Bus operation digital management platform based on low-code platform

Abstract

The invention belongs to the technical field of bus operation management, and discloses a bus operation digital management platform based on a low-code platform. Comprising a monitoring period division module, a vehicle driving data monitoring module, a running vehicle behavior analysis module, a historical personnel data monitoring module, a real-time personnel data monitoring module, a running vehicle scheduling analysis module, a vehicle scheduling effect evaluation module and a data storage library. According to the invention, based on the historical passenger transport demand condition and the real-time passenger transport demand condition of each bus station in the current monitoring time period, whether flexible scheduling vehicles need to be added to each bus station in the current monitoring time period is judged, the passenger flow rule can be accurately informed, reasonable putting of the vehicles is realized, and the resource utilization rate is improved. According to the invention, the specific scheduling mode is further identified after whether the flexible scheduling vehicle needs to be added at each bus station is judged, so that the resource utilization efficiency is practically improved, the endless loss of the transport capacity is avoided, and the vehicle delivery is perfectly matched with the passenger flow distribution.

Description

Technical Field

[0001] The present invention belongs to the technical field of public transportation operation management and relates to a digital management platform for public transportation operations based on a low-code platform. Background Art

[0002] Public transportation operation refers to the organized transportation service activities of public transportation vehicles (mainly buses) in a city or a specific area. It is the core link of the urban public transportation system. Vehicles are one of the most critical resources in the public transportation system. Through vehicle scheduling analysis, we can accurately understand the actual demand for vehicles on each line at different times. This can maximize the use of limited vehicle resources, improve vehicle utilization, and reduce operating costs. Therefore, the analysis of digital management of public transportation operations based on low-code platforms is of great significance.

[0003] In the prior art, there are also related solutions for bus operation management technology, for example, a Chinese invention patent application for a bus operation analysis system with publication number CN110555996B, which includes: a bus operation speed analysis module for analyzing the difference in operating speed between buses and buses or between buses and non-buses based on the comparison of bus operating speed and non-bus operating speed, and a bus flow analysis module for determining bus flow distribution data through floating bus GPS data, bus boarding and alighting card data, user mobile phone positioning data and other information, a bus passenger flow analysis module for determining bus passenger flow data, and a bus stop boarding and alighting volume analysis module for determining bus stop boarding and alighting volume data. The invention realizes a comprehensive analysis of bus operation characteristics through the above modules, which is conducive to improving the level of public transportation management and planning, as well as improving the level of public transportation services.

[0004] In addition, a Chinese invention patent application with publication number CN113822502A is for a bus operation planning method, a bus operation status evaluation method and its equipment. The method obtains the operation site data and operation line data of the bus lines in the planning area, determines the static bus indicators in the area based on the operation site data and operation line data, and then preliminarily judges the supply status of the bus network in the area. For areas with sufficient supply status, the dynamic bus indicators in the area are determined based on the operation line data. Finally, the bus operation status in the area is evaluated based on the static bus indicators and the dynamic bus indicators, and the bus congested sections in the area or roads are found, and the planning information of the operation sites or operation lines of the corresponding bus lines is modified. This invention combines overall multi-dimensional, static and dynamic aspects, so that the evaluation parameters involve different dimensions related to the convenience, efficiency, smoothness and punctuality of bus operations, which greatly enriches the granularity of bus evaluation and improves practicality.

[0005] Although the above two schemes have proposed some solutions for bus operation management technology, they still have certain limitations: (1) The existing technology lacks comprehensive analysis of historical and real-time passenger flow when analyzing the passenger flow situation at bus stops. This analysis method reduces the scientificity and rationality of the scheduling plan, fails to accurately meet the actual needs of the station, weakens the predictability of passenger flow analysis, and fails to predict sudden changes in passenger flow at bus stops in advance, making it difficult to make effective scheduling decisions quickly, hindering the improvement of bus operation efficiency.

[0006] (2) Existing technical solutions usually use a mechanical method of increasing the number of public buses when handling traffic anomalies, without conducting a specific analysis of the actual demand of each bus station. This analysis method reduces resource utilization efficiency, increases operating costs, causes a waste of vehicle resources and human resources, and reduces the overall efficiency of bus operations. On the one hand, too many vehicles flow into some stations, reducing road capacity, aggravating line congestion, and frequent vehicle starts and stops, extending the time on the way. On the other hand, it disrupts the original scheduling plan, causing an imbalance between vehicle intervals and operating time, and reducing operational coordination. It also reduces passenger satisfaction. The crowded platforms, poor waiting environment, and uneven vehicle intervals make it difficult to predict the waiting time for passengers, greatly affecting the passenger travel experience. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a digital management platform for bus operations based on a low-code platform is proposed.

[0008] The objective of the present invention can be achieved through the following technical solutions: a digital management platform for public transportation operations based on a low-code platform, comprising: a monitoring period division module, which is used to divide the operating time of a target bus line into several monitoring periods based on the frequency of the target bus line.

[0009] The vehicle driving data monitoring module is used to monitor the driving data of each running vehicle on the target bus line in real time and obtain the driving data of each running vehicle, wherein the driving data includes route deviation and speed abnormality.

[0010] The running vehicle behavior analysis module is used to analyze the abnormal driving status of each running vehicle and then determine whether each running vehicle has any abnormality.

[0011] The historical personnel data monitoring module is used to obtain the historical personnel data of each bus stop during the current monitoring period based on the historical data records saved in the data repository, including the historical average waiting time and the historical number of waiting people, and analyze the historical passenger demand situation of each bus stop during the current monitoring period.

[0012] The real-time personnel data monitoring module is used to obtain the real-time personnel data of each bus stop during the current monitoring period, including the real-time average waiting time and the real-time number of waiting people, and analyze the real-time passenger demand situation of each bus stop during the current monitoring period.

[0013] The vehicle dispatch analysis module is run to determine whether each bus stop needs to add flexible dispatch vehicles during the current monitoring period. The bus stops that need to add flexible dispatch vehicles are recorded as bus stops to be dispatched, and then the specific dispatch method is identified, which includes full-route vehicle dispatch and section vehicle dispatch.

[0014] The vehicle dispatch effect evaluation module is used to analyze the dispatch effect evaluation situation.

[0015] The data repository is used to store historical data records of the target bus route.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention determines whether each bus stop needs to add flexible dispatching vehicles during the current monitoring period based on the historical passenger demand and real-time passenger demand of each bus stop during the current monitoring period. This analysis method can accurately understand the passenger flow pattern, realize the reasonable deployment of vehicles, and improve resource utilization. It can effectively shorten the waiting time of passengers, reduce vehicle congestion, and improve operational efficiency. It can also improve service quality, provide passengers with a more comfortable riding environment, improve passenger satisfaction, enhance the attractiveness of public transportation, and promote the high-quality development of urban public transportation.

[0017] (2) After determining whether each bus stop needs to add flexible dispatch vehicles, the present invention further identifies the specific dispatch method. This analysis method effectively improves the efficiency of resource utilization, avoids unnecessary loss of transport capacity, and perfectly matches the vehicle deployment with the passenger flow distribution. It effectively guarantees the travel experience of passengers. The whole-process dispatching ensures the smoothness of long-distance travel. The section dispatching quickly evacuates the local passenger flow, greatly shortens the waiting and riding time, and reduces the congestion of the platform. It also profoundly changes the bus operation model and promotes it to stride forward in the direction of refinement and intelligence.

[0018] (3) The setting of the driving state abnormality index can, on the one hand, accurately grasp the fluctuation range of the normal driving state of vehicles under different time periods, different routes and various road conditions, and use these normal data as a key reference to provide a basis for subsequent vehicle operation and dispatch. On the other hand, it can strictly follow the statutory speed limit requirements in accordance with traffic regulations and standards, and route deviations cannot violate regulations such as prohibited passage and one-way streets, so as to ensure the legality and compliance of bus operations and enhance the passenger experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing 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.

[0020] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0021] Figure 2 A flowchart for determining the existence of abnormality in a running vehicle corresponding to an embodiment provided by the present invention.

[0022] Figure 3 A flowchart for determining the demand for flexible dispatching vehicles corresponding to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides a digital management platform for bus operations based on a low-code platform, including a monitoring period division module, a vehicle driving data monitoring module, a running vehicle behavior analysis module, a historical personnel data monitoring module, a real-time personnel data monitoring module, a running vehicle scheduling analysis module, a vehicle scheduling effect evaluation module and a data storage library, wherein the monitoring period division module is respectively connected to the vehicle driving data monitoring module, the historical personnel data monitoring module and the real-time personnel data monitoring module, the vehicle driving data monitoring module is connected to the running vehicle behavior analysis module, the historical personnel data monitoring module and the real-time personnel data monitoring module are both connected to the running vehicle scheduling analysis module, the running vehicle scheduling analysis module is connected to the vehicle scheduling effect evaluation module, and the data storage library is connected to the historical personnel data monitoring module.

[0025] The monitoring period division module is used to divide the operation time of the target bus line into a plurality of monitoring periods based on the shift duration of the target bus line.

[0026] Exemplarily, the operating hours are , the target bus route has a duration of , the corresponding monitoring periods are , , , , , , , , , , , 。

[0027] The vehicle driving data monitoring module is used to monitor the driving data of each operating vehicle on the target bus line in real time, obtain the driving data of each operating vehicle, and the driving data includes route deviation degree and speed abnormality degree.

[0028] It should be noted that the reasons for monitoring the driving data of each operating vehicle on the target bus line: From the perspective of ensuring operation safety and order, real-time tracking of vehicle driving trajectories and speeds can quickly detect sudden changes in direction and speeding caused by driver fatigue, etc., and can also promptly detect route deviations caused by road construction, traffic accidents, etc., facilitating early warning and corrective measures to avoid danger. From the aspect of optimizing operation efficiency, based on the monitoring data, it can be accurately analyzed whether the departure interval is reasonable and whether the vehicle stays at the station for too long, and then flexibly adjust the departure plan and allocate transportation capacity to reduce the waiting time of passengers. Moreover, these data can provide a basis for optimizing line planning, understand the traffic conditions at different times and sections, judge which areas have large passenger flows and slow vehicle speeds, and help accurately open new lines and adjust existing lines in the future to comprehensively improve the quality of bus services.

[0029] It should be noted that the reasons for selecting the route deviation degree and speed abnormality degree as the driving data of each operating vehicle: The route deviation degree can accurately reflect whether the vehicle is driving along the established route. Once a large deviation occurs, it may be caused by driver errors, sudden road conditions, or navigation failures, etc. Real-time monitoring of it can correct errors in a timely manner, ensure operation norms, enhance the sense of security of passengers' travel, and ensure smooth getting on and off at stations; the speed abnormality degree is equally important. It not only controls operation efficiency. If the vehicle speed is too slow, it will lengthen the departure interval, increase the waiting time of passengers, and disrupt the operation rhythm. If it is too fast, there are safety hazards and increased fuel consumption. It can also indirectly reflect the road traffic conditions. When the speeds of multiple vehicles are abnormally reduced, it is likely that the section is congested. The dispatching center can react in a timely manner based on this, adjust the departure plan or route arrangement. The two can be regarded as the "barometers" of bus operation, providing key bases for precise control and optimization.

[0030] In a preferred embodiment of the present invention, the specific analysis method of the route deviation degree is as follows: The positioning device is used to collect the actual positions of each running vehicle in real time, and then it is identified whether the actual positions of each running vehicle are located on the pre-set planned driving route. The initial monitoring time and the end monitoring time corresponding to the driving sections that are not located on the pre-set planned driving route are obtained. The difference between the end monitoring time and the initial monitoring time is calculated to obtain the duration of each driving section that is not located on the pre-set planned driving route, and then the sum calculation is performed to obtain the actual deviation duration of each running vehicle, and the actual driving duration of each running vehicle is obtained.

[0031] The ratio of the actual deviation duration of each running vehicle to the actual driving duration is calculated to obtain the route deviation degree of each running vehicle.

[0032] In a preferred embodiment of the present invention, the specific analysis method of the speed abnormality degree is as follows: The speed monitoring device is used to obtain the driving speed of each running vehicle in real time, and then the driving speed change curve of each running vehicle is drawn with time as the abscissa and driving speed as the ordinate. Then, based on an equal interval length, the curves are evenly distributed with points to obtain a number of speed monitoring points, and the abscissa corresponding to each speed monitoring point is recorded as the monitored driving speed corresponding to each speed monitoring point.

[0033] The difference between the monitored driving speed corresponding to each speed monitoring point and the pre-set driving speed threshold is calculated to obtain the speed abnormality amount corresponding to each speed monitoring point. Then, the ratio of the speed abnormality amount corresponding to each speed monitoring point to the driving speed threshold is calculated respectively to obtain the speed abnormality degree corresponding to each speed monitoring point, and the average value of the speed abnormality degrees corresponding to each speed monitoring point is calculated to obtain the speed abnormality degree corresponding to each running vehicle.

[0034] The running vehicle behavior analysis module is used to analyze the abnormal situation of the driving state of each running vehicle, and then judge whether there is an abnormality in each running vehicle.

[0035] In a preferred embodiment of the present invention, to analyze the abnormal situation of the driving state of each running vehicle, an abnormal index of the driving state of each running vehicle needs to be constructed, and its specific analysis method is as follows: The route deviation degree and the speed abnormality degree of each running vehicle are extracted, and then the sum calculation is performed according to the weights to obtain the abnormal index of the driving state of each running vehicle.

[0036] Exemplarily, the weights corresponding to the route deviation degree and the speed abnormality degree are respectively .

[0037] It should be noted that the basis for setting the weights corresponding to the route deviation degree and speed abnormality degree is as follows: From the perspective of safety impact, on busy roads with complex road conditions and heavy traffic, the threat of speed abnormality to driving safety cannot be underestimated. Overspeeding may instantly cause serious collisions, endangering the lives of passengers and public safety. In this case, the weight of speed abnormality should be increased. Similarly, if the route deviation degree is likely to cause the vehicle to enter a restricted area or a dangerous section under construction by mistake, its weight also needs to be increased. From the perspective of operation efficiency analysis, if a certain section of the road has extremely high requirements for vehicle speed at a certain time period, once the speed abnormality deviation occurs, it will seriously disrupt the operation rhythm, lengthen the headway, and increase the waiting time of passengers. In this case, the weight should be emphasized. On the contrary, if the bus line layout in a certain area is precise, a small route deviation may cause line chaos and affect vehicle intersections. At this time, the weight of the route deviation degree is more critical.

[0038] In a preferred embodiment of the present invention, please refer to Figure 2 As shown, the specific method for determining whether there is an abnormality in each running vehicle is as follows: Extract the driving state abnormality index of each running vehicle, and then compare it with the pre-set driving state abnormality index threshold respectively. If the driving state abnormality index of a certain running vehicle is greater than the driving state abnormality index threshold, it is determined that the running vehicle has an abnormality; otherwise, it is determined that the running vehicle has no abnormality.

[0039] Exemplarily, the driving state abnormality index threshold is .

[0040] It should be noted that the basis for setting the driving state abnormality index threshold is as follows: On the one hand, through in-depth mining and detailed analysis of a large amount of historical operation data, accurately grasp the fluctuation range of the normal driving state of vehicles under different time periods, different lines, and various road conditions, and use these normal state data as key references to initially define the threshold to ensure the effective distinction between normal and abnormal situations. For example, on congested roads during the morning and evening rush hours on weekdays, the vehicle speed is generally low and the route adjustment is frequent, and the corresponding threshold should be adapted to this complex situation; while at night or in the early morning when the traffic is light and the roads are smooth, the threshold setting is quite different. On the other hand, fully consider the traffic law standards. In terms of speed, strictly follow the legal speed limit requirements, and the route deviation cannot violate regulations such as prohibited passage and one-way streets. Incorporate the legal red line into the threshold setting to ensure the legality and compliance of bus operations. Furthermore, combine the passenger experience feedback. If passengers frequently complain about the long waiting time and the decline in riding comfort due to slow vehicle speed or unreasonable routes, adjust the threshold accordingly to prompt the operator to optimize the service and comprehensively ensure the accuracy and effectiveness of the monitoring of the driving state of bus vehicles.

[0041] The historical personnel data monitoring module is used to obtain the historical personnel data of each bus stop in the current monitoring period based on the historical data records saved in the data repository, including the historical average waiting time and the historical number of waiting personnel, and analyze the historical passenger demand situation of each bus stop in the current monitoring period.

[0042] It should be noted that the specific method for obtaining the historical personnel data of each bus stop in the current monitoring period is as follows: extract the historical data records saved in the data repository, and then obtain the corresponding historical data in the current monitoring period. Based on the corresponding historical data in the current monitoring period, obtain the initial waiting time and the end waiting time of each person at each bus stop in the current monitoring period corresponding to each historical data. Then, calculate the difference between the end waiting time and the initial waiting time to obtain the historical waiting time of each person at each bus stop in the current monitoring period corresponding to each historical data. Then, calculate the average value to obtain the historical average waiting time of each bus stop in the current monitoring period corresponding to each historical data. Then, further calculate the average value to obtain the historical average waiting time of each bus stop in the current monitoring period.

[0043] Extract the historical data records saved in the data repository, and then obtain the corresponding historical data in the current monitoring period. Obtain the number of people appearing at each bus stop in the current monitoring period corresponding to each historical data. Then, calculate the average value to obtain the historical number of waiting personnel at each bus stop in the current monitoring period.

[0044] In a preferred embodiment of the present invention, to analyze the historical passenger demand situation of each bus stop in the current monitoring period, it is necessary to construct a historical passenger demand index for each bus stop in the current monitoring period. The specific method is as follows: extract the historical average waiting time and the historical number of waiting personnel of each bus stop in the current monitoring period, and record them as 、 respectively, where represents the number of the bus stop, , represents the number of bus stops.

[0045] Use the formula to analyze and obtain the historical passenger demand index of each bus stop in the current monitoring period, where represents the preset reference waiting time, and represents the preset reference number of waiting personnel.

[0046] It should be noted that the reasons for selecting the historical average waiting time and the historical number of waiting passengers as the influencing factors of the historical passenger demand index for each bus stop during the current monitoring period are as follows: First, the historical average waiting time can accurately reflect the time consumption of passengers waiting at the stop during past operations. The magnitude of this value is directly related to the busyness of the stop's passenger transport. If the average waiting time at a certain stop is relatively long, it indicates that the vehicle allocation in the past was difficult to meet the passenger flow demand, and there was a problem of insufficient transport capacity. For example, at the stops in emerging commercial areas, the average waiting time significantly increases on weekends and holidays compared to weekdays, and the operation department can thus know that additional transport capacity needs to be allocated during these periods. Second, the historical number of waiting passengers directly shows the actual scale of the number of passengers waiting at the stop during past periods. By analyzing the quantity changes at different dates and times, the tidal pattern of the passenger flow can be clearly understood. For example, at the bus stops near schools, obvious peaks in the historical number of waiting passengers will occur during the morning and evening rush hours on weekdays and during school start and end times. Based on this, the operator can arrange resources such as vehicles and drivers in advance to achieve an accurate match between transport capacity and passenger demand and optimize bus operations.

[0047] The real-time passenger data monitoring module is used to obtain the real-time passenger data of each bus stop during the current monitoring period, including the real-time average waiting time and the real-time number of waiting passengers, and analyze the real-time passenger demand situation of each bus stop during the current monitoring period.

[0048] In a preferred embodiment of the present invention, to analyze the real-time passenger demand situation of each bus stop during the current monitoring period, a real-time passenger demand index for each bus stop during the current monitoring period needs to be constructed, and the specific method is as follows: Extract the real-time average waiting time and the real-time number of waiting passengers of each bus stop during the current monitoring period, and based on the historical data records of the target bus routes stored in the data repository, obtain the cumulative average waiting time and the cumulative number of waiting passengers of each bus stop during the current monitoring period in real time.

[0049] It should be supplemented that the specific method for obtaining the real-time average waiting time and the real-time number of waiting passengers of each bus stop during the current monitoring period is as follows: Use a high-definition camera to collect monitoring videos of each bus stop, and then use face recognition technology to obtain each person who appears at each bus stop during the current monitoring period. Based on the monitoring videos of each bus stop, obtain the start waiting time and the end waiting time of each person corresponding to each bus stop during the current monitoring period, and then calculate the difference between the end waiting time and the start waiting time to obtain the actual waiting time of each person corresponding to each bus stop during the current monitoring period, and then calculate the average value to obtain the actual average waiting time of each bus stop during the current monitoring period.

[0050] Based on the monitoring videos of each bus stop, count the real-time number of waiting passengers corresponding to each bus stop during the current monitoring period.

[0051] Calculate the difference between the real-time average waiting time of each bus stop in the current monitoring period and the cumulative average waiting time of the corresponding bus stops to obtain the waiting time anomaly amount of each bus stop in the current monitoring period, and then calculate the ratio with the cumulative average waiting time of the corresponding monitoring periods to obtain the waiting time anomaly degree of each bus stop in the current monitoring period.

[0052] Calculate the difference between the real-time number of waiting passengers at each bus stop in the current monitoring period and the cumulative number of waiting passengers at the corresponding bus stops to obtain the anomaly amount of the number of waiting passengers at each bus stop in the current monitoring period, and then calculate the ratio with the cumulative number of waiting passengers at the corresponding monitoring periods to obtain the anomaly degree of the number of waiting passengers at each bus stop in the current monitoring period.

[0053] Sum up the waiting time anomaly degree and the anomaly degree of the number of waiting passengers at each bus stop in the current monitoring period according to the weights to obtain the real-time passenger demand index of each bus stop in the current monitoring period.

[0054] Exemplarily, the weights corresponding to the waiting time anomaly degree and the anomaly degree of the number of waiting passengers are respectively .

[0055] It should be noted that the basis for setting the weights corresponding to the waiting time anomaly degree and the anomaly degree of the number of waiting passengers: From the perspective of passenger experience, if at a certain time and a certain stop, passengers are extremely sensitive to the waiting time. For example, during the early morning commute on weekdays, office workers are in a hurry and excessive waiting will make them extremely anxious. At this time, the weight of the waiting time anomaly degree should be increased to highlight the importance of promptly dispersing the passenger flow and shortening the waiting time. On the contrary, if at a bus stop after a large-scale event, a large number of people gather, and passengers are more concerned about whether there is enough transportation capacity to transport the crowd at one time, then the weight of the anomaly degree of the number of waiting passengers should be emphasized. From the perspective of operation efficiency, when the operation resources of the line are relatively tight and the vehicle dispatching is difficult, if the waiting time anomaly at a certain stop is likely to disrupt the subsequent departure rhythm and affect the smoothness of the entire line, the weight of the waiting time anomaly degree needs to be increased.

[0056] The operating vehicle dispatching analysis module is used to determine whether additional flexible dispatching vehicles are needed at each bus stop in the current monitoring period, record the bus stops that need to add flexible dispatching vehicles as the bus stops to be dispatched, and then identify the specific dispatching methods, and the dispatching methods include full-course vehicle dispatching and section vehicle dispatching.

[0057] In a preferred embodiment of the present invention, please refer to Figure 3As shown in the figure, the specific method for determining whether flexible scheduling vehicles need to be added to each bus stop during the current monitoring period is as follows: extract the historical passenger demand index and real-time passenger demand index of each bus stop during the current monitoring period, and then calculate the sum according to the weights to obtain the vehicle scheduling demand index of each bus stop during the current monitoring period.

[0058] Exemplarily, the corresponding weights of the historical passenger demand index and the real-time passenger demand index are respectively .

[0059] It should be noted that the setting basis of the corresponding weights of the historical passenger demand index and the real-time passenger demand index: on the one hand, considering the stability of bus operation rules, the historical passenger demand index reflects the long-term passenger flow rules. During the commuting period on weekdays and at the stations in mature business districts, the passenger flow is stable. When operating smoothly daily, giving it a higher weight can ensure that the rhythm is not disrupted. On the other hand, to cope with emergencies such as sudden heavy rain and sudden passenger flows during large-scale events, the real-time passenger demand index is crucial, and the weight needs to be increased to achieve agile response. At the same time, starting from the optimal allocation of resources, when vehicle resources are sufficient, adjust the weight flexibly according to the real-time situation; when resources are tight, take both into account to balance the transport capacity and demand, laying a solid foundation for the efficient operation of the bus.

[0060] It should be noted that the present invention determines whether flexible scheduling vehicles need to be added to each bus stop during the current monitoring period based on the historical passenger demand situation and the real-time passenger demand situation of each bus stop during the current monitoring period. This analysis method can accurately insight into the passenger flow rules, realize the reasonable allocation of vehicles, and improve the resource utilization rate. It can effectively shorten the waiting time of passengers, reduce vehicle congestion, and improve the operation efficiency. It can also improve the service quality, provide a more comfortable riding environment for passengers, improve passenger satisfaction, enhance the attractiveness of bus travel, and promote the high-quality development of urban public transportation.

[0061] Compare the vehicle scheduling demand index of each bus stop during the current monitoring period with the pre-set vehicle scheduling demand index threshold respectively. If the vehicle scheduling demand index of a certain bus stop is greater than the vehicle scheduling demand index threshold, it is determined that the bus stop needs to add flexible scheduling vehicles; otherwise, it is determined that the bus stop does not need to add flexible scheduling vehicles.

[0062] Exemplarily, the vehicle scheduling demand index threshold is .

[0063] In a preferred embodiment of the present invention, the analysis process for identifying the specific scheduling method is as follows: count the bus stops to be scheduled, and then calculate the proportion of the number of bus stops to be scheduled in the total number of bus stops corresponding to the target bus line.

[0064] Compare the proportion of the number of bus stops to be scheduled with the preset threshold of the proportion of the number of bus stops to be scheduled. If the proportion of the number of bus stops to be scheduled is greater than the threshold of the proportion of the number of bus stops to be scheduled, identify the specific scheduling method as full-course vehicle scheduling; otherwise, identify the specific scheduling method as section vehicle scheduling.

[0065] Exemplarily, the threshold of the proportion of the number of bus stops to be scheduled is .

[0066] It should be noted that the setting basis of the threshold of the proportion of the number of bus stops to be scheduled: on the one hand, considering from the perspective of operating costs, if the threshold is too low, full-course vehicle scheduling will be initiated even with slight passenger flow fluctuations. The vehicles shuttle back and forth along the whole route frequently, resulting in increased fuel and power consumption, and may also cause overcapacity in some sections, leading to serious waste of resources; while if the threshold is too high, section vehicle scheduling is overused. During peak passenger flow periods, in the face of insufficient capacity at a large number of stops, passengers have to wait for a long time, and the operation efficiency drops sharply. On the other hand, it is related to the passenger experience. During peak commuting hours, such as in the early morning and evening on weekdays, passengers are eager to get home, the passenger flow is concentrated and the flow direction is single, so the threshold needs to be lowered to ensure the rapid evacuation of the passenger flow; during off-peak hours, the passenger flow is dispersed and the travel demands are diverse, so the threshold is appropriately increased, and section scheduling is flexibly used to not only meet the basic travel needs of passengers but also take into account the operating economy, and use a reasonable threshold to maximize the operating efficiency of the bus.

[0067] It should be noted that after the vehicle scheduling operation is executed, it is also necessary to pay attention to the occupancy rate of each running vehicle in real time. If the average occupancy rate of each running vehicle is lower than the preset reference occupancy rate, the number of flexible scheduling vehicles is reduced.

[0068] The vehicle scheduling effect evaluation module is used to analyze the vehicle scheduling effect evaluation situation.

[0069] In a preferred embodiment of the present invention, to analyze the vehicle scheduling effect evaluation situation, a vehicle scheduling effect evaluation index needs to be constructed, and the specific method is as follows: obtain the actual number of passengers carried by each scheduling vehicle, and then calculate the ratio with the number of seats of each scheduling vehicle to obtain the occupancy rate of each scheduling vehicle.

[0070]

[0071] ​Obtain the actual passenger numbers of each operating vehicle after scheduling and the actual passenger numbers of each operating vehicle before scheduling, and then calculate the ratios with the seat numbers of each operating vehicle respectively to obtain the actual occupancy rates of each operating vehicle after scheduling and the actual occupancy rates of each operating vehicle before scheduling.

[0072] Calculate the average value of the actual occupancy rates of each operating vehicle after scheduling and the occupancy rates of each scheduled vehicle to obtain the reference occupancy rate. Then, calculate the difference between the actual occupancy rate of each operating vehicle before scheduling and the reference occupancy rate to obtain the occupancy rate optimization amount. Further, calculate the ratio of the occupancy rate optimization amount to the reference occupancy rate to obtain the scheduling effect evaluation index.

[0073] The data repository is used to save the historical data records of the target bus line.

[0074] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A bus operation digital management platform based on a low-code platform, characterized in that: include: A monitoring period division module is used to divide the operation time of the target bus line into several monitoring periods based on the frequency of the target bus line; The vehicle driving data monitoring module is used to monitor the driving data of each running vehicle on the target bus line in real time and obtain the driving data of each running vehicle, wherein the driving data includes the route deviation degree and the speed abnormality degree; The running vehicle behavior analysis module is used to analyze the abnormal driving status of each running vehicle, and obtain the driving status abnormality index of each running vehicle through the route deviation and speed abnormality analysis of each running vehicle; and then judge whether each running vehicle has abnormality; The historical passenger data monitoring module is used to obtain the historical passenger data of each bus stop during the current monitoring period based on the historical data records stored in the data repository, including the historical average waiting time and the historical number of waiting passengers, and analyze the historical passenger demand of each bus stop during the current monitoring period; The real-time passenger data monitoring module is used to obtain the real-time passenger data of each bus stop during the current monitoring period, including the real-time average waiting time and the real-time number of waiting passengers, and analyze the real-time passenger demand of each bus stop during the current monitoring period; The vehicle dispatch analysis module is run to determine whether each bus stop needs to add flexible dispatch vehicles during the current monitoring period, and the bus stops that need to add flexible dispatch vehicles are recorded as bus stops to be dispatched. The specific dispatch method is determined according to the proportion of bus stops to be dispatched, and the dispatch method includes full-course vehicle dispatch and section vehicle dispatch; Vehicle dispatch effect evaluation module, used to analyze the dispatch effect evaluation situation; The data repository is used to store historical data records of the target bus route.

2. A public transportation operation digital management platform based on a low-code platform as claimed in claim 1, characterized in that: The specific analysis method of the route deviation degree is as follows: Using the positioning device to collect the actual position of each running vehicle in real time, and then identify whether the actual position of each running vehicle is located on the preset planned driving route, obtain the initial monitoring time and the end monitoring time corresponding to each driving section not located on the preset planned driving route, calculate the difference between the end monitoring time and the initial monitoring time to obtain the duration of each driving section not located on the preset planned driving route, and then perform a sum calculation to obtain the actual offset duration of each running vehicle, and obtain the actual driving duration of each running vehicle; The route deviation degree of each running vehicle is calculated by calculating the ratio of the actual deviation time of each running vehicle to the actual driving time.

3. A public transportation operation digital management platform based on a low-code platform as claimed in claim 2, characterized in that: The specific analysis method of the speed abnormality is as follows: The speed monitoring device is used to obtain the driving speed of each running vehicle in real time, and then the speed change curve of each running vehicle is drawn with time as the horizontal coordinate and the driving speed as the vertical coordinate, and then each curve is evenly distributed based on equal interval length to obtain a number of speed monitoring points, and the horizontal coordinate corresponding to each speed monitoring point is marked as the monitoring driving speed corresponding to each speed monitoring point; The monitored driving speed corresponding to each speed monitoring point is calculated by difference with the preset driving speed threshold to obtain the speed abnormality corresponding to each speed monitoring point, and then the speed abnormality corresponding to each speed monitoring point is calculated by ratio with the driving speed threshold to obtain the speed abnormality degree corresponding to each speed monitoring point, and the speed abnormality degree corresponding to each speed monitoring point is averaged to obtain the speed abnormality degree corresponding to each running vehicle.

4. A public transportation operation digital management platform based on a low-code platform as claimed in claim 1, characterized in that: The analysis of the abnormal driving state of each running vehicle requires the construction of the abnormal driving state index of each running vehicle, and the specific analysis method is as follows: The route deviation and speed abnormality of each running vehicle are extracted, and then the driving state abnormality index of each running vehicle is obtained by summing them up according to the weight.

5. A public transportation operation digital management platform based on a low-code platform as claimed in claim 4, characterized in that: The specific method of determining whether each running vehicle has an abnormality is as follows: The abnormal driving state index of each running vehicle is extracted, and then compared with the preset abnormal driving state index threshold. If the abnormal driving state index of a running vehicle is greater than the abnormal driving state index threshold, it is judged that the running vehicle has an abnormality. Otherwise, it is judged that the running vehicle does not have an abnormality.

6. A public transportation operation digital management platform based on a low-code platform as claimed in claim 1, characterized in that: The analysis of the historical passenger demand situation of each bus stop during the current monitoring period requires the construction of a historical passenger demand index for each bus stop during the current monitoring period, and the specific method is as follows: Extract the historical average waiting time and the historical number of waiting people at each bus stop during the current monitoring period, and record them as , ,in Indicates the bus stop number. , Indicates the number of bus stops; Using the formula Analyze and obtain the historical passenger demand index of each bus stop during the current monitoring period ,in Indicates the preset reference waiting time. Indicates the preset reference number of waiting personnel.

7. A public transportation operation digital management platform based on a low-code platform as claimed in claim 6, characterized in that: The analysis of the real-time passenger demand situation of each bus stop during the current monitoring period requires the construction of a real-time passenger demand index for each bus stop during the current monitoring period, and the specific method is as follows: Extract the real-time average waiting time and the real-time number of waiting people at each bus stop during the current monitoring period, and obtain the cumulative average waiting time and the cumulative number of waiting people at each bus stop during the current monitoring period in real time based on the historical data records of the target bus line stored in the data repository; The difference between the real-time average waiting time of each bus stop in the current monitoring period and the corresponding cumulative average waiting time of each bus stop is calculated to obtain the abnormal amount of waiting time of each bus stop in the current monitoring period, and then the abnormal degree of waiting time of each bus stop in the current monitoring period is calculated by ratio calculation with the corresponding cumulative average waiting time of each monitoring period; The difference between the real-time number of waiting people at each bus stop during the current monitoring period and the cumulative number of waiting people at each corresponding bus stop is calculated to obtain the abnormal number of waiting people at each bus stop during the current monitoring period, and then the ratio is calculated with the cumulative number of waiting people at each corresponding monitoring period to obtain the abnormal degree of the number of waiting people at each bus stop during the current monitoring period; The real-time passenger demand index of each bus stop during the current monitoring period is calculated by summing up the abnormality of the waiting time and the abnormality of the number of waiting people at each bus stop during the current monitoring period according to the weights.

8. A public transportation operation digital management platform based on a low-code platform as claimed in claim 7, characterized in that: The specific method for determining whether each bus stop in the current monitoring period needs to add flexible dispatch vehicles is as follows: The historical passenger demand index and real-time passenger demand index of each bus stop during the current monitoring period are extracted, and then the vehicle dispatch demand index of each bus stop during the current monitoring period is obtained by summing them up according to the weights; The vehicle dispatch demand index of each bus stop in the current monitoring period is compared with the pre-set vehicle dispatch demand index threshold. If the vehicle dispatch demand index of a bus stop is greater than the vehicle dispatch demand index threshold, it is judged that the bus stop needs to add flexible dispatch vehicles. Otherwise, it is judged that the bus stop does not need to add flexible dispatch vehicles.

9. A public transportation operation digital management platform based on a low-code platform as claimed in claim 8, characterized in that: The analysis process of identifying the specific scheduling method is as follows: The bus stops to be dispatched are counted, and then the proportion of the total number of bus stops corresponding to the target bus line is calculated to obtain the proportion of the number of bus stops to be dispatched; The proportion of the number of bus stops to be dispatched is compared with the preset threshold of the proportion of the number of bus stops to be dispatched. If the proportion of the number of bus stops to be dispatched is greater than the threshold, the specific dispatching method is identified as full-route vehicle dispatching; otherwise, the specific dispatching method is identified as section vehicle dispatching.

10. A public transportation operation digital management platform based on a low-code platform as claimed in claim 1, characterized in that: The analysis of the scheduling effect evaluation situation requires the construction of a scheduling effect evaluation index, and the specific method is as follows: The actual number of passengers carried by each dispatched vehicle is obtained, and then the occupancy rate of each dispatched vehicle is calculated by comparing the actual number of passengers carried by each dispatched vehicle with the number of seats of each dispatched vehicle; The actual number of passengers carried by each running vehicle after the dispatch and the actual number of passengers carried by each running vehicle before the dispatch are obtained, and then the ratios are calculated with the number of seats of each running vehicle to obtain the actual occupancy rate of each running vehicle after the dispatch and the actual occupancy rate of each running vehicle before the dispatch; The actual seating rate of each running vehicle after scheduling and the seating rate of each scheduled vehicle are averaged to obtain the reference seating rate, and then the actual seating rate of each running vehicle before scheduling and the reference seating rate are differentially calculated to obtain the optimized seating rate, and then the scheduling effect evaluation index is obtained by calculating the ratio of the optimized seating rate to the reference seating rate.

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