Public transport comprehensive regulation and control system for smart city

Abstract

The invention belongs to the technical field of smart city traffic, and provides a public traffic comprehensive regulation and control system for a smart city, which processes three data including route lane data, route station passenger flow and route signal lamp data to obtain a static characterization value of bus driving data, and obtains a public traffic comprehensive regulation and control result based on the static characterization value of the bus driving data. According to the method, the shift number and the route direction of a bus can be formulated for a bus company, a road congestion degree representative value in a monitored area is obtained according to a traffic flow representative value, the road congestion degree can be judged for a traffic police department, police strength is reasonably distributed, and timing of traffic signal lamps is adjusted in time; the static characterization value of the bus driving data and the dynamic characterization value of the bus driving data are combined, so that reasons and rules for finding road congestion can be provided for traffic police departments.

Description

Technical Field

[0001] The present invention belongs to the field of smart city transportation technology, and specifically relates to a comprehensive public transportation control system for smart cities. Background Art

[0002] With the rapid growth of domestic car ownership, the traditional single traffic control method can no longer meet the current needs of urban traffic. Therefore, it is necessary to manage by predicting road conditions, identifying and adjusting the degree of congestion, and promoting the development efficiency of the entire urban transportation system.

[0003] A Chinese patent application with publication number CN117371596A discloses a comprehensive public transportation control system for smart cities based on multi-source data. It obtains public transportation data through the city's public transportation network and the event data of the day, so that the passenger flow index, peak duration index and event impact index can be calculated in real time to adjust the scheduling of public transportation vehicles. Finally, a control prediction model is established based on historical data to predict whether to adjust the schedule.

[0004] In the prior art, the travel experience of passengers is improved only by adjusting the schedule of public transportation vehicles and predicting whether to adjust the schedule. However, there is no detailed analysis of the schedule adjustment of public transportation vehicles, so that passengers can choose to take other means of transportation and plan their travel time. There is also no analysis and determination of the degree of traffic congestion in the area, so as to help the traffic police department reasonably allocate police force and adjust the timing of traffic lights in time, and provide the traffic police department with the causes and laws of road congestion. For this reason, the present invention provides a comprehensive public transportation control system for smart cities. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a data collection management module is used to collect public transportation data, which includes a static data collection module and a dynamic data real-time collection module;

[0007] The static data collection module collects the historical route information of the bus, including the route lane data, route platform passenger flow and route signal light data;

[0008] Based on the route lane data, route platform passenger flow and route signal light data, the static characterization value of bus driving data is obtained. The specific process is as follows:

[0009] A1, based on the historical driving lane data of the bus, obtain the representative value of the lane data of the bus route;

[0010] A2, based on the historical passenger flow data of the bus station, obtain the representative value of the passenger flow of the route station;

[0011] A3, based on the signal frequency of the bus passing through the route, obtain the representative value of the route signal light data;

[0012] A4, based on the representative value of bus route lane data, the representative value of route platform passenger flow and the representative value of route signal light data, a static representation value of bus driving data is obtained by processing;

[0013] The static representation value E of bus driving data is obtained by the formula: E=L×a1+K×a2+J×a3, where L represents the representative value of bus lane data, K represents the representative value of route platform passenger flow, and J represents the representative value of route signal light data. The distribution of a1, a2, and a3 represents the preset weight coefficients.

[0014] The data dynamic collection module monitors the real-time bus route information, including the degree of road congestion;

[0015] The data processing and analysis module processes the collected public transportation data and analyzes the road traffic status;

[0016] The intelligent adjustment module adjusts and optimizes bus routes according to road traffic conditions.

[0017] The present invention further illustrates that in A1, processing is performed according to the historical lane utilization rate of the bus, and the processing process is as follows:

[0018] A101, obtaining the time for different types of buses to pass through the lane within the cycle, summing the time for different types of buses to pass through the lane within the cycle, and obtaining the lane passing time within the cycle;

[0019] A102, obtaining the time during which the bus does not pass through the lane and the time during which the lane does not pass through the bus, and obtaining the lane idle time during the cycle;

[0020] A103, lane utilization rate = lane travel time within a cycle / (lane idle time within a cycle + lane travel time within a cycle), the lane utilization rate is expressed as a representative value of the bus route lane data.

[0021] The present invention further illustrates that in A2, processing is performed according to the flow of people at the bus station in different time periods, and the processing process is as follows:

[0022] A201, obtain the number of people on the platform in different time periods, sum and average the number of people on the platform in different time periods, and use the average value as the representative value of the passenger flow on the platform of the route every day in the period;

[0023] A202, calculate the standard deviation of the representative value of the passenger flow of the route platform every day in the period, and use the standard deviation as the representative value of the passenger flow of the route platform.

[0024] The present invention further explains: In A3, according to the waiting time data and the passing time data of the bus passing the route signal every day, the processing process is as follows:

[0025] A301, record the waiting time of the bus at the traffic light every day, sum up the waiting time of the bus at the traffic light every day and take the average value, and use the average value as the representation value of the waiting time of the bus at the traffic light every day;

[0026] A302, record the time it takes for a bus to pass through a traffic light every day, and sum up the time it takes for a bus to pass through a traffic light every day to obtain an average value, and use the average value as a representation value of the time it takes for a bus to pass through a traffic light every day;

[0027] A303, based on the sum of the time representation value of the bus waiting for the signal light every day and the time representation value of the bus passing the signal light every day, obtain the frequency of the bus passing the route signal every day;

[0028] A304, sums up the frequency of buses passing through the signal every day and takes the square difference, and uses the square difference as the representative value of the route signal light data.

[0029] The present invention further illustrates that: based on the traffic flow data, a representative value of the road congestion degree is obtained by processing, and the specific process is as follows:

[0030] B1, based on the traffic flow on the bus route, obtain the traffic flow representative value;

[0031] B2, based on the representative value of traffic flow, obtain the representative value of road congestion degree in the monitoring area;

[0032] B3, using the representative value of road congestion degree as the dynamic representation value of bus driving data.

[0033] The present invention further illustrates that in B1, the bus route is divided into several areas, and processing is performed based on the number of lanes occupied by vehicles in each area. The processing process is as follows:

[0034] B101, count the lane data in each area to obtain the total number of lanes in each area;

[0035] B102, counting the number of vehicles in each lane at different time periods in each area, calculating the square difference based on the vehicle data in the lanes at different time periods in the area, and using the square difference as a representative value of the vehicle data in the lanes at different time periods in each area;

[0036] B103, sum up and average the representative values ​​of vehicle data in lanes at different time periods in each area, and use the average value as the representative value of traffic flow.

[0037] The present invention further illustrates that in B2, processing is performed according to the traffic flow representative value and the lane area in the monitoring area, and the processing process is as follows:

[0038] B201, obtaining the lane area in the monitoring area, dividing the monitoring area into several areas, summing the lane area in each area, and obtaining a representative value of the total area of ​​the lanes in the monitoring area;

[0039] B202, the traffic flow representative value is ratioed with the total area representative value of the lanes in the monitoring area to obtain the representative value of the road congestion level in the monitoring area.

[0040] The present invention further illustrates that: based on the static characterization value of bus driving data and the dynamic characterization value of bus driving data, a road traffic peak period determination value is obtained, and the processing process is as follows:

[0041] C1, taking the static characterization value of bus driving data as the independent variable and the dynamic characterization value of bus driving data as the dependent variable, a multinomial regression equation is obtained;

[0042] Multinomial regression equation: Y = ax 2 +bx+c, where Y represents the dynamic representation value of bus driving data, X represents the static representation value of bus driving data, and a, b, and c represent constants respectively;

[0043] C2, based on the multinomial regression equation, the derivative is obtained as the slope of the multinomial regression equation, and the slope is used as the judgment value of the road traffic peak period;

[0044] C3, compares the real-time monitoring data with the road traffic peak period judgment value, and the comparison process is as follows:

[0045] If the real-time monitored data is greater than or equal to the road traffic peak period determination value, it is determined that the road traffic is at peak congestion at this time;

[0046] If the real-time monitoring data is less than the road traffic peak period judgment value, it is determined that the road traffic is not in peak congestion at this time.

[0047] The present invention further describes: based on whether the bus needs to be dynamically adjusted when the road traffic is in a peak period, the specific process is as follows:

[0048] D1, obtain the representative value of the normal driving time of the bus and the representative value of the congestion duration, and get the bus delay time;

[0049] D2, based on the comparison between the bus delay time and the preset bus delay time threshold, the comparison process is as follows:

[0050] If the bus delay time is greater than or equal to the preset bus delay time threshold, it is determined that the bus needs to be dynamically adjusted;

[0051] If the bus delay time is less than the preset bus delay threshold, it is determined that there is no need to dynamically adjust the bus.

[0052] The present invention further states that: in D1, processing is performed based on the total distance representative value of the congested road route to obtain the congestion duration;

[0053] D101, dividing the total distance of the congested road route into a number of line segments, summing up the bus speeds in each line segment and taking the average value, taking the average value as the bus speed on the congested route, and dividing the total distance representative value of the congested road route by the bus speed on the congested route to obtain the congestion duration representative value;

[0054] D102, summing up the normal running time of the bus each time and taking the average value, and taking the average value as the representative value of the normal running time;

[0055] D103, bus delay time = congestion duration representative value - normal travel time representative value.

[0056] The beneficial effects of the present invention are as follows:

[0057] 1. The present invention obtains a static characterization value of bus driving data by processing three data items: route lane data, route platform passenger flow, and route signal light data. Based on the static characterization value of bus driving data, bus schedules and route directions can be formulated for bus companies. By obtaining a representative value of road congestion in the monitoring area based on the representative value of traffic flow, the traffic police department can judge the road congestion degree, reasonably allocate police force, and timely adjust the timing of traffic lights. Combining the static characterization value of bus driving data with the dynamic characterization value of bus driving data can provide the traffic police department with the cause and law of road congestion.

[0058] 2. The present invention obtains the bus delay time by acquiring the representative value of the normal driving time of the bus and the representative value of the congestion duration. By providing the bus delay time, passengers can choose to take other means of transportation. In addition, according to the bus delay time, the bus company can timely adjust the bus schedule interval and re-plan new bus routes. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention will be further described below in conjunction with the accompanying drawings.

[0060] Figure 1 is a flow chart of Embodiment 1 of the present invention;

[0061] Figure 2 is a flow chart of Embodiment 2 of the present invention;

[0062] Figure 3 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0063] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0064] Example 1

[0065] like Figure 1 and Figure 3 As shown, the integrated public transportation control system for smart cities according to the embodiment of the present invention includes:

[0066] The data collection management module is used to collect public transportation data, including a static data collection module and a dynamic real-time data collection module;

[0067] The static data collection module collects the historical route information of the bus, including the route lane data, route platform passenger flow and route signal light data;

[0068] Based on the route lane data, route platform passenger flow and route signal light data, the static characterization value of bus driving data is obtained. The specific process is as follows:

[0069] A1, based on the historical driving lane data of the bus, obtain the representative value of the lane data of the bus route;

[0070] In A1, processing is performed based on the historical lane utilization rate of buses. The processing process is as follows:

[0071] A101, obtaining the time for different types of buses to pass through the lane within the cycle, summing the time for different types of buses to pass through the lane within the cycle, and obtaining the lane passing time within the cycle;

[0072] A102, obtaining the time during which the bus does not pass through the lane and the time during which the lane does not pass through the bus, and obtaining the lane idle time during the cycle;

[0073] A103, lane utilization rate = lane travel time within a cycle / (lane idle time within a cycle + lane travel time within a cycle), the lane utilization rate is expressed as a representative value of the bus route lane data;

[0074] A2, based on the historical passenger flow data of the bus station, obtain the representative value of the passenger flow of the route station;

[0075] In A2, processing is performed according to the passenger flow at the bus station in different time periods. The processing process is as follows:

[0076] A201, obtain the number of people on the platform in different time periods, sum and average the number of people on the platform in different time periods, and use the average value as the representative value of the passenger flow on the platform of the route every day in the period;

[0077] A202, calculate the standard deviation of the representative value of the passenger flow of the route platform every day in the period, and use the standard deviation as the representative value of the passenger flow of the route platform;

[0078] A3, based on the signal frequency of the bus passing through the route, obtain the representative value of the route signal light data;

[0079] In A3, the processing is done based on the waiting time data and the travel time data of the bus passing the route signal every day. The processing process is as follows:

[0080] A301, record the waiting time of the bus at the traffic light every day, sum up the waiting time of the bus at the traffic light every day and take the average value, and use the average value as the representation value of the waiting time of the bus at the traffic light every day;

[0081] A302, record the time it takes for a bus to pass through a traffic light every day, and sum up the time it takes for a bus to pass through a traffic light every day to obtain an average value, and use the average value as a representation value of the time it takes for a bus to pass through a traffic light every day;

[0082] A303, based on the sum of the time representation value of the bus waiting for the signal light every day and the time representation value of the bus passing the signal light every day, obtain the frequency of the bus passing the route signal every day;

[0083] A304, sum the frequency of buses passing through the signal every day and take the square difference, and use the square difference as the representative value of the route signal light data;

[0084] A4, based on the representative value of bus route lane data, the representative value of route platform passenger flow and the representative value of route signal light data, a static representation value of bus driving data is obtained by processing;

[0085] The static representation value E of bus driving data is obtained by the formula: E=L×a1+K×a2+J×a3, where L represents the representative value of bus lane data, K represents the representative value of route platform passenger flow, and J represents the representative value of route signal light data. The distribution of a1, a2, and a3 represents the preset weight coefficients.

[0086] The data dynamic collection module monitors the real-time bus route information, including the degree of road congestion;

[0087] Based on the traffic flow data, the representative value of road congestion is obtained. The specific process is as follows:

[0088] B1, based on the traffic flow on the bus route, obtain the traffic flow representative value;

[0089] In B1, the bus route is divided into several areas, and the processing is performed based on the number of lanes occupied by vehicles in each area. The processing process is as follows:

[0090] B101, count the lane data in each area to obtain the total number of lanes in each area;

[0091] B102, counting the number of vehicles in each lane at different time periods in each area, calculating the square difference based on the vehicle data in the lanes at different time periods in the area, and using the square difference as a representative value of the vehicle data in the lanes at different time periods in each area;

[0092] B103, sum up and average the representative values ​​of vehicle data of lanes in each area at different time periods, and use the average value as the representative value of traffic flow;

[0093] B2, based on the representative value of traffic flow, obtain the representative value of road congestion degree in the monitoring area;

[0094] In B2, processing is performed based on the traffic flow representative value and the lane area in the monitoring area. The processing process is as follows:

[0095] B201, obtaining the lane area in the monitoring area, dividing the monitoring area into several areas, summing the lane area in each area, and obtaining a representative value of the total area of ​​the lanes in the monitoring area;

[0096] B202, the traffic flow representative value is processed by ratio processing with the total area representative value of the lanes in the monitoring area to obtain the road congestion degree representative value in the monitoring area;

[0097] B3, using the road congestion degree representative value as the dynamic representation value of bus driving data;

[0098] The data processing and analysis module processes the collected public transportation data and analyzes the road traffic status;

[0099] Based on the static characterization value and the dynamic characterization value of the bus driving data, the road traffic peak period determination value is obtained. The processing process is as follows:

[0100] C1, taking the static characterization value of bus driving data as the independent variable and the dynamic characterization value of bus driving data as the dependent variable, a multinomial regression equation is obtained;

[0101] Multinomial regression equation: Y = ax 2 +bx+c, where Y represents the dynamic representation value of bus driving data, X represents the static representation value of bus driving data, and a, b, and c represent constants respectively;

[0102] C2, based on the multinomial regression equation, the derivative is obtained as the slope of the multinomial regression equation, and the slope is used as the judgment value of the road traffic peak period;

[0103] C3, compares the real-time monitoring data with the road traffic peak period judgment value, and the comparison process is as follows:

[0104] If the real-time monitored data is greater than or equal to the road traffic peak period determination value, it is determined that the road traffic is at peak congestion at this time;

[0105] If the real-time monitoring data is less than the road traffic peak period judgment value, it is determined that the road traffic is not in peak congestion at this time;

[0106] A data storage module stores the collected and monitored data for analysis of static collected data;

[0107] Specific scheme of this embodiment: by processing three data items, namely, route lane data, route platform passenger flow and route signal light data, a static characterization value of bus driving data is obtained. Based on the static characterization value of bus driving data, bus schedules and route directions can be formulated for bus companies. By obtaining a representative value of road congestion in the monitoring area based on the representative value of traffic flow, the traffic police department can judge the road congestion degree, reasonably allocate police force and adjust the timing of traffic lights in a timely manner. Combining the static characterization value of bus driving data with the dynamic characterization value of bus driving data can provide the traffic police department with the cause and law of road congestion.

[0108] Example 2

[0109] Intelligent adjustment module adjusts and optimizes bus routes according to road traffic conditions;

[0110] like Figure 2 As shown, when the road traffic is in the peak period, whether it is necessary to dynamically adjust the buses, the specific process is as follows:

[0111] D1, obtain the representative value of the normal driving time of the bus and the representative value of the congestion duration, and get the bus delay time;

[0112] In D1, the congestion duration is obtained based on the total distance representative value of the congested road route;

[0113] D101, dividing the total distance of the congested road route into a number of line segments, summing up the bus speeds in each line segment and taking the average value, taking the average value as the bus speed on the congested route, and dividing the total distance representative value of the congested road route by the bus speed on the congested route to obtain the congestion duration representative value;

[0114] D102, summing up the normal running time of the bus each time and taking the average value, and taking the average value as the representative value of the normal running time;

[0115] D103, bus delay time = congestion duration representative value - normal travel time representative value;

[0116] D2, based on the comparison between the bus delay time and the preset bus delay time threshold, the comparison process is as follows:

[0117] If the bus delay time is greater than or equal to the preset bus delay time threshold, it is determined that the bus needs to be dynamically adjusted;

[0118] If the bus delay time is less than the preset bus delay threshold, it is determined that there is no need to dynamically adjust the bus.

[0119] The technical solution of this embodiment is as follows: by obtaining the representative value of the normal travel time of the bus and the representative value of the congestion duration, the bus delay time is obtained. By providing the bus delay time, passengers can choose to take other means of transportation. In addition, according to the bus delay time, the bus company can timely adjust the bus interval time and re-plan new bus routes.

[0120] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The integrated control system for public transportation in smart cities is characterized by: include: The data collection management module is used to collect public transportation data, including a static data collection module and a dynamic data real-time collection module; The static data collection module collects the historical route information of the bus, including the route lane data, route platform passenger flow and route signal light data; Based on the route lane data, route platform passenger flow and route signal light data, the static characterization value of bus driving data is obtained. The specific process is as follows: A1, based on the historical driving lane data of the bus, obtain the representative value of the lane data of the bus route; A2, based on the historical passenger flow data of the bus station, obtain the representative value of the passenger flow of the route station; A3, based on the signal frequency of the bus passing through the route, obtain the representative value of the route signal light data; A4, based on the representative value of bus route lane data, the representative value of route platform passenger flow and the representative value of route signal light data, a static representation value of bus driving data is obtained by processing; The static representation value E of bus driving data is obtained by the formula: E=L×a1+K×a2+J×a3, where L represents the representative value of bus lane data, K represents the representative value of route platform passenger flow, and J represents the representative value of route signal light data. The distribution of a1, a2, and a3 represents the preset weight coefficients. The data dynamic collection module monitors the real-time bus route information, including the degree of road congestion; The data processing and analysis module processes the collected public transportation data and analyzes the road traffic status; The intelligent adjustment module adjusts and optimizes bus routes according to road traffic conditions.

2. The integrated public transportation control system for smart cities according to claim 1 is characterized in that: In A1, processing is performed based on the historical lane utilization rate of buses. The processing process is as follows: A101, obtaining the time for different types of buses to pass through the lane within the cycle, summing the time for different types of buses to pass through the lane within the cycle, and obtaining the lane passing time within the cycle; A102, obtaining the time during which the bus does not pass through the lane and the time during which the lane does not pass through the bus, and obtaining the lane idle time during the cycle; A103, lane utilization rate = lane travel time within a cycle / (lane idle time within a cycle + lane travel time within a cycle), the lane utilization rate is expressed as a representative value of the bus route lane data.

3. The integrated public transportation control system for smart cities according to claim 1 is characterized in that: In A2, processing is performed according to the passenger flow at the bus station in different time periods. The processing process is as follows: A201, obtain the number of people on the platform in different time periods, sum and average the number of people on the platform in different time periods, and use the average value as the representative value of the passenger flow on the platform of the route every day in the period; A202, calculate the standard deviation of the representative value of the passenger flow of the route platform every day in the period, and use the standard deviation as the representative value of the passenger flow of the route platform.

4. The integrated public transportation control system for smart cities according to claim 1 is characterized in that: In A3, the processing is done based on the waiting time data and the travel time data of the bus passing the route signal every day. The processing process is as follows: A301, record the waiting time of the bus at the traffic light every day, sum up the waiting time of the bus at the traffic light every day and take the average value, and use the average value as the representation value of the waiting time of the bus at the traffic light every day; A302, record the time it takes for a bus to pass through a traffic light every day, and sum up the time it takes for a bus to pass through a traffic light every day to obtain an average value, and use the average value as a representation value of the time it takes for a bus to pass through a traffic light every day; A303, based on the sum of the time representation value of the bus waiting for the signal light every day and the time representation value of the bus passing the signal light every day, obtain the frequency of the bus passing the route signal every day; A304, sums up the frequency of buses passing through the signal every day and takes the square difference, and uses the square difference as the representative value of the route signal light data.

5. The integrated public transportation control system for smart cities according to claim 1 is characterized in that: Based on the traffic flow data, the representative value of road congestion is obtained. The specific process is as follows: B1, based on the traffic flow on the bus route, obtain the traffic flow representative value; B2, based on the representative value of traffic flow, obtain the representative value of road congestion degree in the monitoring area; B3, using the representative value of road congestion degree as the dynamic representation value of bus driving data.

6. The integrated public transportation control system for smart cities according to claim 5 is characterized in that: In B1, the bus route is divided into several areas, and the processing is performed based on the number of lanes occupied by vehicles in each area. The processing process is as follows: B101, count the lane data in each area to obtain the total number of lanes in each area; B102, counting the number of vehicles in each lane at different time periods in each area, calculating the square difference based on the vehicle data in the lanes at different time periods in the area, and using the square difference as a representative value of the vehicle data in the lanes at different time periods in each area; B103, sum up and average the representative values ​​of vehicle data in lanes at different time periods in each area, and use the average value as the representative value of traffic flow.

7. The integrated public transportation control system for smart cities according to claim 6 is characterized by: In B2, processing is performed based on the traffic flow representative value and the lane area in the monitoring area. The processing process is as follows: B201, obtaining the lane area in the monitoring area, dividing the monitoring area into several areas, summing the lane area in each area, and obtaining a representative value of the total area of ​​the lanes in the monitoring area; B202, the traffic flow representative value is ratioed with the total area representative value of the lanes in the monitoring area to obtain the representative value of the road congestion level in the monitoring area.

8. The integrated public transportation control system for smart cities according to claim 1 is characterized by: Based on the static characterization value and the dynamic characterization value of the bus driving data, the road traffic peak period determination value is obtained. The processing process is as follows: C1, taking the static characterization value of bus driving data as the independent variable and the dynamic characterization value of bus driving data as the dependent variable, a multinomial regression equation is obtained; Multinomial regression equation: Y = ax 2 +bx+c, where Y represents the dynamic representation value of bus driving data, X represents the static representation value of bus driving data, and a, b, and c represent constants respectively; C2, based on the multinomial regression equation, the derivative is obtained as the slope of the multinomial regression equation, and the slope is used as the judgment value of the road traffic peak period; C3, compares the real-time monitoring data with the road traffic peak period judgment value, and the comparison process is as follows: If the real-time monitored data is greater than or equal to the road traffic peak period determination value, it is determined that the road traffic is at peak congestion at this time; If the real-time monitoring data is less than the road traffic peak period judgment value, it is determined that the road traffic is not in peak congestion at this time.

9. The integrated public transportation control system for smart cities according to claim 1 is characterized by: When the road traffic is at peak hours, whether it is necessary to dynamically adjust the buses, the specific process is as follows: D1, obtain the representative value of the normal driving time of the bus and the representative value of the congestion duration, and get the bus delay time; D2, based on the comparison between the bus delay time and the preset bus delay time threshold, the comparison process is as follows: If the bus delay time is greater than or equal to the preset bus delay time threshold, it is determined that the bus needs to be dynamically adjusted; If the bus delay time is less than the preset bus delay threshold, it is determined that there is no need to dynamically adjust the bus.

10. The integrated public transportation control system for smart cities according to claim 9 is characterized in that: In D1, the congestion duration is obtained based on the total distance representative value of the congested road route; D101, dividing the total distance of the congested road route into a number of line segments, summing up the bus speeds in each line segment and taking the average value, taking the average value as the bus speed on the congested route, and dividing the total distance representative value of the congested road route by the bus speed on the congested route to obtain the congestion duration representative value; D102, summing up the normal running time of the bus each time and taking the average value, and taking the average value as the representative value of the normal running time; D103, bus delay time = congestion duration representative value - normal travel time representative value.

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