] Defined in time The dynamic rental price is , the price is based on the base price As a benchmark, adjustments are made based on three factors: demand intensity, line cost, and dispatching pressure:

[0101] ,in, Indicates at time The final rental price, Indicates the basic rental price, set as the standard rental fee on weekdays, represents the adjustment coefficient, which controls the impact of demand intensity, line cost and dispatch pressure on price. Indicates at time The intensity of travel demand, ,in, Indicates at time The number of rental orders, Indicates the demand benchmark value, which is the average demand of historical orders. Normalize current demand for line cost and demand intensity The higher it is, the stronger the demand is during the current period, and the corresponding rental price will be adjusted upward;

[0102] Specifically, a dynamic pricing model is used to comprehensively consider demand intensity, line costs, and dispatch pressure to adjust rental prices in real time, increase prices during peak periods to balance supply and demand, and provide discounts during low-demand periods to attract more small groups to travel, making resource returns more stable.

[0103] In step S3, the rental price adjustment method also includes:

[0104] Defining Line Costs for:

[0105] ,

[0106] in, Indicates the total number of stations on the current merged line. represents the travel cost coefficient per unit distance, Indicates the site The distance to the next station, represents the unit no-load cost coefficient, Indicates the site Dead time to the next station;

[0107] Defining Scheduling Pressure Reflects the scheduling tension of vehicles and drivers:

[0108] ,

[0109] in, Indicates at time Number of vehicles available, represents the total number of vehicles, the standardized dispatch pressure, represents the weight coefficient, balancing the impact of the number of available vehicles and the remaining dispatch capacity, Indicates at time The actual number of passengers carried, Indicates the maximum passenger capacity of the vehicle and the dispatching pressure The higher the value, the tighter the current vehicle and driver resources are, and the rental price needs to be adjusted accordingly; the rental price is adjusted automatically , achieving adaptive upward adjustment to peak demand, while providing discounts during low demand, maximizing resource utilization efficiency and benefits.

[0110] Specifically, real-time push of itinerary adjustment information and personalized price tips greatly enhances the user's experience perception. Real-time synchronization of new route information to the user side enables users to know the sequence of stations or itinerary adjustments in advance, thereby arranging travel reasonably and effectively eliminating users' concerns about itinerary changes.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new energy bus leasing method based on the Internet of Things, characterized by: include, Step S1, intelligent vehicle scheduling and demand forecasting, Based on the Internet of Things technology, we collect rental order information in real time, combine historical data with real-time traffic conditions, predict the demand concentration area, match multiple group trips in adjacent areas according to the predicted demand concentration area, and generate a merged route; Step S2: Dynamically adjust the driving route and the order of stops. Based on the merged route generated in step S1, the driving route and the stop order are adjusted according to the real-time traffic conditions and demand fluctuations; If a surge in demand or traffic congestion is predicted for a certain route, the dispatch plan will be adjusted, and the changed station sequence and route information will be pushed to the user end in real time; Step S3, intelligent pricing based on demand dynamics, Automatically adjust the rental price according to the combined route and dynamic dispatching scheme generated in step S1 and step S2; Increase rents in peak demand areas or time periods, and offer discounts during low demand or off-season periods; In step S2, the driving route and the stop order are adjusted according to the real-time traffic conditions and demand fluctuations as follows: Define the adaptability index to comprehensively evaluate the current route in time Efficiency: ， in, Indicates at time Down from area To area The line adaptability index, Indicates at time area To area The real-time demand Indicates from the area To area The road condition index is the better the road condition, the larger the index is. Indicates the current slave region To area The actual driving time, represents the time cost coefficient, which adjusts the effect of travel time on the adaptability index; In step S2, based on the adaptability index, a threshold is preset. ,when The value is lower than the set threshold , optimize the current stop sequence and define the priority of each stop based on demand and distance: ， in, Indicates a stop In time The priority of Indicates time Next station The demand, Indicates area To site distance.

2. A new energy bus leasing method based on the Internet of Things according to claim 1, characterized in that: The rental order information includes the number of passengers, departure place, destination and travel time.

3. A new energy bus leasing method based on the Internet of Things according to claim 2, characterized in that: In step S1, the method of predicting the demand concentration area is: Combining historical order data and real-time traffic conditions, define demand concentration: ， in, Indicates at time From area To area The demand concentration forecast results are: Indicates the region from which historical order data is calculated To area In time The average demand, Indicates from the area To area In time The number of real-time demand requests, Indicates from the area To area In time Real-time traffic flow, and Represents the weight coefficient, which adjusts the influence of historical data and real-time data respectively; Use the moving average method to predict the peak demand in the future and smooth the demand concentration: ， in, Indicates time The demand concentration forecast results are: represents the number of time periods in the smoothing window, An index representing a time lag.

4. A new energy bus leasing method based on the Internet of Things according to claim 3, characterized in that: In step S1, the method of generating multiple group itineraries matching and merging routes is as follows: According to the prediction results , identify adjacent high-demand areas, and calculate the demand similarity between different areas: ， in, Indicates area With area The similarity of demand, Respectively, the regions and Region The number of demand-related regions, Indicates from the area To area In the future time The predicted demand concentration, Indicates from the area To area In the future time The predicted demand concentration is and The product capture area and The two-way demand intensity between Is the index, indicating the area Departure to different destinations, It is a region The total number of associated regions, Is the index, indicating the region Departure to different destinations, It is a region The total number of associated regions, ， From the area The sum of squares of all the demand concentrations from the start, measuring the region The overall travel demand intensity.

5. A new energy bus leasing method based on the Internet of Things according to claim 4, characterized in that: In step S1, for adjacent regions that have been successfully matched, a merged route is generated according to the demand similarity. and Region If the two departing groups have similar itineraries, calculate the optimal merge distance: ， in, Indicates from the area and Region The combined route path distance, Indicates area With area Direct driving distance, Represents the adjustment coefficient, balancing the impact of path length and demand concentration on line merging, path distance As a weight reference for merging lines, the lower the weight, the higher the priority for merging.

6. A new energy bus leasing method based on the Internet of Things according to claim 5, characterized in that: In step S2, the method of adjusting the driving route and the stop order also includes: Define the adjusted line cost function to evaluate the optimization effect: ,in, Indicates at time The adjusted total line cost, Indicates the total number of sites, represents the travel cost coefficient per unit distance, Indicates area To site The distance represents the time cost coefficient, Indicates at time From area To site The driving time.

7. A new energy bus leasing method based on the Internet of Things according to claim 6, characterized in that: In step S3, the rental price is adjusted in the following manner: Defined in time The dynamic rental price is , the price is based on the base price As a benchmark, adjustments are made based on three factors: demand intensity, line cost, and dispatching pressure: ,in, Indicates at time The final rental price, Indicates the basic rental price, set as the standard rental fee on weekdays, represents the adjustment coefficient, which controls the impact of demand intensity, line cost and dispatch pressure on price. Indicates at time The intensity of travel demand, ,in, Indicates at time The number of rental orders, Indicates the demand benchmark value, which is the average demand of historical orders. is the line cost.

8. The method for renting a new energy bus based on the Internet of Things according to claim 7 is characterized in that: In step S3, the method of adjusting the rental price also includes: Defining Line Costs for: ， in, Indicates the total number of stations on the current merged line. represents the travel cost coefficient per unit distance, Indicates the site The distance to the next station, represents the unit no-load cost coefficient, Indicates the site Dead time to the next station; Defining Scheduling Pressure Reflects the scheduling tension of vehicles and drivers: ， in, Indicates at time Number of vehicles available, represents the total number of vehicles, the standardized dispatch pressure, represents the weight coefficient, balancing the impact of the number of available vehicles and the remaining dispatch capacity, Indicates at time The actual number of passengers carried, Indicates the maximum passenger capacity of the vehicle.

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