Urban service optimization system based on group intelligence

Abstract

The invention discloses an urban service optimization system based on swarm intelligence, and the system comprises the steps: obtaining historical taking data and urban road network data, building a database and an urban road network coordinate system, analyzing the delay time of a previous bus, evaluating the delay deviation, analyzing the stations, which coincide with other bus routes, in remaining routes, and carrying out the optimization of the urban service. The method comprises the following steps: acquiring historical riding data of a target bus, evaluating shunting deviation, further predicting a full-load rate of the target bus arriving at remaining stations according to the historical riding data, adjusting departure time of a next bus according to the predicted full-load rate, analyzing riding information of passengers, evaluating passenger flow when the bus stops at the target station, and judging whether to carry out early warning analysis or not. If yes, the driving states of the motor vehicles and the non-motor vehicles near the target station are further evaluated, an early warning result is obtained, and broadcasting is controlled to send out safety reminding according to the analyzed early warning result, and the method has the advantages of improving the passenger travel efficiency and guaranteeing the bus passenger safety.

Description

Technical Field

[0001] The present invention relates to the technical field of public transportation optimization, and in particular to an urban service optimization system based on swarm intelligence. Background Art

[0002] With the rapid development of cities in recent years, public transportation systems have become an important means of travel for urban residents. At present, urban residents can take the bus to go to work, school, buy groceries, etc. However, with the rapid growth of residents and the expansion of road scale, the original fixed bus departure time interval easily causes residents to wait too long for the bus or cannot get on the bus. Residents pay more attention to travel efficiency when traveling, and most of the lanes in front of the bus stops are non-motorized vehicle lanes. There are more and more motor vehicles and non-motor vehicles on the road. When the bus stops, the door suddenly opens, and passengers, motor vehicles and non-motor vehicles on the road fail to notice and avoid it in time, which can easily cause accidental injury accidents.

[0003] Therefore, it is necessary to design an urban service optimization system based on swarm intelligence to improve passenger travel efficiency and ensure the safety of bus passengers. Summary of the invention

[0004] The purpose of the present invention is to provide a city service optimization system based on swarm intelligence to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a city service optimization system based on swarm intelligence, characterized in that it includes a data collection module, a departure time optimization module, a warning analysis module and a broadcast reminder module, wherein the data collection module is used to obtain historical riding data, establish a historical database, obtain urban road network data, establish an urban road network database and an urban road network coordinate system;

[0006] The departure time optimization module is used to analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the platforms in the remaining routes of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining platforms based on historical riding data, combined with delay deviation and diversion deviation. According to the predicted full load rate, the operating status of the target bus is evaluated and the departure time of the next bus is adjusted;

[0007] The warning analysis module is used to analyze the passenger riding information, evaluate the passenger flow when the bus stops at the target station, determine whether to perform a warning analysis, and if necessary, further evaluate the driving status of motor vehicles and non-motor vehicles near the target station to obtain a warning result;

[0008] The broadcast reminder module is used to control the bus broadcast, platform broadcast and motor vehicle broadcast according to the analyzed warning results, and issue arrival reminders and safety reminders.

[0009] According to the above technical solution, the departure time optimization module includes a bus operation status analysis module and a departure time adjustment module. The bus operation status analysis module is used to analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the platforms in the remaining route of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining platforms based on historical riding data, combined with delay deviation and diversion deviation. The departure time adjustment module evaluates the operation status of the target bus based on the predicted full load rate and adjusts the departure time of the next bus.

[0010] According to the above technical solution, the bus operation status analysis module includes a delay deviation analysis submodule, a diversion deviation analysis submodule and a full load rate prediction submodule. The delay deviation analysis submodule is used to analyze the delay duration of the previous bus and evaluate the impact of the delay duration on the number of passengers getting on and off the bus. The diversion deviation analysis submodule is used to analyze the stations in the remaining route of the previous bus that overlap with other bus routes and evaluate the impact of passenger diversion on the full load rate. The full load rate prediction submodule is used to predict the full load rate of the target bus arriving at the remaining stations based on historical riding data, combined with delay deviation and diversion deviation.

[0011] According to the above technical solution, the early warning analysis module includes a bus arrival analysis unit and a platform early warning analysis unit. The bus arrival analysis unit is used to analyze the distance between the bus and the platform, determine whether the bus stops at the platform, and send a stop signal. The platform early warning analysis unit analyzes the passenger's riding information characteristics, obtains the passenger flow when the bus stops at the platform, determines whether to perform early warning analysis, and obtains the platform early warning result.

[0012] According to the above technical solution, the warning analysis module also includes a passenger warning analysis unit and a motor vehicle warning analysis unit. The passenger warning analysis unit is used to analyze the driving status of non-motor vehicles in front of the target platform after receiving the warning analysis signal, evaluate the safety index of passengers getting off the bus when it stops at the target platform, and obtain passenger warning results. The motor vehicle warning analysis unit is used to analyze the position characteristics of the motor vehicle and the target platform after receiving the warning analysis signal, and obtain the motor vehicle warning results.

[0013] According to the above technical solution, the operation method of the urban service optimization system includes the following steps:

[0014] Step S1: Acquire historical riding data, establish a historical database, acquire urban road network data, establish an urban road network database and an urban road network coordinate system;

[0015] Step S2: Analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the stations in the remaining routes of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining stations based on historical riding data, combined with delay deviation and diversion deviation. According to the predicted full load rate, evaluate the operating status of the target bus and adjust the departure time of the next bus;

[0016] Step S3: Analyze the passenger riding information, evaluate the passenger flow when the bus stops at the target station, determine whether to perform early warning analysis, and if necessary, further evaluate the driving status of motor vehicles and non-motor vehicles near the target station to obtain early warning results;

[0017] Step S4: According to the analyzed warning results, the in-bus broadcast, platform broadcast and vehicle-mounted broadcast are controlled to issue a safety reminder.

[0018] According to the above technical solution, step S1 further includes the following steps:

[0019] Step S11: Obtain historical riding data, including the number of passengers getting on and off each bus station at each time period for each bus route, establish a historical database and enter the data into the database;

[0020] Step S12: Obtain urban road network data, including urban road network diagram, road name, road length, road curvature, road slope and road intersection location, establish an urban road network database, enter the data into the database, establish an urban road network coordinate system, mark the target bus route, mark the turning point coordinates and the coordinates of each platform point of the target bus route, and obtain the bus location point coordinates, motor vehicle location point coordinates and non-motor vehicle location point coordinates in real time.

[0021] According to the above technical solution, step S2 further includes the following steps:

[0022] Step S21: Obtain the total number of passengers N on the previous bus 总 and the maximum passenger capacity N max , calculate the real-time full load rate of the previous bus

[0023] Step S22: The last bus marks a starting timestamp when it leaves the starting station, and marks a station timestamp each time it arrives at a station during the driving process. The difference between the station timestamp and the starting timestamp is used as the actual running time T of the last bus arriving at the station.i , T i represents the actual running time of the bus from the starting station to the i-th station, and retrieves the running time threshold t of the bus from the starting station to the i-th station in the database i , if T i <t i , then the bus is delayed, calculate the delay time T 晚点 =T i -t i , according to the bus delay time T 晚点 Retrieve the corresponding boarding number deviation coefficient μi and disembarking number deviation coefficient v from the database i , according to the historical riding data, the number of people N who boarded the original target bus at each station i and the number of people getting off the bus M i , according to the delay time deviation, the predicted number of boarding passengers of the target bus at each station is obtained. i N i and the predicted number of people getting off the bus v i M i ;

[0024] Step S23: Obtain the stations in the remaining routes of the target bus that overlap with other bus routes. If the number of overlapping stations is N 重 ≥2, the bus route is marked as a diversion bus route, and the bus corresponding to the diversion bus route is a diversion bus. 重 Retrieve the corresponding dilution weight ε from the database j , get the number of diverted buses k at each station j , calculate the diversion influence coefficient In the formula, j = 1, 2…, n, j represents the number of overlapping stops between the diverted bus and the target bus, k j means there are k diversion buses with j overlapping stations, ε j The dilution weight corresponding to the number of overlapping stations j is ε;

[0025] Step S24: Based on the predicted number of people μ on board i N i and the predicted number of people getting off the bus v i M i and the diversion influence coefficient ξ, predicting the full load rate of the previous bus arriving at the remaining platforms on the target route Where i = 1, 2, ..., n, N 总 is the total number of passengers in the bus, and compares the predicted full load rate P of each platform during the period i and full load rate threshold θ, if P i>θ, the previous bus has an abnormal operation state, and the next bus is dispatched immediately to coordinate the bus operation state of the target bus route. If P j ≤θ, the previous bus is in normal operation and the next bus will depart according to the original cycle.

[0026] According to the above technical solution, step S3 further includes the following steps:

[0027] Step S31: Obtain the coordinates of the bus location point in the urban road network coordinate system, traverse the target bus route according to the bus driving direction, stop when traversing to the platform point, and use it as the target platform point, where the bus location point, turning point and target platform point are used as m+1 division nodes to divide the target route into m road segments, and obtain the location coordinates of m+1 division nodes according to the traversal order (a i , b i );

[0028] Step S32: Obtain the road curvature Q of the road segments between m+1 partition nodes 1_2 , Q 2_3 , ..., Q m_m+1 , where Q m_m+1 Refers to the road curvature of the road segment between the mth division node and the m+1th division node. The corresponding curvature influence coefficient α in the database is retrieved according to the road curvature. 1_2 , α 2_3 , ..., α m_m+1 , get the road slope P of the road segment between m+1 divided nodes 1_2 , P 2_3 , ..., P m_m+1 , where P m_m+1 Refers to the road slope of the road section between the mth division node and the m+1th division node, and retrieves the corresponding slope influence coefficient β from the database 1_2 , β 2_3 , ..., β m_m+1 ;

[0029] Step S33: Calculate the distance between the bus and the target platform based on the position coordinates of each division node and the road curvature and road slope of the road segment between each division node. Where, k = 1, 2, ..., m. If the distance L between the bus and the target platform is less than or equal to the threshold, it is judged that the bus is about to stop at the target platform, and a bus arrival signal is issued.

[0030] According to the above technical solution, step S33 further includes the following steps:

[0031] Step S331: After receiving the signal that the bus is about to arrive at the station, obtain the passenger information in the database and count the number of passengers M who get off the target bus at the target station. 下 and the number of people on board M 上 , calculate the passenger flow of the target platform K = M 下 +M 上 If the passenger flow K is 0, no early warning analysis is performed. If the passenger flow K is greater than 0, it is determined that early warning analysis is needed, and an early warning analysis signal is issued, and the target platform broadcast is controlled to provide safety reminders to the waiting passengers at the target platform and the non-motor vehicles in front of the target platform;

[0032] Step S332: After receiving the warning analysis signal, further obtain the target station location coordinates (a1, b1), obtain the location coordinates (a2, b2) of all non-motor vehicles on the road section where the target bus is about to arrive at the station, obtain the road curvature Q1 and road slope P1 of the road section where the station is located, retrieve the corresponding curvature influence coefficient α1 and slope influence coefficient β1, and calculate the distance between each non-motor vehicle and the target station. Where (a1, b1) represents the location coordinates of the platform, (a2, b2) represents the location coordinates of the non-motor vehicle, and the travel speed V of each non-motor vehicle is identified. 非 , according to the driving speed V 非 Retrieve the corresponding non-motor vehicle warning distance threshold between each non-motor vehicle and the target station in the database Comparison of L1 and non-motor vehicle warning distance thresholds If there is a non-motor vehicle in the traffic flow Then control the broadcasting in the bus to issue safety reminders to passengers;

[0033] Step S333: After receiving the warning analysis signal, further obtain the position coordinates (a3, b3) of all motor vehicles on the road section where the bus is about to arrive, obtain the position coordinates (a1, b1) of the target platform, obtain the road curvature Q1 and road slope P1 of the road section where the platform is located, retrieve the corresponding curvature influence coefficient α1 and slope influence coefficient β1, and calculate the distance between each motor vehicle and the target platform Where, (a1, b1) represents the location coordinates of the station, (a3, b3) represents the location coordinates of the motor vehicle, and L2 is compared with the motor vehicle warning distance threshold φ. If L2<φ, the motor vehicle is marked as within the warning reminder range, and the motor vehicle's on-board broadcast is controlled to provide safety reminders to the motor vehicle driver.

[0034] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention predicts the full load rate of the bus according to historical riding data by analyzing the impact of delays and diversions, evaluates the operating status of the previous bus according to the predicted full load rate, adjusts the departure time of the next bus, relieves operating pressure, improves the travel efficiency of passengers, reduces the long waiting time of passengers due to excessive passenger flow, optimizes the allocation of public transportation resources, and effectively improves the operating efficiency of the bus; by analyzing the traffic status of non-motor vehicles between the bus and the platform when passengers get off the bus, a safety reminder is issued to the passengers to ensure the safety of passengers getting off the bus; by analyzing whether the bus stops at the platform and the passenger flow getting on and off the bus when stopping at the platform, a safety reminder is issued to the non-motor vehicles traveling between the bus and the platform, so that the non-motor vehicles can have time to slow down and avoid the flow of people, thereby ensuring the safety of passengers and non-motor vehicles; by analyzing the distance between the motor vehicle and the platform, a safety reminder is issued to the traveling motor vehicle, so that the motor vehicle can avoid the flow of people in time, which is convenient for the bus to stop at the platform, thereby ensuring the safety of the motor vehicle and the bus. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] See also Figure 1 ,The present invention provides a technical solution: a city service optimization system based on swarm intelligence, including a data collection module, a departure time optimization module, a warning analysis module and a broadcast reminder module, the data collection module is used to obtain historical riding data, establish a historical database, obtain urban road network data, establish an urban road network database and an urban road network coordinate system;

[0039] The departure time optimization module is used to analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the platforms in the remaining routes of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining platforms based on historical riding data, combined with delay deviation and diversion deviation. Based on the predicted full load rate, the operating status of the target bus is evaluated and the departure time of the next bus is adjusted;

[0040] The early warning analysis module is used to analyze the passenger riding information, evaluate the passenger flow when the bus stops at the target station, determine whether to conduct early warning analysis, and if necessary, further evaluate the driving status of motor vehicles and non-motor vehicles near the target station to obtain early warning results;

[0041] The broadcast reminder module is used to control the bus broadcast, platform broadcast and motor vehicle on-board broadcast according to the analyzed warning results, and issue arrival reminders and safety reminders.

[0042] The departure time optimization module includes a bus operation status analysis module and a departure time adjustment module. The bus operation status analysis module is used to analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the stations in the remaining route of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining stations based on historical riding data, combined with delay deviation and diversion deviation. The departure time adjustment module evaluates the operating status of the target bus based on the predicted full load rate and adjusts the departure time of the next bus.

[0043] The bus operation status analysis module includes a delay deviation analysis submodule, a diversion deviation analysis submodule and a full load rate prediction submodule. The delay deviation analysis submodule is used to analyze the delay duration of the previous bus and evaluate the impact of the delay duration on the number of passengers getting on and off the bus. The diversion deviation analysis submodule is used to analyze the stations in the remaining route of the previous bus that overlap with other bus routes and evaluate the impact of passenger diversion on the full load rate. The full load rate prediction submodule is used to predict the full load rate of the target bus arriving at the remaining stations based on historical riding data, combined with delay deviation and diversion deviation.

[0044] The early warning analysis module includes a bus arrival analysis unit and a platform early warning analysis unit. The bus arrival analysis unit is used to analyze the distance between the bus and the platform, determine whether the bus stops at the platform, and send out a stop signal. The platform early warning analysis unit analyzes the passenger's riding information characteristics, obtains the passenger flow when the bus stops at the platform, determines whether to perform early warning analysis, and obtains the platform early warning results.

[0045] The early warning analysis module also includes a passenger early warning analysis unit and a motor vehicle early warning analysis unit. The passenger early warning analysis unit is used to analyze the driving status of non-motor vehicles in front of the target platform after receiving the early warning analysis signal, evaluate the safety index of passengers getting off the bus when it stops at the target platform, and obtain passenger early warning results. The motor vehicle early warning analysis unit is used to analyze the position characteristics of the motor vehicle and the target platform after receiving the early warning analysis signal, and obtain the motor vehicle early warning results.

[0046] The operation method of the urban service optimization system includes the following steps:

[0047] Step S1: Acquire historical riding data, establish a historical database, acquire urban road network data, establish an urban road network database and an urban road network coordinate system;

[0048] Step S2: Analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the stations in the remaining routes of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining stations based on historical riding data, combined with delay deviation and diversion deviation. According to the predicted full load rate, evaluate the operating status of the target bus and adjust the departure time of the next bus;

[0049] Step S3: Analyze the passenger riding information, evaluate the passenger flow when the bus stops at the target station, determine whether to perform early warning analysis, and if necessary, further evaluate the driving status of motor vehicles and non-motor vehicles near the target station to obtain early warning results;

[0050] Step S4: According to the analyzed warning results, the in-bus broadcast, platform broadcast and vehicle-mounted broadcast are controlled to issue a safety reminder.

[0051] Step S1 further comprises the following steps:

[0052] Step S11: Obtain historical riding data, including the number of passengers getting on and off each bus station at each time period for each bus route, establish a historical database and enter the data into the database;

[0053] Step S12: Obtain urban road network data, including urban road network diagram, road name, road length, road curvature, road slope and road intersection location, establish an urban road network database, enter the data into the database, establish an urban road network coordinate system, mark the target bus route, mark the turning point coordinates and the coordinates of each platform point of the target bus route, and obtain the bus location point coordinates, motor vehicle location point coordinates and non-motor vehicle location point coordinates in real time.

[0054] Step S2 further comprises the following steps:

[0055] Step S21: Obtain the total number of passengers N on the previous bus 总 and the maximum passenger capacity N max , calculate the real-time full load rate of the previous bus

[0056] Step S22: The last bus marks a starting timestamp when it leaves the starting station, and marks a station timestamp each time it arrives at a station during the driving process. The difference between the station timestamp and the starting timestamp is used as the actual running time T of the last bus arriving at the station. i , T i represents the actual running time of the bus from the starting station to the i-th station, and retrieves the running time threshold t of the bus from the starting station to the i-th station in the database i , if T i <t i , then the bus is delayed, calculate the delay time T 晚点 =T i -t i , according to the bus delay time T 晚点 Retrieve the corresponding boarding number deviation coefficient μ from the database i and the coefficient of deviation of the number of people getting off the bus v i , according to the historical riding data, the number of people N who boarded the original target bus at each station i and the number of people getting off the bus M i , according to the delay time deviation, the predicted number of boarding passengers of the target bus at each station is obtained. i N i and the predicted number of people getting off the bus v i M i The delay affects the number of people getting on each platform. The change in the number of people getting on leads to the update of the number of people getting off. By calculating the delay time of the bus, the deviation of the number of people getting on and off each platform is compensated, and the accuracy of predicting the number of people getting on and off each platform is improved;

[0057] Step S23: Obtain the stations in the remaining routes of the target bus that overlap with other bus routes. If the number of overlapping stations is N 重 ≥2, the bus route is marked as a diversion bus route, and the bus corresponding to the diversion bus route is a diversion bus. 重 Retrieve the corresponding dilution weight ε from the database j , get the number of diverted buses k at each station j , calculate the diversion influence coefficient In the formula, j = 1, 2…, n, j represents the number of overlapping stops between the diverted bus and the target bus, k j means there are k diversion buses with j overlapping stations, ε jThe dilution weight corresponding to the number of overlapping stations j is ε. By analyzing the stations that overlap with other bus routes, the degree of diversion of other buses to the target bus is evaluated. For example, if bus route A has two overlapping stations with the remaining route of the target bus, and a passenger's waiting station and destination station are the two overlapping stations, then he can also choose to take bus route A, which relieves the riding pressure of the target bus. Bus route A plays a diversion role. By analyzing the diversion effect, the accuracy of the predicted full load rate is improved;

[0058] Step S24: Based on the predicted number of people μ on board i N i and the predicted number of people getting off the bus v i M i and the diversion influence coefficient ξ, predicting the full load rate of the previous bus arriving at the remaining platforms on the target route Where i = 1, 2, ..., n, N 总 is the total number of passengers in the bus, and compares the predicted full load rate P of each platform during the period i and full load rate threshold θ, if P i >θ, the previous bus has an abnormal operation state, and the next bus is dispatched immediately to coordinate the bus operation state of the target bus route. If P j ≤θ, the previous bus is in normal operation, and the next bus will depart according to the original cycle. By analyzing the operating status of the previous bus, when an abnormal operating status occurs, the next bus will receive the instruction immediately and depart in advance to relieve the operating pressure of the previous bus. For example, if the target bus is delayed for half an hour, the number of waiting passengers at the platform behind increases by 15 people within half an hour. The target bus cannot carry all the waiting passengers, and the next bus needs to depart earlier to relieve the operating pressure, improve the travel efficiency of passengers, reduce the long waiting time of passengers due to large passenger flow, optimize the allocation of public transportation resources, and effectively improve the operating efficiency of buses.

[0059] Step S3 further comprises the following steps:

[0060] Step S31: Obtain the coordinates of the bus location point in the urban road network coordinate system, traverse the target bus route according to the bus driving direction, stop when traversing to the platform point, and use it as the target platform point, where the bus location point, turning point and target platform point are used as m+1 division nodes to divide the target route into m road segments, and obtain the location coordinates of m+1 division nodes according to the traversal order (a i , b i );

[0061] Step S32: Obtain the road curvature Q of the road segments between m+1 partition nodes1_2 , Q 2_3 , ..., Q m_m+1 , where Q m_m+1 Refers to the road curvature of the road segment between the mth division node and the m+1th division node. The corresponding curvature influence coefficient α in the database is retrieved according to the road curvature. 1_2 , α 2_3 , ..., α m_m+1 , get the road slope P of the road segment between m+1 divided nodes 1_2 , P 2_3 , ..., P m_m+1 , where P m_m+1 Refers to the road slope of the road section between the mth division node and the m+1th division node, and retrieves the corresponding slope influence coefficient β from the database 1_2 , β 2_3 , ..., β m_m+1 ;

[0062] Step S33: Calculate the distance between the bus and the target platform based on the position coordinates of each division node and the road curvature and road slope of the road segment between each division node. Where, k = 1, 2, ..., m. If the distance L between the bus and the target platform is less than or equal to the threshold, it is judged that the bus is about to stop at the target platform, and a bus arrival signal is issued.

[0063] Step S33 further includes the following steps:

[0064] Step S331: After receiving the signal that the bus is about to arrive at the station, obtain the passenger information in the database and count the number of passengers M who get off the target bus at the target station. 下 and the number of people on board M 上 , calculate the passenger flow of the target platform K = M 下 +M 上 If the passenger flow K is 0, no early warning analysis is performed. If the passenger flow K is greater than 0, it is determined that early warning analysis is needed, and an early warning analysis signal is issued, and the target platform broadcast is controlled to provide safety reminders to the waiting passengers at the target platform and the non-motor vehicles in front of the target platform;

[0065] Step S332: After receiving the warning analysis signal, further obtain the target station location coordinates (a1, b1), obtain the location coordinates (a2, b2) of all non-motor vehicles on the road section where the target bus is about to arrive at the station, obtain the road curvature Q1 and road slope P1 of the road section where the station is located, retrieve the corresponding curvature influence coefficient α1 and slope influence coefficient β1, and calculate the distance between each non-motor vehicle and the target station. Where (a1, b1) represents the location coordinates of the platform, (a2, b2) represents the location coordinates of the non-motor vehicle, and the travel speed V of each non-motor vehicle is identified. 非 , according to the driving speed V 非 Retrieve the corresponding non-motor vehicle warning distance threshold between each non-motor vehicle and the target station in the database Comparison of L1 and non-motor vehicle warning distance thresholds If there is a non-motor vehicle in the traffic flow Then control the broadcasting in the bus to issue safety reminders to passengers;

[0066] Step S333: After receiving the warning analysis signal, further obtain the position coordinates (a3, b3) of all motor vehicles on the road section where the bus is about to arrive, obtain the position coordinates (a1, b1) of the target platform, obtain the road curvature Q1 and road slope P1 of the road section where the platform is located, retrieve the corresponding curvature influence coefficient α1 and slope influence coefficient β1, and calculate the distance between each motor vehicle and the target platform Wherein, (a1, b1) represents the location coordinates of the platform, (a3, b3) represents the location coordinates of the motor vehicle, and L2 is compared with the motor vehicle warning distance threshold φ. If L2<φ, the motor vehicle is marked as within the warning reminder range, and the motor vehicle on-board broadcast is controlled to give safety reminders to the motor vehicle driver. Through the safety reminders of the bus broadcast, platform broadcast and on-board broadcast, non-motor vehicle and motor vehicle drivers are informed that there are buses stopping at nearby platforms and whether there are people getting on or off the bus, and they adjust the driving speed in time to avoid the bus, which improves the travel efficiency and safety of motor vehicles. Passengers are aware of the status of non-motor vehicle traffic in front of the platform in advance, and pay attention to looking left and right when getting off the bus, which ensures the safety of passengers getting off the bus.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0068] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An urban service optimization system based on swarm intelligence, characterized by: It includes a data collection module, a departure time optimization module, a warning analysis module and a broadcast reminder module. The data collection module is used to obtain historical riding data, establish a historical database, obtain urban road network data, establish an urban road network database and an urban road network coordinate system; The departure time optimization module is used to analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the platforms in the remaining routes of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining platforms based on historical riding data, combined with delay deviation and diversion deviation. According to the predicted full load rate, the operating status of the target bus is evaluated and the departure time of the next bus is adjusted; The warning analysis module is used to analyze the passenger riding information, evaluate the passenger flow when the bus stops at the target station, determine whether to perform a warning analysis, and if necessary, further evaluate the driving status of motor vehicles and non-motor vehicles near the target station to obtain a warning result; The broadcast reminder module is used to control the bus broadcast, platform broadcast and motor vehicle broadcast according to the analyzed warning results, and issue arrival reminders and safety reminders.

2. The urban service optimization system based on swarm intelligence according to claim 1 is characterized by: The departure time optimization module includes a bus operation status analysis module and a departure time adjustment module. The bus operation status analysis module is used to analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the platforms in the remaining route of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining platforms based on historical riding data combined with delay deviation and diversion deviation. The departure time adjustment module evaluates the operation status of the target bus based on the predicted full load rate and adjusts the departure time of the next bus.

3. The urban service optimization system based on swarm intelligence according to claim 2 is characterized by: The bus operation status analysis module includes a delay deviation analysis submodule, a diversion deviation analysis submodule and a full load rate prediction submodule. The delay deviation analysis submodule is used to analyze the delay duration of the previous bus and evaluate the impact of the delay duration on the number of passengers getting on and off the bus. The diversion deviation analysis submodule is used to analyze the platforms in the remaining route of the previous bus that overlap with other bus routes and evaluate the impact of passenger diversion on the full load rate. The full load rate prediction submodule is used to predict the full load rate of the target bus arriving at the remaining platforms based on historical riding data, combined with delay deviation and diversion deviation.

4. The urban service optimization system based on swarm intelligence according to claim 3 is characterized by: The early warning analysis module includes a bus arrival analysis unit and a platform early warning analysis unit. The bus arrival analysis unit is used to analyze the distance between the bus and the platform, determine whether the bus stops at the platform, and send a stop signal. The platform early warning analysis unit analyzes the passenger's riding information characteristics, obtains the passenger flow when the bus stops at the platform, determines whether to perform early warning analysis, and obtains the platform early warning result.

5. The urban service optimization system based on swarm intelligence according to claim 4 is characterized by: The warning analysis module also includes a passenger warning analysis unit and a motor vehicle warning analysis unit. The passenger warning analysis unit is used to analyze the driving status of non-motor vehicles in front of the target platform after receiving the warning analysis signal, evaluate the safety index of passengers getting off the bus when it stops at the target platform, and obtain passenger warning results. The motor vehicle warning analysis unit is used to analyze the position characteristics of the motor vehicle and the target platform after receiving the warning analysis signal, and obtain the motor vehicle warning result.

6. The urban service optimization system based on swarm intelligence according to claim 5 is characterized by: The operation method of the city service optimization system comprises the following steps: Step S1: Acquire historical riding data, establish a historical database, acquire urban road network data, establish an urban road network database and an urban road network coordinate system; Step S2: Analyze the delay time of the previous bus, evaluate the impact of the delay time on the number of passengers getting on and off the bus, analyze the stations in the remaining routes of the previous bus that overlap with other bus routes, evaluate the impact of passenger diversion on the full load rate, and predict the full load rate of the target bus arriving at the remaining stations based on historical riding data, combined with delay deviation and diversion deviation. According to the predicted full load rate, evaluate the operating status of the target bus and adjust the departure time of the next bus; Step S3: Analyze the passenger riding information, evaluate the passenger flow when the bus stops at the target station, determine whether to perform early warning analysis, and if necessary, further evaluate the driving status of motor vehicles and non-motor vehicles near the target station to obtain early warning results; Step S4: According to the analyzed warning results, the in-bus broadcast, platform broadcast and vehicle-mounted broadcast are controlled to issue a safety reminder.

7. The urban service optimization system based on swarm intelligence according to claim 6 is characterized by: The step S1 further comprises the following steps: Step S11: Obtain historical riding data, including the number of passengers getting on and off each bus station at each time period for each bus route, establish a historical database and enter the data into the database; Step S12: Obtain urban road network data, including urban road network diagram, road name, road length, road curvature, road slope and road intersection location, establish an urban road network database, enter the data into the database, establish an urban road network coordinate system, mark the target bus route, mark the turning point coordinates and the coordinates of each platform point of the target bus route, and obtain the bus location point coordinates, motor vehicle location point coordinates and non-motor vehicle location point coordinates in real time.

8. The urban service optimization system based on swarm intelligence according to claim 7 is characterized by: The step S2 further comprises the following steps: Step S21: Obtain the total number of passengers N on the previous bus 总 and the maximum passenger capacity N max , calculate the real-time full load rate of the previous bus Step S22: The last bus marks a starting timestamp when it leaves the starting station, and marks a station timestamp each time it arrives at a station during the driving process. The difference between the station timestamp and the starting timestamp is used as the actual running time T of the last bus arriving at the station. i , T i represents the actual running time of the bus from the starting station to the i-th station, and retrieves the running time threshold t of the bus from the starting station to the i-th station in the database i , if T i <t i , then the bus is delayed, calculate the delay time T 晚点 =T i -t i , according to the bus delay time T 晚点 Retrieve the corresponding boarding number deviation coefficient μ from the database i and the coefficient of deviation of the number of people getting off the bus v i , according to the historical riding data, the number of people N who boarded the original target bus at each station i and the number of people getting off the bus M i , according to the delay time deviation, the predicted number of boarding passengers of the target bus at each station is obtained. i N i and the predicted number of people getting off the bus ν i M i ; Step S23: Obtain the stations in the remaining routes of the target bus that overlap with other bus routes. If the number of overlapping stations N 重 ≥2, the bus route is marked as a diversion bus route, and the bus corresponding to the diversion bus route is a diversion bus. 重 Retrieve the corresponding dilution weight ε from the database j , get the number of diverted buses k at each station j , calculate the diversion influence coefficient In the formula, j = 1, 2…, n, j represents the number of overlapping stops between the diverted bus and the target bus, k j means there are k diversion buses with j overlapping stations, ε j The dilution weight corresponding to the number of overlapping stations j is ε; Step S24: Based on the predicted number of people μ on board i N i and the predicted number of people getting off the bus ν i M i and the diversion influence coefficient ξ, predicting the full load rate of the previous bus arriving at the remaining platforms on the target route Where i = 1, 2, ..., n, N 总 is the total number of passengers in the bus, and compares the predicted full load rate P of each platform during the period i and full load rate threshold θ, if P i >θ, the previous bus has an abnormal operation state, and the next bus is dispatched immediately to coordinate the bus operation state of the target bus route. If P i ≤θ, the previous bus is in normal operation and the next bus will depart according to the original cycle.

9. The urban service optimization system based on swarm intelligence according to claim 8 is characterized by: The step S3 further comprises the following steps: Step S31: Obtain the coordinates of the bus position point in the urban road network coordinate system, traverse the target bus route according to the bus driving direction, stop when traversing to the platform point, and use it as the target platform point, where the bus position point, turning point and target platform point are used as m+1 division nodes to divide the target route into m road segments, and obtain the position coordinates of m+1 division nodes according to the traversal order (a i , b i ); Step S32: Obtain the road curvature Q of the road segments between m+1 partition nodes 1_2 , Q 2_3 , ..., Q m_m+1 , where Q m_m+1 Refers to the road curvature of the road segment between the mth division node and the m+1th division node. The corresponding curvature influence coefficient α in the database is retrieved according to the road curvature. 1_2 , α 2_3 , ..., α m_m+1 , get the road slope P of the road segment between m+1 divided nodes 1_2 , P 2_3 , ..., P m_m+1 , where P m_m+1 Refers to the road slope of the road section between the mth division node and the m+1th division node, and retrieves the corresponding slope influence coefficient β from the database 1_2 , β 2_3 , ..., β m_m+1 ; Step S33: Calculate the distance between the bus and the target platform based on the position coordinates of each division node and the road curvature and road slope of the road segment between each division node. Where k = 1, 2, …, m. If the distance L between the bus and the target platform is less than or equal to the threshold, it is judged that the bus is about to stop at the target platform, and a bus arrival signal is issued.

10. The urban service optimization system based on swarm intelligence according to claim 9 is characterized by: The step S33 further comprises the following steps: Step S331: After receiving the signal that the bus is about to arrive at the station, obtain the passenger information in the database and count the number of passengers M who get off the target bus at the target station. 下 and the number of people on board M 上 , calculate the passenger flow of the target platform K = M 下 +M 上 If the passenger flow K is 0, no early warning analysis is performed. If the passenger flow K is greater than 0, it is determined that early warning analysis is needed, and an early warning analysis signal is issued, and the target platform broadcast is controlled to provide safety reminders to the waiting passengers at the target platform and the non-motor vehicles in front of the target platform; Step S332: After receiving the warning analysis signal, further obtain the target station location coordinates (a1, b1), obtain the location coordinates (a2, b2) of all non-motor vehicles on the road section where the target bus is about to arrive at the station, obtain the road curvature Q1 and road slope P1 of the road section where the station is located, retrieve the corresponding curvature influence coefficient α1 and slope influence coefficient β1, and calculate the distance between each non-motor vehicle and the target station. Where (a1, b1) represents the location coordinates of the platform, (a2, b2) represents the location coordinates of the non-motor vehicle, and the travel speed V of each non-motor vehicle is identified. 非 , according to the driving speed V 非 Retrieve the corresponding non-motor vehicle warning distance threshold between each non-motor vehicle and the target station in the database Comparison of L1 and non-motor vehicle warning distance thresholds If there is a non-motor vehicle in the traffic flow Then control the broadcasting in the bus to issue safety reminders to passengers; Step S333: After receiving the warning analysis signal, further obtain the position coordinates (a3, b3) of all motor vehicles on the road section where the bus is about to arrive, obtain the position coordinates (a1, b1) of the target platform, obtain the road curvature Q1 and road slope P1 of the road section where the platform is located, retrieve the corresponding curvature influence coefficient α1 and slope influence coefficient β1, and calculate the distance between each motor vehicle and the target platform Where (a1, b1) represents the location coordinates of the station, (a3, b3) represents the location coordinates of the motor vehicle, and L2 is compared with the motor vehicle warning distance threshold φ. If L2 < φ, the motor vehicle is marked as within the warning reminder range, and the motor vehicle on-board broadcast is controlled to give a safety reminder to the motor vehicle driver.

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