Scheduling plan model and evaluation based on hybrid programming of automatic driving and conventional bus

Abstract

The invention relates to the technical field of public transport operation management systems, and discloses a scheduling plan model based on hybrid scheduling of automatic driving and one conventional bus, which comprises a scheduling constraint module, a scheduling planning module, a scheduling planning module, a scheduling planning module, a scheduling planning module, a scheduling planning module and a scheduling planning module, and is characterized in that the scheduling planning module is used for setting a model constraint condition of hybrid scheduling of automatic driving and one conventional bus line; the rule is a rule which must be followed in a mixed generation scheduling process and is used for avoiding generation of an obvious infeasible solution; the scheduling calculation module is used for calculating a mixed scheduling result; according to the method, a multi-source passenger flow data sensing result is utilized to accurately study and judge the line passenger flow level, and through deep fusion of automatic driving available passenger flow and conventional buses, scheduling planning effect synergy and cost gain under certain transport capacity configuration are realized; on the basis of the conventional bus passenger flow volume change, an optimal solution is obtained in each period by using an automatic driving vehicle driving scheduling plan, the hybrid bus scheduling plan is promoted to dynamically respond, and the integrating degree of the hybrid plan is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of public transportation operation management systems, and in particular to a scheduling planning model and evaluation based on a mixed formation of autonomous driving and a conventional bus. Background Art

[0002] With the rapid development of autonomous driving technology and the acceleration of urbanization, the public transportation system is facing unprecedented changes. Autonomous driving buses are gradually becoming an important part of future public transportation with their advantages of high efficiency, safety and flexibility. Based on the current automatic scheduling function platform, the system scheduling model generally automatically generates the optimal scheduling plan and preset scheduling plan for a route through route characteristics and attributes, industry standards and other inputs;

[0003] As autonomous buses are gradually penetrating and applied to mixed traffic, there is no complete mixed scheduling plan evaluation method that can be used to evaluate the scheduling evaluation index system before and after autonomous driving mixing under the integration of autonomous driving vehicle capacity resources. Bus operators rely solely on experience and manual generation of support shifts, only considering a one-way mixed evaluation method, and considering a single influencing factor. It cannot truly reflect the actual operation strategy of the company's buses and the two-way vehicle allocation to maximize the optimization space. Therefore, it is particularly urgent to study the mixed scheduling under the mixed formation mode of autonomous driving and a conventional bus based on active passenger flow perception, and realize the maximization of autonomous driving vehicle and conventional bus capacity resources, and scientifically, accurately and effectively deploy capacity. Summary of the invention

[0004] The purpose of the present invention is to provide a scheduling planning model and evaluation based on the mixed formation of autonomous driving and a conventional bus, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A scheduling model based on mixed autonomous driving and conventional bus, including:

[0007] Scheduling constraint module: The scheduling constraint module is used to set the model constraints of the mixed scheduling of autonomous driving and a regular bus line, that is, the rules that must be followed in the process of mixed scheduling to avoid the generation of obvious infeasible solutions;

[0008] Shift scheduling calculation module: The shift scheduling calculation module is used to calculate the results of mixed shift scheduling.

[0009] As a further solution of the present invention: the constraints include:

[0010] Support multiple departure points:

[0011] Each regular route supports multiple departure stations;

[0012] There are six types of buses: double shift, single shift, morning half shift, evening half shift, mid shift and double shift with a stop. Each shift can be full-distance, short-distance or other different types of shifts.

[0013] Autonomous driving shifts can support both full-distance and short-distance shifts;

[0014] Driver working hours:

[0015] The driver’s daily working hours cannot exceed the prescribed maximum operating hours;

[0016] The daily driving time cannot exceed the maximum driving time; the daily operating hours and driving hours of drivers are regulated, but the working hours of autonomous vehicle safety officers are not subject to such restrictions;

[0017] Schedule:

[0018] The start time of the current trip must be later than the end time of the previous trip;

[0019] Meal time:

[0020] Drivers must have rest periods between consecutive shifts;

[0021] Sufficient additional stop time should be allocated within the meal time, and drivers should be allowed to eat according to the set meal time;

[0022] Vehicles cannot stop for more than a certain time threshold;

[0023] The self-driving car will stop for rest according to the rated time. During the meal period, additional time will be allocated for the safety officer to eat.

[0024] Shift uniformity:

[0025] Regular vehicle dispatch should be neither too sparse nor too dense, and there should be no gaps;

[0026] The shifts should maintain continuity, that is, the difference between the start time of the current vehicle in the vehicle row and the start time of the previous shift in the vehicle row should be within a certain range;

[0027] Autonomous driving vehicles will be evenly interspersed throughout the regular bus journey, maintaining a balanced integration and reasonable arrangement of regular and autonomous driving services.

[0028] Conventional public transportation is the main support, with autonomous driving as a supplement:

[0029] In order to avoid uneven distribution of workload for conventional public transport, the number of trips allocated to each driver in a day should be uniform based on characteristic factors such as turnover;

[0030] Autonomous driving is based on available transport resources, allocating the number of trips on demand, and the number of mixed shifts is relatively even;

[0031] The departure times of the first and last vehicles are fixed:

[0032] The scheduled time for autonomous driving support is relatively fixed. The number of scheduled shifts is allocated on demand based on passenger flow, while the actual departure time varies with turnover.

[0033] The intervals between shifts are calculated evenly, and the autonomous driving shifts are interspersed reasonably:

[0034] The current vehicle dispatch interval needs to be reasonably calculated based on the vehicles in front and behind, and the dispatch should be as even as possible;

[0035] The number of automatic dispatching classes varies with the number of interleaving classes, ensuring that the classes are arranged as evenly as possible without interruption;

[0036] Realize mixed scheduling and collection of vehicles:

[0037] The vehicle should be collected at the station where it left as much as possible to prevent empty trips from being collected.

[0038] The self-driving vehicle will cut in and out at the default location to avoid reverse parking.

[0039] Under the premise of ensuring the minimum service standard, vehicles exceeding the transport capacity may be collected;

[0040] Multiple autonomous driving available support vehicles can be arranged to intersperse and collect vehicles in the forward direction;

[0041] Scheduling short-line shifts and support shift generation:

[0042] If there is discontinuity in passenger flow, consider sending short-distance flights;

[0043] Automatically generate available autonomous driving support shifts based on passenger flow as an effective supplement;

[0044] Minimum stop time:

[0045] The types of tasks generated by autonomous driving include short-line and interval tasks. When the starting station is the terminal, the minimum stop time must be met. When the starting station is the intermediate station, the turnaround time must be met.

[0046] Operational task configuration:

[0047] When the conventional line initiates an operation demand for the autonomous driving, under the condition of meeting the idle time, the terminal station of the autonomous driving non-operating sub-line is automatically matched with the starting station of the conventional line sub-line, and the even interlacing method of the autonomous driving support vehicle is used to automatically generate a mixed scheduling plan;

[0048] Departure station or terminal:

[0049] The autonomous driving vehicle allocation plan is divided into full-day planned shifts and time-period planned shifts;

[0050] When obtaining the all-day planned shift allocation, prioritize the average allocation of autonomous driving shifts based on the conventional line departure terminal and the number of vehicles;

[0051] When obtaining the scheduled flights during the available time period, the system will prioritize the last scheduled end station of the automated driving and the nearest exit station of the conventional line, and will prioritize the nearest exit station according to the passenger flow direction;

[0052] Automatic driving forward and reverse reception processing:

[0053] When the scheduled operation task ends and the station collects vehicles in reverse, the planned departure time and the operation U-turn position are calculated backwards, and the original forward collection algorithm is used to carry out the short-line forward collection process.

[0054] An evaluation of a scheduling plan based on a mix of autonomous driving and a regular bus includes the following steps:

[0055] S1. Collect data and analyze demand: Collect relevant operational data and analyze current transportation demand, including passenger flow characteristics during peak and off-peak hours, and passengers’ service expectations;

[0056] S2. Formulate mixed-class scheduling goals: determine the main goals of mixed-class scheduling and formulate evaluation indicators based on the goals;

[0057] S3. Establish an evaluation index system: Design and establish an evaluation index system, including indicators of social benefits and economic benefits;

[0058] S4. Data analysis and comparison: Use the collected data to quantitatively analyze the operational effects before and after mixed scheduling; compare the changes in key indicators before and after mixed scheduling;

[0059] S5. Evaluate scheduling feasibility: Consider the feasibility of mixed-shift scheduling in the actual scheduling process and analyze potential problems and challenges in the scheduling process;

[0060] S6. Formulate optimization suggestions: Based on the evaluation results, make optimization suggestions for the mixed-shift scheduling plan and propose improvement strategies to enhance the comprehensive benefits of the mixed-shift scheduling, while ensuring the maximization of passenger satisfaction and corporate economic benefits;

[0061] S7. Implementation and monitoring: Convert optimization suggestions into specific implementation plans and conduct implementation monitoring. Establish a monitoring mechanism, regularly evaluate the implementation effect of the mixed-shift scheduling plan, and adjust the optimization strategy in a timely manner to adapt to changes in the transportation environment.

[0062] As a further solution of the present invention: the operating data in S1 includes passenger flow, line capacity, vehicle utilization, and frequency of service; the service expectations of passengers in S1 include waiting time and riding comfort.

[0063] As a further solution of the present invention: the main objectives in S2 include improving passenger satisfaction, optimizing transportation costs and improving operational efficiency, and the evaluation indicators in S2 include passenger service quality, enterprise economic benefits and scheduling feasibility.

[0064] As a further solution of the present invention: the social benefit indicators in S3 include passenger service quality related indicators, including average waiting time, carriage fullness rate and stop time, and the economic benefit indicators in S3 include operating costs, revenue, vehicle utilization rate and driver costs.

[0065] As a further solution of the present invention: the changes in the key indicators in S4 include changes in vehicle load factor, adjustment effects of departure frequency and changes in income.

[0066] As a further solution of the present invention: the feasibility of the actual scheduling process in S5 includes the rationality of the departure interval, the distribution balance of the shifts and the workload of the drivers. The potential problems and challenges in S5 include the processing capacity during peak hours and the control of the stop time.

[0067] As a further solution of the present invention: the optimization suggestions in S6 include adjusting the shift schedule, optimizing the vehicle configuration and improving the service standard.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. The present invention utilizes the results of multi-source passenger flow data perception to accurately judge the passenger flow level of the route, which helps the mixed scheduling model to plan routes in advance with reference to passenger flow demand. Through autonomous driving, the passenger flow can be deeply integrated with conventional public transportation, and the scheduling planning effect and cost gain under a certain capacity configuration can be improved.

[0070] 2. Based on the changes in passenger flow of conventional buses, the present invention uses the driving scheduling plan of autonomous driving vehicles to obtain the optimal solution in each cycle, achieve the optimization of the two-way overall scheduling goals of the mixed plan, and realize autonomous driving and a conventional bus under the traction of the mixed formation scheduling plan, so as to promote the dynamic response of the mixed bus scheduling plan, further improve the fit of the mixed plan, and make resource utilization more efficient and more in line with production.

[0071] 2. The present invention can effectively change the passive situation of lack of two-way passenger flow prediction, two-way dynamic response, and two-way efficient scheduling in the scheduling management of traditional bus line groups, and improve the bus collaborative scheduling capability and intelligent scheduling management level in the mixed formation mode of automatic driving and a conventional bus. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a structural block diagram of a scheduling model based on the mixed formation of autonomous driving and a conventional bus;

[0073] Figure 2 A flowchart for evaluating a scheduling plan based on a mixed system of autonomous driving and a conventional bus;

[0074] Figure 3 It is a calculation flow chart of a scheduling calculation module based on the mixed formation of autonomous driving and a conventional bus;

[0075] Figure 4 The figure is a logic diagram of the mixed scheduling generation process based on the mixed formation of autonomous driving and a regular bus. DETAILED DESCRIPTION

[0076] See also Figure 1-4 In an embodiment of the present invention, a scheduling model based on mixed autonomous driving and a conventional bus includes:

[0077] Scheduling constraint module: The scheduling constraint module is used to set the model constraints for the mixed scheduling of autonomous driving and a regular bus line, that is, the rules that must be followed in the process of generating mixed scheduling to avoid the generation of obviously infeasible solutions;

[0078] Shift calculation module: The shift calculation module is used to calculate the results of mixed shift scheduling;

[0079] See also Figure 3 and Figure 4 ,The calculation steps of the scheduling calculation module include:

[0080] 1. Basic data construction:

[0081] Initialization of route attributes and service parameters: Initialize basic parameter configuration, including route start and end time, route station information, route station mileage, vehicle capacity, time period full load rate, time period maximum departure interval, and time period minimum / maximum stop time;

[0082] Passenger flow segmentation at line stations during time periods: Obtain the passenger flow, turnover time, and high-section passenger flow at station stations during time periods, and calculate the high-section passenger flow, the frequency and interval of departure during time periods, and the number of vehicles to be allocated. Therefore, the optimal scheduled passenger flow can be segmented into the following two parts, as follows: ① Obtain the passenger flow during the regular line period, make full use of the available vehicle allocation plan for autonomous driving to generate a schedule, and eliminate part of the passenger flow taken away by the autonomous driving schedule; ② Remove the passenger flow during the autonomous driving schedule, fill the remaining passenger flow in each period back to the regular line, and generate a schedule according to the regular line (Note: The calculation of the number of vehicles during the period is based on the constraints of line operation and service standards, and is combined with line big data to deduce the passenger flow during the period, and the number of vehicles during the period is generated through the scheduling model, which is not independently evaluated in this article);

[0083] Support shifts interspersed with technical routes:

[0084] Optimal shift scheduling: Maintain the intervals between regular line shifts, evenly arrange the autonomous driving shifts according to time periods, and integrate the optimal mixed scheduling of autonomous driving and regular lines;

[0085] Preset shift interspersed: Generate a preset shift plan for regular lines based on passenger flow and turnover time during regular periods, evenly intersperse the number of autonomous driving vehicles, and complete preset mixed shifts for autonomous driving and regular lines;

[0086] Generate a mixed shift schedule:

[0087] Optimal mixed-train plan: The number of available autonomous driving vehicles is used to generate an autonomous driving schedule, and the passengers that have been taken away are removed and filled back to the regular line. The regular line schedule is generated based on the number of trips in half an hour, and the autonomous driving schedule is evenly interspersed.

[0088] Preset mixed-shift plan: Generate a preset schedule for the regular line first, and then evenly intersperse the regular line according to the number of available autonomous driving vehicles, that is, the autonomous driving support shift is arranged between the two long lines;

[0089] 2. Optimal mixed shift generation logic:

[0090] Obtain passenger flow and autonomous driving schedules during regular line periods. For example, obtain the schedule of Line B through the interface. Without affecting the operation of Line B, make full use of autonomous driving to generate an autonomous driving schedule, averaging the departure interval according to the turnaround time and the number of assigned vehicles.

[0091] Eliminate the autonomous driving passenger volume and generate a redundant passenger flow regular plan. For example, eliminate the autonomous driving passenger volume for the whole day and use the Guangzhou scheduling algorithm to generate the optimal schedule for Line A;

[0092] Autonomous driving is embedded in the conventional plan and mixed and integrated to optimize the autonomous driving departure time. If the vehicle is departing from the terminal, it will avoid the departure time of the conventional vehicle by two minutes. If the original departure interval is less than four minutes, it will be inserted in the middle. If it is not the terminal, it will not be processed.

[0093] 3. Preset mixed shift generation logic:

[0094] Eliminate the autonomous driving plan to generate the regular original schedule;

[0095] Obtain the automatic driving plan and evenly intersperse the regular plan. For example, the interface obtains the B line schedule. Without affecting the B line operation, the automatic driving is interspersed from the operation time of the automatic driving. If the bus is departing from the terminal, avoid the regular bus departure time by two minutes. If the original departure interval is less than four minutes, it is inserted in the middle. Non-terminal stations are not processed.

[0096] 4. Mixed shift generation process:

[0097] 1. Generation steps:

[0098] Scheduling is based on the departure time, and the vehicle allocation must comply with the constraints of the scheduling model. Due to the limited passenger capacity of unmanned vehicles, they cannot be used as the standard for regular vehicle operation service shifts, and cannot respond to the minimum number of departures per hour set by the industry service. They must be operated in the middle of the regular vehicle full-time shifts;

[0099] The mixed operation scheduling process of conventional vehicles is as follows:

[0100] Step 1: Obtain the scheduling plan of the autonomous driving vehicle on the autonomous driving route and generate the start and end times of the route;

[0101] Step 2: Determine whether to preset the shift schedule. If you select Yes, the conventional line preset shift schedule will be generated first, and then the autonomous driving vehicle shift schedule will be generated; if you select No, the autonomous driving vehicle shift schedule will be generated directly;

[0102] Step 3: Select the optimal U-turn point according to the passenger flow, and determine whether there are multiple U-turn points. If not, directly generate a scheduling plan based on the U-turn point; if so, determine whether there are five consecutive stations with a full load rate below 30% after the high section. If so, determine whether there is a U-turn point after the first full load rate is below 30%. If there is a U-turn point, generate a scheduling plan until the nearest U-turn point is found, otherwise generate a scheduling plan by finding the farthest U-turn point;

[0103] Step 4: Retrieve the driving plan of the automated driving vehicle supporting the convenience line and check whether it conflicts with the driving plan of the automated driving vehicle system. If so, find the turning point with the shortest stop time based on the departure time of the automated driving vehicle supporting the convenience line driving plan and generate the full-day automated driving vehicle supporting the convenience line driving plan; otherwise, continue to check whether the subsequent plans cause conflicts;

[0104] Step 5: When there is no conflict in the detection plan, the driving plan for the autonomous driving vehicle to support the convenience line will be directly generated. Otherwise, the shortest U-turn point will be found as described above to generate the driving plan for the autonomous driving vehicle to support the convenience line;

[0105] Step 6: Determine whether the optimal mixed scheduling is achieved. If yes, optimize the optimal scheduling plan for the conventional line based on the conventional passenger flow, regenerate the optimal scheduling plan for the conventional line, integrate the autonomous driving vehicle support plan and the conventional line scheduling plan to generate the optimal mixed scheduling plan. Otherwise, directly integrate the autonomous driving vehicle and conventional vehicle scheduling plans, and evenly intersperse them to generate a preset mixed scheduling plan.

[0106] 2. Support shift arrangements:

[0107] Generate a schedule based on the operating hours, initialize the departure schedule based on the turnaround time of different directions of the line, and schedule the up and down lines by time period, such as: the line starts operating from 6:00-22:00, and the full-time operation turnaround time of the line is obtained at intervals of 15 minutes. Generate a departure schedule based on the number of departures in half an hour, first create a schedule for the long line, and then arrange the support shifts before the long line, such as 3 shifts and 1 support shift from 07:00 to 07:30, and 2 shifts from 07:30 to 08:00, then the departure schedule is 07:00, 07:06 (support), 07:12, 07:24, 07:36, 07:48, and so on, until all schedule positions are arranged;

[0108] 3. Mixed vehicle arrangement:

[0109] According to the timetable, the opposite vehicles are preferred; if the opposite vehicles cannot depart on time, a new vehicle will be arranged to perform the shift; for example, if the A vehicle departs at 06:00 for the upward trip, the whole journey takes 50 minutes, and the minimum stop time is 8 minutes, then the earliest departure time of the A vehicle for the downward trip is 06:58, and the B vehicle departs at 06:15 for the upward trip, the whole journey takes 55 minutes, and the minimum stop time is 5 minutes, then the earliest departure time of the B vehicle for the downward trip is 07:15. If the downward departure schedule is 07:00, 07:10, and 07:20, then the 07:00 shift for the downward trip will be performed by the A vehicle, the 07:20 shift will be performed by the B vehicle, and the 07:10 shift will require the addition of a new vehicle C to perform;

[0110] 4. Short-term turning point arrangement:

[0111] When multiple U-turn points are selected, that is, there are multiple U-turn points, the five consecutive stations with full load rates below 30% after the high-section station can be searched, and the nearest U-turn point can be searched from front to back after the first station with full load rates below 30%; if no U-turn point can be found, the nearest U-turn point can be searched from back to front after the first station with full load rates below 30%; if no five consecutive stations with full load rates below 30% can be found after the high section, the farthest U-turn point can be selected;

[0112] 5. Mixed class selection:

[0113] It can be seen from the above mixed scheduling model that some parameters are dynamic parameters. Different parameters and the available autonomous driving vehicle system driving plan will have different effects on the mixed scheduling results. By adjusting the parameter combination and the autonomous driving vehicle system driving plan scheduling table, and performing multiple scheduling, scheduling screening, shift interspersion, optimization integration, etc., a relatively optimal scheduling model and application that conforms to the evaluation model can be generated.

[0114] Preferably, the constraints include:

[0115] Support multiple departure points:

[0116] Each regular route supports multiple departure stations;

[0117] There are six types of buses: double shift, single shift, morning half shift, evening half shift, mid shift and double shift with a stop. Each shift can be full-distance, short-distance or other different types of shifts.

[0118] Autonomous driving shifts can support both full-distance and short-distance shifts;

[0119] Driver working hours:

[0120] The driver’s daily working hours cannot exceed the prescribed maximum operating hours;

[0121] The daily driving time cannot exceed the maximum driving time; the daily operating hours and driving hours of drivers are regulated, but the working hours of autonomous vehicle safety officers are not subject to such restrictions;

[0122] Schedule:

[0123] The start time of the current trip must be later than the end time of the previous trip;

[0124] Meal time:

[0125] Drivers must have rest periods between consecutive shifts;

[0126] Sufficient additional stop time should be allocated within the meal time, and drivers should be allowed to eat according to the set meal time;

[0127] Vehicles cannot stop for more than a certain time threshold;

[0128] The self-driving car will stop for rest according to the rated time. During the meal period, additional time will be allocated for the safety officer to eat.

[0129] Shift uniformity:

[0130] Regular vehicle dispatch should be neither too sparse nor too dense, and there should be no gaps;

[0131] The shifts should maintain continuity, that is, the difference between the start time of the current vehicle in the vehicle row and the start time of the previous shift in the vehicle row should be within a certain range;

[0132] Autonomous driving vehicles will be evenly interspersed throughout the regular bus journey, maintaining a balanced integration and reasonable arrangement of regular and autonomous driving services.

[0133] Conventional public transportation is the main support, with autonomous driving as a supplement:

[0134] In order to avoid uneven distribution of workload for conventional public transport, the number of trips allocated to each driver in a day should be uniform based on characteristic factors such as turnover;

[0135] Autonomous driving is based on available transport resources, allocating the number of trips on demand, and the number of mixed shifts is relatively even;

[0136] The departure times of the first and last vehicles are fixed:

[0137] The scheduled time for autonomous driving support is relatively fixed. The number of scheduled shifts is allocated on demand based on passenger flow, while the actual departure time varies with turnover.

[0138] The intervals between shifts are calculated evenly, and the autonomous driving shifts are interspersed reasonably:

[0139] The current vehicle dispatch interval needs to be reasonably calculated based on the vehicles in front and behind, and the dispatch should be as even as possible;

[0140] The number of automatic dispatching classes varies with the number of interleaving classes, ensuring that the classes are arranged as evenly as possible without interruption;

[0141] Realize mixed scheduling and collection of vehicles:

[0142] The vehicle should be collected at the station where it left as much as possible to prevent empty trips from being collected.

[0143] The self-driving vehicle will cut in and out at the default location to avoid reverse parking.

[0144] Under the premise of ensuring the minimum service standard, vehicles exceeding the transport capacity may be collected;

[0145] Multiple autonomous driving available support vehicles can be arranged to intersperse and collect vehicles in the forward direction;

[0146] Scheduling short-line shifts and support shift generation:

[0147] If there is discontinuity in passenger flow, consider sending short-distance flights;

[0148] Automatically generate available autonomous driving support shifts based on passenger flow as an effective supplement;

[0149] Minimum stop time:

[0150] The types of tasks generated by autonomous driving include short-line and interval tasks. When the starting station is the terminal, the minimum stop time must be met. When the starting station is the intermediate station, the turnaround time must be met.

[0151] Operational task configuration:

[0152] When the conventional line initiates an operation demand for the autonomous driving, under the condition of meeting the idle time, the terminal station of the autonomous driving non-operating sub-line is automatically matched with the starting station of the conventional line sub-line, and the even interlacing method of the autonomous driving support vehicle is used to automatically generate a mixed scheduling plan;

[0153] Departure station or terminal:

[0154] The autonomous driving vehicle allocation plan is divided into full-day planned shifts and time-period planned shifts;

[0155] When obtaining the all-day planned shift allocation, prioritize the average allocation of autonomous driving shifts based on the conventional line departure terminal and the number of vehicles;

[0156] When obtaining the scheduled flights during the available time period, the system will prioritize the last scheduled end station of the automated driving and the nearest exit station of the conventional line, and will prioritize the nearest exit station according to the passenger flow direction;

[0157] Automatic driving forward and reverse processing:

[0158] When the scheduled operation task is completed and the station is collecting vehicles in reverse, the planned departure time and the operation U-turn position are calculated backwards, and the original forward collection algorithm is used to carry out the forward collection in a short-line manner.

[0159] An evaluation of a scheduling plan based on a mix of autonomous driving and a regular bus includes the following steps:

[0160] S1. Data collection and analysis needs: Collect relevant operational data, including passenger flow, line capacity, vehicle utilization, and frequency; analyze current transportation needs, including passenger flow characteristics during peak and off-peak hours, and passengers' service expectations, including waiting time and ride comfort;

[0161] S2. Formulate mixed-shift scheduling objectives: Determine the main objectives of mixed-shift scheduling, including improving passenger satisfaction, optimizing transportation costs and improving operational efficiency, and formulate evaluation indicators based on the objectives, including passenger service quality, enterprise economic benefits and scheduling feasibility;

[0162] S3. Establish an evaluation index system: Design and establish an evaluation index system, including indicators of both social benefits and economic benefits. Social benefit indicators include indicators related to passenger service quality, including average waiting time, carriage fullness rate and stop time. Economic benefit indicators include operating costs, revenue, vehicle utilization rate and driver costs.

[0163] S4. Data analysis and comparison: Use the collected data to quantitatively analyze the operational effects before and after mixed scheduling; compare the changes in key indicators before and after mixed scheduling, including changes in vehicle load factor, adjustment effects of shift frequency, and changes in revenue;

[0164] S5. Evaluate scheduling feasibility: Consider the feasibility of the mixed-shift scheduling plan in the actual scheduling process, including the rationality of the departure interval, the distribution balance of the shifts and the workload of the drivers, and analyze the potential problems and challenges in the scheduling process, including the handling capacity during peak hours and the control of stop time;

[0165] S6. Formulate optimization suggestions: Based on the evaluation results, make optimization suggestions for the mixed-shift scheduling plan, including adjusting the shift schedule, optimizing the vehicle configuration and improving the service standards, and propose improvement strategies to enhance the comprehensive benefits of the mixed-shift scheduling, while ensuring the maximization of passenger satisfaction and corporate economic benefits;

[0166] S7. Implementation and monitoring: Convert optimization suggestions into specific implementation plans, monitor their implementation, establish monitoring mechanisms, regularly evaluate the implementation effects of mixed-shift scheduling plans, and timely adjust optimization strategies to adapt to changes in the transportation environment;

[0167] To evaluate the rationality of mixed scheduling, we must first consider the social benefits and corporate economic benefits. Social benefits mainly refer to the quality of passenger service, such as the waiting time for passengers, the passenger load rate of the carriage, that is, the comfort of riding, the bus turnover efficiency, the frequency of departure, the line capacity, etc. In addition, this evaluation is mainly reflected in the benefit income, and the company's income includes card swiping income and financial subsidy income. In addition to social and economic benefits, it is also necessary to consider the line service standards to further evaluate the rationality and feasibility of its scheduling, such as vehicles cannot be disconnected, departures should be as even as possible, and the stop rate cannot be too high.

[0168] In summary, the rationality of mixed-shift scheduling can be evaluated from three aspects: (1) passenger service quality; (2) enterprise economic benefits; and (3) scheduling feasibility.

[0169] Details are as follows:

[0170] (1) Passenger service quality The evaluation of passenger service indicators before and after mixed support includes: the number of supporting flights, the number of supported flights, the number of passengers on supporting flights, the number of passengers on non-supporting flights, the total number of passengers on flights throughout the day, the passenger flow on high-section flights throughout the day, the average stop time, the average one-way time, the average departure interval, the average vehicle load factor, etc.

[0171] (2) Enterprise economic benefits The economic benefit indicators before and after mixed support include: number of vehicles, number of drivers, total passenger volume, total number of shifts, total income, daily income of working vehicles, daily income of operating vehicles, total passenger mileage, daily passenger mileage of vehicles, passenger volume of vehicle shifts, passenger volume of vehicle kilometers, income per thousand kilometers, income per thousand vehicle passenger kilometers, etc.;

[0172] (3) The evaluation of the operational service indicators before and after the scheduling feasibility mixed support includes: the number of assigned vehicles, high-section passenger flow, driving time, operating time, labor productivity, punctuality rate, number of departures, number of short-line departures, departure intervals, peak vehicle ratio, full load rate, terminal stop time, passenger volume, average passenger flow per vehicle, station passenger flow, average driver working hours, the vehicle with the longest total driving time and its time, the vehicle with the shortest total driving time and its time, the maximum departure interval, the shortest departure interval, the maximum terminal stop time, the shortest terminal stop time, peak operating mileage, operating mileage, empty trips for leaving and collecting vehicles, and mileage. The benefit indicator evaluation is limited to the actual benefit evaluation of the scheduled operation production date, covering the daily income of the operating vehicle, income per thousand kilometers, and total income. According to the analysis of mixed-train benefit indicators and the comparison of line operation service indicator parameters, the line capacity resource allocation and operation service standards are further adjusted and optimized to obtain the optimal solution for autonomous driving and mixed-train scheduling, enhance the integration and consistency of line mixed-train scheduling plan vehicle allocation resources and actual production capacity resources, and further improve the line operation service level. Through the effective integration of autonomous driving vehicles and public transportation, the full load rate of the carriage is reduced, and the utilization rate of effective shifts is increased, thereby improving passenger satisfaction and improving the refined competitiveness of public transportation services. If the full load rate of the departure schedule is too high and the carriage is crowded, the passenger service experience is poor; if the departure speed is slow, the passenger service quality is poor and the passenger flow loss is large. Through the comparison of service indicator parameter evaluation, it can be more reflected that conventional bus lines can invest more accurately in planned capacity under the mixed integration of autonomous driving vehicles, which can improve the effective shifts. Based on the passenger flow of regular bus routes and combined with the autonomous driving driving plan, the departure interval, minimum number of departures and vehicle load rate per unit time in each peak period will be determined. The departure interval and vehicle load rate will be used as the line service attributes, and according to the departure constraints and parameter standards, a mixed scheduling plan that meets the actual needs of the bus companies will be formulated. This will not only ensure the service system standards of the bus companies, but also maximize the operating efficiency of the bus companies. The method of interspersing driving plans available for autonomous driving can greatly improve the comfort of passenger compartments, thereby increasing the operating efficiency and departure speed of time periods, and at the same time, effectively improving the quality of operational services.

[0173] In order to facilitate the evaluation of the operation effect of mixed lines, the passenger service quality is converted into line operation cost. Therefore, the rationality of the schedule before and after mixed lines can be evaluated by comparing the line operation service indicators. This paper constructs a mixed line evaluation method based on the three dimensions of passenger (service) cost, enterprise (input) cost and operation (production) cost.

[0174] Passenger cost: refers to the economic losses caused by passengers waiting for the bus (including complaints to customer service, passenger return visits, production guidance, etc.); enterprise income: includes enterprise card swiping income and financial subsidy income; in summary, the total benefit of the lines before and after mixed operation = enterprise income - passenger cost.

[0175] Total cost = passenger cost + enterprise cost;

[0176] (1) Passenger cost calculation method

[0177] Upward passenger costs:

[0178]

[0179] (2) Enterprise operating costs include operating costs, financial input costs, and ticket revenue. Since empty mileage is not included in the subsidy scope, operating costs include passenger operation costs and empty mileage operation costs;

[0180] Upstream enterprise operating costs:

[0181]

[0182] Downside enterprise operating costs:

[0183]

[0184] Empty trips need to add empty trip costs, and empty trips cannot obtain financial operating subsidies. Empty trip costs are vehicle operating costs:

[0185]

[0186] Total enterprise cost = upstream enterprise operating cost + downstream enterprise operating cost + idle cost, that is:

[0187]

[0188] Integrating the model constraints and objective function, the final model is as follows:

[0189]

[0190]

[0191]

[0192] α and β are weight coefficients, α+β=1, and α=0.5 and β=0.5 are generally set;

[0193] T min Minimum departure interval, T max Maximum departure interval;

[0194] u means uplink, d means downlink;

[0195] t ui represents the departure time of the i-th train in the upward direction; t di Indicates the departure time t' of the i-th train in the downlink direction; t' indicates the length of the time period, such as 30 minutes, then 12 indicates 6:00 and 13 indicates 6:30;

[0196] Indicates the upstream s ui Passenger flow during the period; ui Table 1. The time period number of the departure time of the i-th bus in the upper row, s = {0, 1, 2, 3, 4, 5....., 47}, It means the waiting time is half of the departure interval;

[0197] n represents the number of all shifts in one row in one day, and m represents the number of stations in one row;

[0198] O represents the maximum vehicle capacity; represents the passenger flow of section at station j during period s;

[0199] L ui represents the mileage of the i-th upward trip, u e Unit cost of enterprise operation, u s Subsidy income of enterprise units, unit: yuan / km, p represents the route fare, generally two yuan; q us represents the passenger flow during the uplink period, L h represents the mileage of the kth empty trip.

[0200] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0201] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A scheduling model based on the mixed use of autonomous driving and a regular bus. It is characterized in that include: Scheduling constraint module: The scheduling constraint module is used to set the model constraints of the mixed scheduling of autonomous driving and a regular bus line, that is, the rules that must be followed in the process of mixed scheduling to avoid the generation of obvious infeasible solutions; Shift scheduling calculation module: The shift scheduling calculation module is used to calculate the results of mixed shift scheduling.

2. A scheduling model based on mixed autonomous driving and a conventional bus according to claim 1, It is characterized in that The constraints include: Support multiple departure points: Each regular route supports multiple departure stations; There are six types of buses: double shift, single shift, morning half shift, evening half shift, mid shift and double shift with a stop. Each shift can be full-distance, short-distance or other different types of shifts. Autonomous driving shifts can support both full-distance and short-distance shifts; Driver working hours: The driver’s daily working hours cannot exceed the prescribed maximum operating hours; The daily driving time cannot exceed the maximum driving time; the daily operating hours and driving hours of drivers are regulated, but the working hours of autonomous vehicle safety officers are not subject to such restrictions; Shift time: The start time of the current trip must be later than the end time of the previous trip; Meal time: Drivers must have rest periods between consecutive shifts; Sufficient additional stop time should be allocated within the meal time, and drivers should be allowed to eat according to the set meal time; Vehicles cannot stop for more than a certain time threshold; The self-driving car will stop for rest according to the rated time. During the meal period, additional time will be allocated for the safety officer to eat. Shift uniformity: Regular vehicle dispatch should be neither too sparse nor too dense, and there should be no gaps; The shifts should maintain continuity, that is, the difference between the start time of the current vehicle in the vehicle row and the start time of the previous shift in the vehicle row should be within a certain range; Autonomous driving vehicles will be evenly interspersed throughout the regular bus journey, maintaining a balanced integration and reasonable arrangement of regular and autonomous driving services. Conventional public transportation is the main support, with autonomous driving as a supplement: In order to avoid uneven distribution of workload for conventional public transport, the number of trips allocated to each driver in a day should be uniform based on characteristic factors such as turnover; Autonomous driving is based on available transport resources, allocating the number of trips on demand, and the number of mixed shifts is relatively even; The departure times of the first and last vehicles are fixed: The scheduled time for autonomous driving support is relatively fixed. The number of scheduled shifts is allocated on demand based on passenger flow, while the actual departure time varies with turnover. The intervals between shifts are calculated evenly, and the autonomous driving shifts are interspersed reasonably: The current vehicle dispatch interval needs to be reasonably calculated based on the vehicles in front and behind, and the dispatch should be as even as possible; The number of automatic dispatching classes varies with the number of interleaving classes, ensuring that the classes are arranged as evenly as possible without interruption; Realize mixed scheduling and collection of vehicles: The vehicle should be collected at the station where it left as much as possible to prevent empty trips from being collected. The self-driving vehicle will cut in and out at the default location to avoid reverse parking. Under the premise of ensuring the minimum service standard, vehicles exceeding the transport capacity may be collected; Multiple autonomous driving available support vehicles can be arranged to intersperse and collect vehicles in the forward direction; Scheduling short-line shifts and support shift generation: If there is discontinuity in passenger flow, consider sending short-distance flights; Automatically generate available autonomous driving support shifts based on passenger flow as an effective supplement; Minimum stop time: The types of tasks generated by autonomous driving include short-line and interval tasks. When the starting station is the terminal, the minimum stop time must be met. When the starting station is the intermediate station, the turnaround time must be met. Operational task configuration: When the conventional line initiates an operation demand for the autonomous driving, under the condition of meeting the idle time, the terminal station of the autonomous driving non-operating sub-line is automatically matched with the starting station of the conventional line sub-line, and the even interlacing method of the autonomous driving support vehicle is used to automatically generate a mixed scheduling plan; Departure station or terminal: The autonomous driving vehicle allocation plan is divided into full-day planned shifts and time-period planned shifts; When obtaining the all-day planned shift allocation, prioritize the average allocation of autonomous driving shifts based on the conventional line departure terminal and the number of vehicles; When obtaining the scheduled flights during the available time period, the system will prioritize the last scheduled end station of the automated driving and the nearest exit station of the conventional line, and will prioritize the nearest exit station according to the passenger flow direction; Automatic driving forward and reverse reception processing: When the scheduled operation task ends and the station collects vehicles in reverse, the planned departure time and the operation U-turn position are calculated backwards, and the original forward collection algorithm is used to carry out the short-line forward collection process.

3. An evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 1 or 2, It is characterized in that The following steps are involved: S1. Collect data and analyze demand: Collect relevant operational data and analyze current transportation demand, including passenger flow characteristics during peak and off-peak hours, and passengers’ service expectations; S2. Formulate mixed-class scheduling goals: determine the main goals of mixed-class scheduling and formulate evaluation indicators based on the goals; S3. Establish an evaluation index system: Design and establish an evaluation index system, including indicators of social benefits and economic benefits; S4. Data analysis and comparison: Use the collected data to quantitatively analyze the operational effects before and after mixed scheduling; compare the changes in key indicators before and after mixed scheduling; S5. Evaluate scheduling feasibility: Consider the feasibility of mixed-shift scheduling in the actual scheduling process and analyze potential problems and challenges in the scheduling process; S6. Formulate optimization suggestions: Based on the evaluation results, make optimization suggestions for the mixed-shift scheduling plan and propose improvement strategies to enhance the comprehensive benefits of the mixed-shift scheduling, while ensuring the maximization of passenger satisfaction and corporate economic benefits; S7. Implementation and monitoring: Convert optimization suggestions into specific implementation plans and conduct implementation monitoring. Establish a monitoring mechanism, regularly evaluate the implementation effect of the mixed-shift scheduling plan, and adjust the optimization strategy in a timely manner to adapt to changes in the transportation environment.

4. The evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 3, It is characterized in that The operation data in S1 include passenger flow, line capacity, vehicle utilization, and frequency of service. The service expectations of passengers in S1 include waiting time and riding comfort.

5. The evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 3, It is characterized in that The main objectives of S2 include improving passenger satisfaction, optimizing transportation costs and improving operational efficiency, and the evaluation indicators of S2 include passenger service quality, enterprise economic benefits and scheduling feasibility.

6. The evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 3, It is characterized in that The social benefit indicators in S3 include indicators related to passenger service quality, including average waiting time, carriage fullness rate and stop time, and the economic benefit indicators in S3 include operating costs, revenue, vehicle utilization rate and driver costs.

7. The evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 3, It is characterized in that The key indicator changes in S4 include changes in vehicle load factor, the adjustment effect of departure frequency and changes in revenue.

8. The evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 3, It is characterized in that The feasibility of the actual scheduling process in S5 includes the rationality of the departure interval, the distribution balance of the shifts and the workload of the drivers. The potential problems and challenges in S5 include the processing capacity during peak hours and the control of the stop time.

9. The evaluation of a scheduling plan based on a mixed autonomous driving and a conventional bus according to claim 3, It is characterized in that The optimization suggestions in S6 include adjusting the shift schedule, optimizing the vehicle configuration and improving the service standard.

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